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                    [STAFF_TITLE] => Assistant Professor
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                    [RESEARCH_INTERESTS] => Field-scale evapotranspiration mapping using remotely sensed data with cloud computing
Multi-sensor data fusion for improved spatiotemporal sampling
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He has successfully defended and obtained PhD degree. 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=> Array ( ) [FNAME] => Forrest [MNAME] => S [LNAME] => Melton [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826925578 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2022 [PUB_START] => 2022-01-01 [PUB_END] => 2022-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [18] => Array ( [@attributes] => Array ( [id] => 245248829440 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Irrigation plays significantly different roles in influencing hydrological processes in two breadbasket regions [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248829441 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yiming [MNAME] => Array ( ) [LNAME] => Wang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248829442 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yuyu [MNAME] => Array ( ) [LNAME] => Zhou [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248829443 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kristie [MNAME] => J. [LNAME] => Franz [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248829444 ) [FACULTY_NAME] => Array ( ) [FNAME] => Xuesong [MNAME] => Array ( ) [LNAME] => Zhang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248829445 ) [FACULTY_NAME] => Array ( ) [FNAME] => Junyu [MNAME] => Array ( ) [LNAME] => Qi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248829446 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gensuo [MNAME] => Array ( ) [LNAME] => Jia [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248829447 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Science of the Total Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 844 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85134354558 [EDITORS] => Array ( ) [ISBNISSN] => 18791026 00489697 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2022 Elsevier B.V.Agriculture is a major water user, especially in dry and drought-prone areas that rely on irrigation to support agricultural production. In recent years, the over-extraction of groundwater, exacerbated by climate change, population growth, and intensive agricultural irrigation, has led to a drop in water levels and influenced the hydrological cycle. Understanding changes in hydrological processes is essential for pursuing water sustainability. This study aims to estimate the amount and impact of irrigation on hydrological processes in two breadbasket regions, Jing-Jin-Ji (JJJ), China, and northern Texas (NTX), US. We used the Soil and Water Assessment Tool (SWAT) to explore spatiotemporal variations of irrigation from 2008 to 2013 and compared changes in hydrological processes caused by irrigation. The results indicated that deficit irrigation is more common in JJJ than in NTX and can reduce approximately 50 % of irrigation water use in areas with intensively irrigated cropland. The applied irrigation varies less over time in NTX but fluctuates in JJJ. Compared with NTX, the higher irrigation intensity in JJJ results in a more significant change in downstream peak streamflow of around 6 m3/s. Moreover, the difference in crop growing seasons can lead to different impacts of irrigation on hydrological processes. For example, the percentage change of surface runoff under real-world relative to the no-irrigation scenario was the greatest, around 40 %, in JJJ and NTX. However, the peak change occurred at different times, with the nearing maturity of winter wheat in May in JJJ and corn in August in NTX. The great potential to reduce groundwater extraction by adopting water conservation irrigation techniques calls for policies and regulations to help farmers shift towards more sustainable water management practices. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => October (4th Quarter/Autumn) [DTD_PUB] => 20 [DTY_PUB] => 2022 [PUB_START] => 2022-10-20 [PUB_END] => 2022-10-20 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [19] => Array ( [@attributes] => Array ( [id] => 245248804864 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248804865 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yanghui [MNAME] => Array ( ) [LNAME] => Kang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248804866 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248804867 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248804868 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248804869 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hector [MNAME] => Array ( ) [LNAME] => Nieto [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248804870 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248804871 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248804872 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => White [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248804873 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => Array ( ) [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248804874 ) [FACULTY_NAME] => Array ( ) [FNAME] => Alfonso [MNAME] => Array ( ) [LNAME] => Torres-Rua [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248804875 ) [FACULTY_NAME] => Array ( ) [FNAME] => Maria [MNAME] => Mar [LNAME] => Alsina [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248804876 ) [FACULTY_NAME] => Array ( ) [FNAME] => Arnon [MNAME] => Array ( ) [LNAME] => Karnieli [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Irrigation Science [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 40 [ISSUE] => 4-5 [PAGENUM] => 531-551 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85125256970 [EDITORS] => Array ( ) [ISBNISSN] => 14321319 03427188 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2022, The Author(s).Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => September [DTD_PUB] => 1 [DTY_PUB] => 2022 [PUB_START] => 2022-09-01 [PUB_END] => 2022-09-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [20] => Array ( [@attributes] => Array ( [id] => 245248821248 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248821249 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jie [MNAME] => Array ( ) [LNAME] => Xue [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248821250 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248821251 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248821252 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248821253 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => R. [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248821254 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248821255 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248821256 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248821257 ) [FACULTY_NAME] => Array ( ) [FNAME] => Nicolas [MNAME] => Array ( ) [LNAME] => Bambach [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248821258 ) [FACULTY_NAME] => Array ( ) [FNAME] => Andrew [MNAME] => J. [LNAME] => McElrone [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248821259 ) [FACULTY_NAME] => Array ( ) [FNAME] => Sebastian [MNAME] => J. [LNAME] => Castro [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248821260 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G. [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248821261 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => H. [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248821262 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => G. [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [14] => Array ( [@attributes] => Array ( [id] => 245248821263 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lawrence [MNAME] => E. [LNAME] => Hipps [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [15] => Array ( [@attributes] => Array ( [id] => 245248821264 ) [FACULTY_NAME] => Array ( ) [FNAME] => María [MNAME] => Array ( ) [LNAME] => del Mar Alsina [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Irrigation Science [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 40 [ISSUE] => 4-5 [PAGENUM] => 609-634 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85130293153 [EDITORS] => Array ( ) [ISBNISSN] => 14321319 03427188 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2022, The Author(s).Robust information on consumptive water use (evapotranspiration, ET) derived from remote sensing can significantly benefit water decision-making in agriculture, informing irrigation schedules and water management plans over extended regions. To be of optimal utility for operational usage, these remote sensing ET data should be generated at the sub-field spatial resolution and daily-to-weekly timesteps commensurate with the scales of water management activities. However, current methods for field-scale ET retrieval based on thermal infrared (TIR) imaging, a valuable diagnostic of canopy stress and surface moisture status, are limited by the temporal revisit of available medium-resolution (100 m or finer) thermal satellite sensors. This study investigates the efficacy of a data fusion method for combining information from multiple medium-resolution sensors toward generating high spatiotemporal resolution ET products for water management. TIR data from Landsat and ECOSTRESS (both at ~ 100-m native resolution), and VIIRS (375-m native) are sharpened to a common 30-m grid using surface reflectance data from the Harmonized Landsat-Sentinel dataset. Periodic 30-m ET retrievals from these combined thermal data sources are fused with daily retrievals from unsharpened VIIRS to generate daily, 30-m ET image timeseries. The accuracy of this mapping method is tested over several irrigated cropping systems in the Central Valley of California in comparison with flux tower observations, including measurements over irrigated vineyards collected in the GRAPEX campaign. Results demonstrate the operational value added by the augmented TIR sensor suite compared to Landsat alone, in terms of capturing daily ET variability and reduced latency for real-time applications. The method also provides means for incorporating new sources of imaging from future planned thermal missions, further improving our ability to map rapid changes in crop water use at field scales. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => September [DTD_PUB] => 1 [DTY_PUB] => 2022 [PUB_START] => 2022-09-01 [PUB_END] => 2022-09-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [21] => Array ( [@attributes] => Array ( [id] => 245248792576 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248792577 ) [FACULTY_NAME] => Array ( ) [FNAME] => Sangchul [MNAME] => Array ( ) [LNAME] => Lee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248792578 ) [FACULTY_NAME] => Array ( ) [FNAME] => Junyu [MNAME] => Array ( ) [LNAME] => Qi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248792579 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gregory [MNAME] => W. [LNAME] => McCarty [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248792580 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248792581 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248792582 ) [FACULTY_NAME] => Array ( ) [FNAME] => Xuesong [MNAME] => Array ( ) [LNAME] => Zhang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248792583 ) [FACULTY_NAME] => Array ( ) [FNAME] => Glenn [MNAME] => E. [LNAME] => Moglen [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248792584 ) [FACULTY_NAME] => Array ( ) [FNAME] => Dooahn [MNAME] => Array ( ) [LNAME] => Kwak [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248792585 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hyunglok [MNAME] => Array ( ) [LNAME] => Kim [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248792586 ) [FACULTY_NAME] => Array ( ) [FNAME] => Venkataraman [MNAME] => Array ( ) [LNAME] => Lakshmi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248792587 ) [FACULTY_NAME] => Array ( ) [FNAME] => Seongyun [MNAME] => Array ( ) [LNAME] => Kim [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Agricultural Water Management [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 264 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85123321586 [EDITORS] => Array ( ) [ISBNISSN] => 18732283 03783774 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2022 Elsevier B.V.Water cycling within agricultural watersheds includes high uncertainty because of natural and anthropogenic factors (e.g., cultivation practices). Remotely sensed evapotranspiration products (RS-ET) have been adopted as an additional constraint on watershed modeling to enhance the accuracy of water cycling predictions while reducing uncertainty. However, plant parameters affecting evapotranspiration (ET) in watershed models are poorly calibrated without the use of appropriate constraints. The goal of this study is to assess the predictive uncertainty of the Soil and Water Assessment Tool (SWAT), depending on the inclusion or exclusion of annual crop yield as an additional constraint for an agricultural watershed. We analyzed the simulated results with acceptable performance measures depending on a varying degree of model constraints: one constraint (streamflow), two constraints (streamflow and RS-ET) and three constraints (streamflow, RS-ET, and crop yield). The three performance measures used were Nash-Sutcliffe Efficiency (NSE), Percent bias (P-bias), and Kling-Gupta Efficiency (KGE). As the number of model's constraints increased, the number of acceptable parameter sets were substantially reduced from 180 (acceptable for streamflow) to 116 (acceptable for streamflow and RS-ET) and 2 (acceptable for streamflow, RS-ET, and crop yield). In addition, overall model performance measures for ET were greatest in the simulation results with three constraints representing 0.02–0.2 and 0.04–0.05 greater NSE and KGE values than those of one constraint and two constraints, respectively. The parameter set with the best ET performance measures was also acceptable for predicting crop yield. Based on these results, we conclude that this crop yield data can be adopted as a model constraint for agricultural watersheds to reduce model uncertainty in ET simulations and to increase model prediction accuracy. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => April (2nd Quarter/Spring) [DTD_PUB] => 30 [DTY_PUB] => 2022 [PUB_START] => 2022-04-30 [PUB_END] => 2022-04-30 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [22] => Array ( [@attributes] => Array ( [id] => 245248819200 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248819201 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248819202 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248819203 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248819204 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jie [MNAME] => Array ( ) [LNAME] => Xue [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248819205 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248819206 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 14 [ISSUE] => 8 [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85128731552 [EDITORS] => Array ( ) [ISBNISSN] => 20724292 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area. 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water use monitoring in California vineyards [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826823169 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yanghui [MNAME] => Array ( ) [LNAME] => Kang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826823170 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826823171 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826823172 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826823173 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hector [MNAME] => Array ( ) [LNAME] => Nieto [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826823174 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826823175 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( 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[EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [30] => Array ( [@attributes] => Array ( [id] => 245248851968 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248851969 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248851970 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248851971 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248851972 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jeffrey [MNAME] => D. [LNAME] => Wood [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248851973 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lianhong [MNAME] => Array ( ) [LNAME] => Gu [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248851974 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 265 [ISSUE] => Array ( ) [PAGENUM] => 112640 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => http://dx.doi.org/10.1016/j.rse.2021.112640 [EDITORS] => Array ( ) [ISBNISSN] => 0034-4257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => November [DTD_PUB] => Array ( ) [DTY_PUB] => 2021 [PUB_START] => 2021-11-01 [PUB_END] => 2021-11-30 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [31] => Array ( [@attributes] => Array ( [id] => 245248833536 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Mapping daily evapotranspiration at field scale using the harmonized landsat and sentinel-2 dataset, with sharpened VIIRS as a sentinel-2 thermal proxy [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248833537 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jie [MNAME] => Array ( ) [LNAME] => Xue [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248833538 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248833539 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248833540 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248833541 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248833542 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => R. [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248833543 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248833544 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 13 [ISSUE] => 17 [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85112841295 [EDITORS] => Array ( ) [ISBNISSN] => 20724292 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflec-tance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Land-sats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak grow-ing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => September [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-09-01 [PUB_END] => 2021-09-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [32] => Array ( [@attributes] => Array ( [id] => 245248800768 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Effects of land-use change and drought on decadal evapotranspiration and water balance of natural and managed forested wetlands along the southeastern US lower coastal plain [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248800769 ) [FACULTY_NAME] => Array ( ) [FNAME] => Maricar [MNAME] => Array ( ) [LNAME] => Aguilos [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248800770 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ge [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248800771 ) [FACULTY_NAME] => Array ( ) [FNAME] => Asko [MNAME] => Array ( ) [LNAME] => Noormets [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248800772 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jean-Christophe [MNAME] => Array ( ) [LNAME] => Domec [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248800773 ) [FACULTY_NAME] => Array ( ) [FNAME] => Steve [MNAME] => Array ( ) [LNAME] => McNulty [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248800774 ) [FACULTY_NAME] => Array ( ) [FNAME] => Michael [MNAME] => Array ( ) [LNAME] => Gavazzi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248800775 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kevan [MNAME] => Array ( ) [LNAME] => Minick [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248800776 ) [FACULTY_NAME] => Array ( ) [FNAME] => Bhaskar [MNAME] => Array ( ) [LNAME] => Mitra [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248800777 ) [FACULTY_NAME] => Array ( ) [FNAME] => Prajaya [MNAME] => Array ( ) [LNAME] => Prajapati [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248800778 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248800779 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => Array ( ) [LNAME] => King [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Agricultural and Forest Meteorology [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 303 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85102030541 [EDITORS] => Array ( ) [ISBNISSN] => 01681923 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2021 Elsevier B.V.Forested wetlands are important in regulating regional hydrology and climate. However, long-term studies on the hydrologic impacts of converting natural forested wetlands to pine plantations are rare for the southern US. From 2005-2018, we quantified water cycling in two post-harvest and newly-planted loblolly pine (Pinus taeda) plantations (YP2–7, 2–7 yrs old; YP2–8, 2–8 yrs old), a rotation-age loblolly pine plantation (MP, 15–28 yrs old), and a natural bottomland hardwood forest (BHF, > 100 yrs old) along the lower coastal plain of North Carolina. We quantified the differences in inter-annual and seasonal water balance and trends of evapotranspiration (ET) using eddy covariance over 37 site-years and assessed key climatic and biological drivers of ET. We found that the rotation-age plantation (MP) had higher annual ET (933 ± 63 mm) than the younger plantations (776 ± 74 mm for YP2–7 and 638 ± 190 mm for YP2–8), and the BHF (743 ± 172 mm), owing to differences in stand age, canopy cover, and micrometeorology. Chronosequence analysis of the pine sites showed that ET increased with stand age up to 10 years, then gradually stabilized for the remainder of the rotation of 28 – 30 years. YP2–8 was sensitive to water availability, decreasing ET by 30 – 43 % during the extreme 2007 – 2008 drought, but reductions in ET at MP were only 8 – 11 %. Comparing to BHF, ditching with management enhanced drainage at YP2–7 and YP2–8, while drainage was lower at the mature pine site. This study provides insight into land use-hydrology-climate interactions that have important implications for forested wetland management in a time of rapidly changing environmental conditions of the LCP of the southern US. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => June [DTD_PUB] => 15 [DTY_PUB] => 2021 [PUB_START] => 2021-06-15 [PUB_END] => 2021-06-15 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [33] => Array ( [@attributes] => Array ( [id] => 245248790528 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => A data-driven approach to estimate leaf area index for Landsat images over the contiguous US [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248790529 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yanghui [MNAME] => Array ( ) [LNAME] => Kang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248790530 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mutlu [MNAME] => Array ( ) [LNAME] => Ozdogan [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248790531 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248790532 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248790533 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => A. [LNAME] => White [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248790534 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248790535 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248790536 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tyler [MNAME] => A. [LNAME] => Erickson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 258 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85102590019 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2021 Elsevier Inc.Leaf Area Index (LAI) is a fundamental vegetation biophysical variable serving as an essential input to many land surface and atmospheric models. Long-term LAI maps are typically generated with satellite images at moderate spatial resolution (0.25 to 1 km), such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS). While useful for regional-scale land surface modeling, these moderate resolution products often cannot resolve spatial heterogeneity important for many agricultural and hydrological applications. This paper proposes an approach to map LAI at 30-m resolution based on Landsat images for the Contiguous US (CONUS) consistent with the MODIS product, aimed at multi-scale modeling applications. The algorithm was driven by 1.6 million spatially homogeneous samples derived from MODIS LAI and Landsat surface reflectance products from 2006 to 2018. Based on these samples, we trained separate random forest models to estimate LAI from Landsat surface reflectance for eight biomes of the National Land Cover Database (NLCD). A balanced sample design regarding the saturation status of MODIS LAI and a machine-learning-based noise detection technique were introduced to mitigate the trade-off in estimation accuracy between medium LAI (e.g., 3 to 4, unsaturated) and high LAI (e.g., 4–6, saturated). This approach was evaluated using ground measurements from 19 National Ecological Observatory Network (NEON) sites and eight independent sites from other sources. These sites comprise a representative sample of forests, grasslands, shrublands, and croplands across the US. For NEON sites, the LAI estimates show an overall Root Mean Squared Error (RMSE) of 0.8 with r2 of 0.88. For the eight independent sites, the Landsat LAI algorithm achieves RMSE between 0.52 and 0.91. The uncertainty in Landsat estimated LAI varies across biomes and locations. The proposed algorithm was implemented on the Google Earth Engine platform, allowing for the rapid generation of long-term high-resolution LAI records from the 1980s using Landsat images (code is available at https://github.com/yanghuikang/Landsat-LAI). Our findings also highlight the importance of sample balance on regression-based modeling in remote sensing applications. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => June [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-06-01 [PUB_END] => 2021-06-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [34] => Array ( [@attributes] => Array ( [id] => 245248841728 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Phenological corrections to a field-scale, ET-based crop stress indicator: An application to yield forecasting across the U.S. Corn Belt [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248841729 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248841730 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248841731 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248841732 ) [FACULTY_NAME] => Array ( ) [FNAME] => David [MNAME] => M. [LNAME] => Johnson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248841733 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248841734 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248841735 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248841736 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => R. [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248841737 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jason [MNAME] => A. [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248841738 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => Array ( ) [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248841739 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tilden [MNAME] => P. [LNAME] => Meyers [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248841740 ) [FACULTY_NAME] => Array ( ) [FNAME] => Carl [MNAME] => J. [LNAME] => Bernacchi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248841741 ) [FACULTY_NAME] => Array ( ) [FNAME] => Caitlin [MNAME] => E. [LNAME] => Moore [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 257 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85101338376 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2021 The Author(s)Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt – Mead, NE, Ames, IA and Champaign, IL – to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010 to 2017, which included both drought and pluvial conditions as well as a broad range in yield values. Detrended yield anomalies for corn and soybean were correlated with ESI computed using annual ET curves temporally aligned based on (1) calendar date, (2) crop emergence date, and (3) a growing degree day (GDD) scaled time axis. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improve the correlation, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. Regression equations derived at the time of peak correlation were used to estimate yields at county scale using a leave-one-out cross-validation strategy. The ESI-based yield estimates agree well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.79 to 0.93 and percent root-mean-square errors of 5–8%. These results demonstrate that remotely sensed ET at high spatiotemporal resolution can convey valuable water stress information for forecasting crop yields across the Corn Belt if interannual phenological variability is considered. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => May [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-05-01 [PUB_END] => 2021-05-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [35] => Array ( [@attributes] => Array ( [id] => 245248843776 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248843777 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248843778 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248843779 ) [FACULTY_NAME] => Array ( ) [FNAME] => Donghui [MNAME] => Array ( ) [LNAME] => Xie [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248843780 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248843781 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ruiqing [MNAME] => Array ( ) [LNAME] => Chen [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248843782 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248843783 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248843784 ) [FACULTY_NAME] => Array ( ) [FNAME] => Zhongxin [MNAME] => Array ( ) [LNAME] => Chen [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 253 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85095570595 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2020 Elsevier Inc.Multi-sensor remote sensing data fusion technologies have been developed and widely applied in recent years, providing a feasible and economical solution to increase the availability of high spatial and temporal resolution data. These methods, however, have been challenging to apply in highly heterogeneous areas, especially in complex agricultural landscapes where there are rapid changes at small scales, while features at larger scales change more slowly. In this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL). This method utilizes a crop reference curve (CRC) approach, in which a set of NDVI time series are extracted from pure MODIS pixels (250 m resolution) identified using the CDL, and then used to fit Landsat-like observations (30 m). The CRC based method was applied over a complex agricultural landscape in the Choptank River watershed on the Eastern Shore of Maryland. Landsat data from 2013 and 2014 and Harmonized Landsat and Sentinel-2 (HLS) data from 2018 were used to reconstruct 30 m daily NDVI maps for major crop types. Results show that the relative error (RE) in reconstructed NDVI is around 6–8% during periods of rapid crop growth, and 3–5% during peak periods when growth is slow. The accuracy of the CRC method outperforms a standard image pair-based data fusion algorithm (Spatial and Temporal Adaptive Reflectance Fusion Model; STARFM), which yields RE of 4–9% in slow-growth periods and 10–16% in fast-growth periods when clear Landsat images are scarce. The CRC method was also compared with time-series data fusion methods, including a harmonic fitting model and the SaTellite dAta IntegRation (STAIR) model. The results show that CRC gives similar results when the Landsat-like image availability is high (around 27 images per year), but outperforms other methods when availability is limited (less than 15 images per year). The reconstructed NDVI time series for corn, soybean, winter wheat/soybean and forest at 30-m resolution show clear phenological patterns at the sub-field scale. The resulting 30-m NDVI timeseries data provide useful information for mapping crop phenology and monitoring crop condition in complex agricultural landscapes, especially for complex double-cropping areas. However, the input requirement of an accurate 30-m crop classification map constrains its application to areas and periods where classifications are available. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => February [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-02-01 [PUB_END] => 2021-02-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [36] => Array ( [@attributes] => Array ( [id] => 245248802816 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Evaluation of a CONUS-Wide ECOSTRESS DisALEXI Evapotranspiration Product [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248802817 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kerry [MNAME] => Array ( ) [LNAME] => Cawse-Nicholson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248802818 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248802819 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248802820 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248802821 ) [FACULTY_NAME] => Array ( ) [FNAME] => Simon [MNAME] => J. [LNAME] => Hook [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248802822 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joshua [MNAME] => B. [LNAME] => Fisher [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248802823 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gregory [MNAME] => Array ( ) [LNAME] => Halverson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248802824 ) [FACULTY_NAME] => Array ( ) [FNAME] => Glynn [MNAME] => C. [LNAME] => Hulley [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248802825 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248802826 ) [FACULTY_NAME] => Array ( ) [FNAME] => Dennis [MNAME] => D. [LNAME] => Baldocchi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248802827 ) [FACULTY_NAME] => Array ( ) [FNAME] => Nathaniel [MNAME] => A. [LNAME] => Brunsell [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248802828 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ankur [MNAME] => R. [LNAME] => Desai [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248802829 ) [FACULTY_NAME] => Array ( ) [FNAME] => Timothy [MNAME] => J. [LNAME] => Griffis [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248802830 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kimberly [MNAME] => A. [LNAME] => Novick [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 14 [ISSUE] => Array ( ) [PAGENUM] => 10117-10133 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85115185284 [EDITORS] => Array ( ) [ISBNISSN] => 21511535 19391404 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2008-2012 IEEE.The atmosphere-land exchange inverse disaggregation (DisALEXI) algorithm is a multi-scale energy balance model that estimates evapotranspiration (ET) using land-surface temperature (LST) as a driving remote sensing input. Using LST products from ECOSTRESS, a thermal radiometer mounted on the International Space Station, DisALEXI ET products have been produced over the contiguous United States (CONUS) at 70 m resolution. The goal of this study is to demonstrate the accuracy of the CONUS-wide ET produced by the Jet Propulsion Laboratory (JPL) and to compare the results with the original DisALEXI ET produced by researchers at the United States Department of Agriculture (USDA). DisALEXI-USDA has been produced ad-hoc using Landsat LST, and is routinely produced over six target sites using ECOSTRESS LST. DisALEXI-JPL was implemented in order to expand the spatial coverage. DisALEXI-JPL was evaluated at 26 CONUS eddy covariance sites, showing good correlation, with R2 = 0.80 and RMSE = 0.81 mm/day, which is comparable to previous DisALEXI validation studies (RMSE ∼1 mm/day). The two DisALEXI implementations compared well, with R2 = 0.92. This article evaluates DisALEXI-JPL and shows that the algorithm is valid over a larger segment of CONUS. We also show the impact of quality flags, as pixels with high view zenith angles or high aerosol optical depth showed greater deviation from field measurements. As a product demonstration, we show a regional map of fine-scale ET, where the fine-scale variation over wider areas can detect small areas of stress much sooner than products with coarse resolution representing average conditions. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => January (1st Quarter/Winter) [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-01-01 [PUB_END] => 2021-01-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [37] => Array ( [@attributes] => Array ( [id] => 245248823296 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248823297 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248823298 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248823299 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jie [MNAME] => Array ( ) [LNAME] => Xue [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248823300 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => R. [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248823301 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248823302 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248823303 ) [FACULTY_NAME] => Array ( ) [FNAME] => Chris [MNAME] => R. [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248823304 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248823305 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kerry [MNAME] => Array ( ) [LNAME] => Cawse-Nicholson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248823306 ) [FACULTY_NAME] => Array ( ) [FNAME] => Glynn [MNAME] => Array ( ) [LNAME] => Hulley [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248823307 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joshua [MNAME] => B. [LNAME] => Fisher [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248823308 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G. [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248823309 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tilden [MNAME] => P. [LNAME] => Meyers [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248823310 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => Array ( ) [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [14] => Array ( [@attributes] => Array ( [id] => 245248823311 ) [FACULTY_NAME] => Array ( ) [FNAME] => Dennis [MNAME] => D. [LNAME] => Baldocchi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [15] => Array ( [@attributes] => Array ( [id] => 245248823312 ) [FACULTY_NAME] => Array ( ) [FNAME] => Camilo [MNAME] => Array ( ) [LNAME] => Rey-Sanchez [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 252 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85090288735 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2020Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For optimal utility in agricultural water management applications, frequent thermal imaging (<4-day revisit) at sub-field (100 m or less) spatial resolution is desired. While, the current suite of Landsat satellites (7 and 8) provides the required spatial resolution, the 8-day combined revisit can be inadequate to capture rapid changes in surface moisture status or crop phenology, particularly in areas of persistent cloud cover. The new ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, with an average 4-day revisit interval and nominal 70-m resolution, provides a valuable research platform for augmenting Landsat TIR sampling and for investigating TIR-based ET mapping mission requirements more broadly. This study investigates the interoperability of Landsat and ECOSTRESS imaging for developing ET image timeseries with high spatial (30-m) and temporal (daily) resolution. A data fusion algorithm is used to fuse Landsat and ECOSTRESS ET retrievals at 30 m with daily 500-m retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) over target agricultural sites spanning the United States.The added value of the combined multi-source dataset is quantified in comparison with daily flux tower observations collected within these target domains. In addition, we investigate ET model performance as a function of ECOSTRESS view angle, overpass time, and time separation between TIR and Landsat visible to shortwave infrared (VSWIR) data acquisitions used to generate land-surface temperature, leaf area index, and albedo inputs to the surface energy balance model. The results demonstrate the value of the higher temporal sampling provided by ECOSTRESS, especially in areas that are frequently impacted by cloud cover. Limiting usage to ECOSTRESS scenes collected between 9:00 a.m. to 5:00 p.m. and nadir viewing angles <20° yielded daily (24-h) ET retrievals of comparable quality to the well-tested Landsat baseline. We also discuss challenges in using land-surface temperature from a thermal free-flyer system for ET retrieval, which may have ramifications for future TIR water-use mapping missions. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => January (1st Quarter/Winter) [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-01-01 [PUB_END] => 2021-01-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [38] => Array ( [@attributes] => Array ( [id] => 245248839680 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => OpenET: Filling a Critical Data Gap in Water Management for the Western United States [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248839681 ) [FACULTY_NAME] => Array ( ) [FNAME] => Forrest [MNAME] => S. [LNAME] => Melton [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248839682 ) [FACULTY_NAME] => Array ( ) [FNAME] => Justin [MNAME] => Array ( ) [LNAME] => Huntington [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248839683 ) [FACULTY_NAME] => Array ( ) [FNAME] => Robyn [MNAME] => Array ( ) [LNAME] => Grimm [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248839684 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jamie [MNAME] => Array ( ) [LNAME] => Herring [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248839685 ) [FACULTY_NAME] => Array ( ) [FNAME] => Maurice [MNAME] => Array ( ) [LNAME] => Hall [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248839686 ) [FACULTY_NAME] => Array ( ) [FNAME] => Dana [MNAME] => Array ( ) [LNAME] => Rollison [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248839687 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tyler [MNAME] => Array ( ) [LNAME] => Erickson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248839688 ) [FACULTY_NAME] => Array ( ) [FNAME] => Richard [MNAME] => Array ( ) [LNAME] => Allen [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248839689 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248839690 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joshua [MNAME] => B. [LNAME] => Fisher [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248839691 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ayse [MNAME] => Array ( ) [LNAME] => Kilic [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248839692 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gabriel [MNAME] => B. [LNAME] => Senay [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248839693 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => Array ( ) [LNAME] => Volk [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248839694 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [14] => Array ( [@attributes] => Array ( [id] => 245248839695 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lee [MNAME] => Array ( ) [LNAME] => Johnson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [15] => Array ( [@attributes] => Array ( [id] => 245248839696 ) [FACULTY_NAME] => Array ( ) [FNAME] => Anderson [MNAME] => Array ( ) [LNAME] => Ruhoff [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [16] => Array ( [@attributes] => Array ( [id] => 245248839697 ) [FACULTY_NAME] => Array ( ) [FNAME] => Philip [MNAME] => Array ( ) [LNAME] => Blankenau [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [17] => Array ( [@attributes] => Array ( [id] => 245248839698 ) [FACULTY_NAME] => Array ( ) [FNAME] => Matt [MNAME] => Array ( ) [LNAME] => Bromley [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [18] => Array ( [@attributes] => Array ( [id] => 245248839699 ) [FACULTY_NAME] => Array ( ) [FNAME] => Will [MNAME] => Array ( ) [LNAME] => Carrara [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [19] => Array ( [@attributes] => Array ( [id] => 245248839700 ) [FACULTY_NAME] => Array ( ) [FNAME] => Britta [MNAME] => Array ( ) [LNAME] => Daudert [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [20] => Array ( [@attributes] => Array ( [id] => 245248839701 ) [FACULTY_NAME] => Array ( ) [FNAME] => Conor [MNAME] => Array ( ) [LNAME] => Doherty [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [21] => Array ( [@attributes] => Array ( [id] => 245248839702 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christian [MNAME] => Array ( ) [LNAME] => Dunkerly [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [22] => Array ( [@attributes] => Array ( [id] => 245248839703 ) [FACULTY_NAME] => Array ( ) [FNAME] => MacKenzie [MNAME] => Array ( ) [LNAME] => Friedrichs [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [23] => Array ( [@attributes] => Array ( [id] => 245248839704 ) [FACULTY_NAME] => Array ( ) [FNAME] => Alberto [MNAME] => Array ( ) [LNAME] => Guzman [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [24] => Array ( [@attributes] => Array ( [id] => 245248839705 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gregory [MNAME] => Array ( ) [LNAME] => Halverson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [25] => Array ( [@attributes] => Array ( [id] => 245248839706 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jody [MNAME] => Array ( ) [LNAME] => Hansen [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [26] => Array ( [@attributes] => Array ( [id] => 245248839707 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jordan [MNAME] => Array ( ) [LNAME] => Harding [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [27] => Array ( [@attributes] => Array ( [id] => 245248839708 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yanghui [MNAME] => Array ( ) [LNAME] => Kang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [28] => Array ( [@attributes] => Array ( [id] => 245248839709 ) [FACULTY_NAME] => Array ( ) [FNAME] => David [MNAME] => Array ( ) [LNAME] => Ketchum [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [29] => Array ( [@attributes] => Array ( [id] => 245248839710 ) [FACULTY_NAME] => Array ( ) [FNAME] => Blake [MNAME] => Array ( ) [LNAME] => Minor [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [30] => Array ( [@attributes] => Array ( [id] => 245248839711 ) [FACULTY_NAME] => Array ( ) [FNAME] => Charles [MNAME] => Array ( ) [LNAME] => Morton [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [31] => Array ( [@attributes] => Array ( [id] => 245248839712 ) [FACULTY_NAME] => Array ( ) [FNAME] => Samuel [MNAME] => Array ( ) [LNAME] => Ortega-Salazar [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [32] => Array ( [@attributes] => Array ( [id] => 245248839713 ) [FACULTY_NAME] => Array ( ) [FNAME] => Thomas [MNAME] => Array ( ) [LNAME] => Ott [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [33] => Array ( [@attributes] => Array ( [id] => 245248839714 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mutlu [MNAME] => Array ( ) [LNAME] => Ozdogan [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [34] => Array ( [@attributes] => Array ( [id] => 245248839715 ) [FACULTY_NAME] => Array ( ) [FNAME] => Peter [MNAME] => M. [LNAME] => ReVelle [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [35] => Array ( [@attributes] => Array ( [id] => 245248839716 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mitch [MNAME] => Array ( ) [LNAME] => Schull [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [36] => Array ( [@attributes] => Array ( [id] => 245248839717 ) [FACULTY_NAME] => Array ( ) [FNAME] => Carlos [MNAME] => Array ( ) [LNAME] => Wang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [37] => Array ( [@attributes] => Array ( [id] => 245248839718 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [38] => Array ( [@attributes] => Array ( [id] => 245248839719 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ray [MNAME] => G. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Journal of the American Water Resources Association [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85112818158 [EDITORS] => Array ( ) [ISBNISSN] => 17521688 1093474X [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2021 The Authors. Journal of the American Water Resources Association published by Wiley Periodicals LLC on behalf of American Water Resources AssociationThe lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => January (1st Quarter/Winter) [DTD_PUB] => 1 [DTY_PUB] => 2021 [PUB_START] => 2021-01-01 [PUB_END] => 2021-01-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [39] => Array ( [@attributes] => Array ( [id] => 291826806784 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => A field-scale, ET-based crop stress indicator for yield estimates: an application across the Corn Belt, USA [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826806785 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826806786 ) [FACULTY_NAME] => Array ( ) [FNAME] => MB [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826806787 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826806788 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun 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[ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2020 [PUB_START] => 2020-01-01 [PUB_END] => 2020-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [45] => Array ( [@attributes] => Array ( [id] => 291826905088 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Relating Water Stress to Yield Estimates Using Thermal Remote Sensing: An Application across the US Corn Belt [TITLE_SECONDARY] => 100th American Meteorological Society Annual Meeting [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826905089 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) 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[MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2020 [PUB_START] => 2020-01-01 [PUB_END] => 2020-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [46] => Array ( [@attributes] => Array ( [id] => 245248847872 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248847873 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jie [MNAME] => Array ( ) [LNAME] => Xue [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248847874 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248847875 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248847876 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248847877 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248847878 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248847879 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => R. [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248847880 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248847881 ) [FACULTY_NAME] => Array ( ) [FNAME] => Alfonso [MNAME] => Array ( ) [LNAME] => Torres-Rua [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248847882 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mitch [MNAME] => Array ( ) [LNAME] => Schull [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 251 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85090289028 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2020 The AuthorsLand surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180–270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => December [DTD_PUB] => 15 [DTY_PUB] => 2020 [PUB_START] => 2020-12-15 [PUB_END] => 2020-12-15 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [47] => Array ( [@attributes] => Array ( [id] => 245248796672 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248796673 ) [FACULTY_NAME] => 2292554 [FNAME] => Fangni [MNAME] => Array ( ) [LNAME] => Lei [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248796674 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wade [MNAME] => T. [LNAME] => Crow [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248796675 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248796676 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jianzhi [MNAME] => Array ( ) [LNAME] => Dong [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248796677 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248796678 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => R. [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248796679 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248796680 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248796681 ) [FACULTY_NAME] => Array ( ) [FNAME] => Claudia [MNAME] => Array ( ) [LNAME] => Notarnicola [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248796682 ) [FACULTY_NAME] => Array ( ) [FNAME] => Felix [MNAME] => Array ( ) [LNAME] => Greifeneder [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248796683 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => M. [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248796684 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G. [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248796685 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248796686 ) [FACULTY_NAME] => Array ( ) [FNAME] => Nick [MNAME] => Array ( ) [LNAME] => Dokoozlian [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 239 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85077365657 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2020 Elsevier Inc.Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field-scale using a data assimilation strategy. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => March [DTD_PUB] => 15 [DTY_PUB] => 2020 [PUB_START] => 2020-03-15 [PUB_END] => 2020-03-15 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [48] => Array ( [@attributes] => Array ( [id] => 245248825344 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248825345 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248825346 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248825347 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248825348 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248825349 ) [FACULTY_NAME] => Array ( ) [FNAME] => Asko [MNAME] => Array ( ) [LNAME] => Noormets [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248825350 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ge [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248825351 ) [FACULTY_NAME] => Array ( ) [FNAME] => Randolph [MNAME] => Array ( ) [LNAME] => Wynne [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248825352 ) [FACULTY_NAME] => Array ( ) [FNAME] => Valerie [MNAME] => Array ( ) [LNAME] => Thomas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248825353 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 238 [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85058802923 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2018 The AuthorsForest ecosystem services such as clean water, wildlife habitat, and timber supplies are increasingly threatened by drought and disturbances (e.g., harvesting, fires and conversion to other uses), which can have great impacts on stand development and water balance. Improved understanding of the hydrologic response of forested systems to drought and disturbance at spatiotemporal resolutions commensurate with these impacts is important for effective forest management. Evapotranspiration (ET) is a key hydrologic variable in assessing forest functioning and health, but it remains a challenge to accurately quantify ET at landscape scales with the spatial and temporal detail required for effective decision-making. In this study, we apply a multi-sensor satellite data fusion approach to study the response of forest ET to drought and disturbance over a 7-year period. This approach combines Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) ET product time series retrieved using a surface energy balance model to generate a multi-year ET datacube at 30-m resolution and daily timesteps. The study area (~900 km2) contains natural and managed forest as well as croplands in the humid lower coastal plains in North Carolina, USA, and the simulation period from 2006 to 2012 includes both normal and severe drought conditions. The model results were evaluated at two AmeriFlux sites (US-NC2 and US-NC1) dominated by a mature and a recently clearcut pine plantation, respectively, and showed good agreement with observed fluxes, with 8–13% relative errors at monthly timesteps. Changes in water use patterns in response to drought and disturbance as well as forest stand aging were assessed using the remotely sensed time series describing total evapotranspiration, the transpiration (T) component of ET, and a moisture stress metric given by the actual-to-reference ET ratio (fRET). Analyses demonstrate differential response to drought by land cover type and stand age, with larger impacts on total ET observed in young pine stands than in mature stands which have substantially deeper rooting systems. Transpiration flux shows a clear ascending trend with the growth of young pine plantations, while stand thinning within the plantation leads to decreases in both remotely sensed leaf area index and T, as expected. Time series maps of fRET anomalies at 30-m resolution capture signals of drought, disturbance and the subsequent recovery after clearcut at the stand scale and may be an effective indicator for water use change detection and monitoring in forested landscapes. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => March [DTD_PUB] => 1 [DTY_PUB] => 2020 [PUB_START] => 2020-03-01 [PUB_END] => 2020-03-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [49] => Array ( [@attributes] => Array ( [id] => 291826827264 ) [CONTYPE] => Book [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Characterizing Field-scale Water Use, Phenology and Productivity in Agricultural Landscapes Using Multi-sensor Data Fusion: Annual Project Progress Report (80NSSC18K0483) [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826827265 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826827266 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826827267 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826827268 ) [FACULTY_NAME] => Array ( 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[INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Investigating drought-induced tree mortality using remotely sensed ET data in a temperate forest in the Central US [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826866177 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826866178 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826866179 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array 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=> Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2019 [PUB_START] => 2019-01-01 [PUB_END] => 2019-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [53] => Array ( [@attributes] => Array ( [id] => 245248856064 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Using Daily Stand-Scale Evapotranspiration (ET) Estimated from Remotely Sensed Data to Investigate Drought Impact on et in a Temporate Forest in the Central Us [TITLE_SECONDARY] => International Geoscience and Remote Sensing Symposium (IGARSS) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248856065 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248856066 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248856067 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248856068 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248856069 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jeffrey [MNAME] => Array ( ) [LNAME] => Wood [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248856070 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lianhong [MNAME] => Array ( ) [LNAME] => Gu [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => 6035-6038 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85077722935 [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2019 IEEE.Forests provide many important service functions, including habitat for wildlife, timber production and watershed water regulation. Short-term drought can make forests more susceptible to wildfire and insect attack, while long-term drought can directly increase forest mortality. Evapotranspiration (ET) is a key parameter that links the hydrological and ecological processes. Studying the impact of drought on ET, especially at stand-scale, can provide useful information for forest management. In this study, we applied a multi-scale data fusion ET modeling method using remotely sensed data to estimate daily 30 m ET over a natural forest in central US from 2010 to 2012, with 2012 as an extreme drought year. The estimated ET agrees well with the observed ET. Drought impact on ET is further analyzed and demonstrates the value of remotely sensed ET in studying drought impact on forest water use. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => July (3rd Quarter/Summer) [DTD_PUB] => 1 [DTY_PUB] => 2019 [PUB_START] => 2019-07-01 [PUB_END] => 2019-07-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [54] => Array ( [@attributes] => Array ( [id] => 245248806912 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248806913 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => R. 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[LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248806917 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => H. [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248806918 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => R. [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248806919 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248806920 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248806921 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => G. [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248806922 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hector [MNAME] => Array ( ) [LNAME] => Nieto [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248806923 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lawrence [MNAME] => E. [LNAME] => Hipps [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248806924 ) [FACULTY_NAME] => Array ( ) [FNAME] => Maria [MNAME] => Mar [LNAME] => Alsina [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248806925 ) [FACULTY_NAME] => Array ( ) [FNAME] => Luis [MNAME] => Array ( ) [LNAME] => Sanchez [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Irrigation Science [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 37 [ISSUE] => 3 [PAGENUM] => 431-449 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85053628260 [EDITORS] => Array ( ) [ISBNISSN] => 14321319 03427188 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2018, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water-use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual-to-reference ET (fRET) based on remotely sensed land-surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface-energy balance model, a multi-scale ET remote-sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote-Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET time-series retrievals from multiple satellite platforms to generate estimates at both the high spatial (30 m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.6 mm day−1 in ET at the daily time step and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multi-annual time scales. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => May [DTD_PUB] => 1 [DTY_PUB] => 2019 [PUB_START] => 2019-05-01 [PUB_END] => 2019-05-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [55] => Array ( [@attributes] => Array ( [id] => 245248815104 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Impact of insolation data source on remote sensing retrievals of evapotranspiration over the California delta [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248815105 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248815106 ) [FACULTY_NAME] => Array ( ) [FNAME] => George [MNAME] => Array ( ) [LNAME] => Diak [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248815107 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248815108 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248815109 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248815110 ) [FACULTY_NAME] => Array ( ) [FNAME] => Elke [MNAME] => Array ( ) [LNAME] => Eichelmann [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248815111 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => S. [LNAME] => Hemes [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248815112 ) [FACULTY_NAME] => Array ( ) [FNAME] => Dennis [MNAME] => Array ( ) [LNAME] => Baldocchi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248815113 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248815114 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 11 [ISSUE] => 3 [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85061375577 [EDITORS] => Array ( ) [ISBNISSN] => 20724292 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2019 by the authors. The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)-the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ETmodels. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento-San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University ofWisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth's Radiant Energy System(CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m -2 ·d -1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d -1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => February [DTD_PUB] => 1 [DTY_PUB] => 2019 [PUB_START] => 2019-02-01 [PUB_END] => 2019-02-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [56] => Array ( [@attributes] => Array ( [id] => 291826817024 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Advances in the Use of Remote Sensing for Multi-Scale Crop ET Estimation and Yield Assessment. [TITLE_SECONDARY] => ASA, CSSA, and CSA International Annual Meeting (2018) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826817025 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826817026 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826817027 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826817028 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826817029 ) [FACULTY_NAME] => Array ( ) [FNAME] => Chris [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826817030 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jason [MNAME] => Array ( ) [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2018 [PUB_START] => 2018-01-01 [PUB_END] => 2018-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [57] => Array ( [@attributes] => Array ( [id] => 291826853888 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => GRAPEX: A Project Integrating Ground, Aerial and Satellite Observations for Improved Water Management of Vineyards [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826853889 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826853890 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826853891 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826853892 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hector [MNAME] => Array ( ) [LNAME] => Nieto [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826853893 ) [FACULTY_NAME] => Array ( ) [FNAME] => Alfonso [MNAME] => F [LNAME] => Torres-Rua [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826853894 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mac [MNAME] => Array ( ) [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826853895 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => H [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826853896 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 291826853897 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826853898 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => Array ( ) [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 291826853899 ) [FACULTY_NAME] => Array ( ) [FNAME] => Array ( ) [MNAME] => Array ( ) [LNAME] => others [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2018 [ISSUE] => Array ( ) [PAGENUM] => B23D--01 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2018 [PUB_START] => 2018-01-01 [PUB_END] => 2018-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [58] => Array ( [@attributes] => Array ( [id] => 291826855936 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Impacts of Pixel Scale and Phenology on the Satellite-Based Evaporative Stress Index (Invited Presentation) [TITLE_SECONDARY] => 98th American Meteorological Society Annual Meeting [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826855937 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826855938 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826855939 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826855940 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826855941 ) [FACULTY_NAME] => Array ( ) [FNAME] => JA [MNAME] => Array ( ) [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] 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[8] => Array ( [@attributes] => Array ( [id] => 291826923529 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jason [MNAME] => Array ( ) [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826923530 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 291826923531 ) [FACULTY_NAME] => Array ( ) [FNAME] => Array ( ) [MNAME] => Array ( ) [LNAME] => others [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2018 [ISSUE] => Array ( ) [PAGENUM] => H54E--05 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2018 [PUB_START] => 2018-01-01 [PUB_END] => 2018-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [64] => Array ( [@attributes] => Array ( [id] => 245248854016 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => The grape remote sensing atmospheric profile and evapotranspiration experiment [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248854017 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248854018 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248854019 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G. [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248854020 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipppper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248854021 ) [FACULTY_NAME] => Array ( ) [FNAME] => Alfonso [MNAME] => Array ( ) [LNAME] => Torres-Rua [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248854022 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => K. [LNAME] => Parry [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248854023 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hector [MNAME] => Array ( ) [LNAME] => Nieto [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248854024 ) [FACULTY_NAME] => Array ( ) [FNAME] => Nurit [MNAME] => Array ( ) [LNAME] => Agam [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248854025 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => A. [LNAME] => White [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248854026 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248854027 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => Array ( ) [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248854028 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => H. [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248854029 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lawrence [MNAME] => E. [LNAME] => Hipppps [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248854030 ) [FACULTY_NAME] => Array ( ) [FNAME] => Sebastian [MNAME] => Array ( ) [LNAME] => Los [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [14] => Array ( [@attributes] => Array ( [id] => 245248854031 ) [FACULTY_NAME] => Array ( ) [FNAME] => Maria [MNAME] => Mar [LNAME] => Alsina [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [15] => Array ( [@attributes] => Array ( [id] => 245248854032 ) [FACULTY_NAME] => Array ( ) [FNAME] => Luis [MNAME] => Array ( ) [LNAME] => Sanchez [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [16] => Array ( [@attributes] => Array ( [id] => 245248854033 ) [FACULTY_NAME] => Array ( ) [FNAME] => Brent [MNAME] => Array ( ) [LNAME] => Sams [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [17] => Array ( [@attributes] => Array ( [id] => 245248854034 ) [FACULTY_NAME] => Array ( ) [FNAME] => Nickck [MNAME] => Array ( ) [LNAME] => Dokoozlian [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [18] => Array ( [@attributes] => Array ( [id] => 245248854035 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mac [MNAME] => Array ( ) [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [19] => Array ( [@attributes] => Array ( [id] => 245248854036 ) [FACULTY_NAME] => Array ( ) [FNAME] => Scott [MNAME] => Array ( ) [LNAME] => Jones [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [20] => Array ( [@attributes] => Array ( [id] => 245248854037 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [21] => Array ( [@attributes] => Array ( [id] => 245248854038 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tiffany [MNAME] => G. [LNAME] => Wilson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [22] => Array ( [@attributes] => Array ( [id] => 245248854039 ) [FACULTY_NAME] => 2292554 [FNAME] => Fangni [MNAME] => Array ( ) [LNAME] => Lei [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [23] => Array ( [@attributes] => Array ( [id] => 245248854040 ) [FACULTY_NAME] => Array ( ) [FNAME] => Andrew [MNAME] => Array ( ) [LNAME] => McElrone [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [24] => Array ( [@attributes] => Array ( [id] => 245248854041 ) [FACULTY_NAME] => Array ( ) [FNAME] => Josh [MNAME] => L. [LNAME] => Heitman [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [25] => Array ( [@attributes] => Array ( [id] => 245248854042 ) [FACULTY_NAME] => Array ( ) [FNAME] => Adam [MNAME] => M. [LNAME] => Howard [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [26] => Array ( [@attributes] => Array ( [id] => 245248854043 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kirk [MNAME] => Array ( ) [LNAME] => Post [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [27] => Array ( [@attributes] => Array ( [id] => 245248854044 ) [FACULTY_NAME] => Array ( ) [FNAME] => Forrest [MNAME] => Array ( ) [LNAME] => Melton [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [28] => Array ( [@attributes] => Array ( [id] => 245248854045 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Bulletin of the American Meteorological Society [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 99 [ISSUE] => 9 [PAGENUM] => 1791-1812 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85053618385 [EDITORS] => Array ( ) [ISBNISSN] => 00030007 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2018 American Meteorological Society.The GRAPEX project focuses on the development of improved water management tools for vineyards. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => September [DTD_PUB] => 1 [DTY_PUB] => 2018 [PUB_START] => 2018-09-01 [PUB_END] => 2018-09-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [65] => Array ( [@attributes] => Array ( [id] => 245248808960 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Field-scale assessment of land and water use change over the California delta using remote sensing [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248808961 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248808962 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248808963 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Knipper [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248808964 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248808965 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248808966 ) [FACULTY_NAME] => Array ( ) [FNAME] => Dennis [MNAME] => Array ( ) [LNAME] => Baldocchi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248808967 ) [FACULTY_NAME] => Array ( ) [FNAME] => Elke [MNAME] => Array ( ) [LNAME] => Eichelmann [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248808968 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kyle [MNAME] => Array ( ) [LNAME] => Hemes [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248808969 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248808970 ) [FACULTY_NAME] => Array ( ) [FNAME] => Josue [MNAME] => Array ( ) [LNAME] => Medellin-Azuara [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248808971 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 10 [ISSUE] => 6 [PAGENUM] => Array ( ) [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85048942485 [EDITORS] => Array ( ) [ISBNISSN] => 20724292 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2018 by the authors.The ability to accurately monitor and anticipate changes in consumptive water use associated with changing land use and land management is critical to developing sustainable water management strategies in water-limited climatic regions. In this paper, we present an application of a remote sensing data fusion technique for developing high spatiotemporal resolution maps of evapotranspiration (ET) at scales that can be associated with changes in land use. The fusion approach combines ET map timeseries developed using an multi-scale energy balance algorithm applied to thermal data from Earth observation platforms with high spatial but low temporal resolution (e.g., Landsat) and with moderate resolution but frequent temporal coverage (e.g., MODIS (Moderate Resolution Imaging Spectroradiometer)). The approach is applied over the Sacramento-San Joaquin Delta region in California-an area critical to both agricultural production and drinking water supply within the state that has recently experienced stresses on water resources due to a multi-year (2012-2017) extreme drought. ET "datacubes" with 30-m resolution and daily timesteps were constructed for the 2015-2016 water years and related to detailed maps of land use developed at the same spatial scale. The ET retrievals are evaluated at flux sites over multiple land covers to establish a metric of accuracy in the annual water use estimates, yielding root-mean-square errors of 1.0, 0.8, and 0.3 mm day-1 at daily, monthly, and yearly timesteps, respectively, for all sites combined. Annual ET averaged over the Delta changed only 3 mm year-1 between water years, from 822 to 819 mm year-1, translating to an area-integrated total change in consumptive water use of seven thousand acre-feet (TAF). Changes were largest in areas with recorded land-use change between water years-most significantly, fallowing of crop land presumably in response to reductions in water availability and allocations due to the drought. Moreover, the time evolution in water use associated with wetland restoration-an effort aimed at reducing subsidence and carbon emissions within the inner Delta-is assessed using a sample wetland chronosequence. Region-specific matrices of consumptive water use associated with land use changes may be an effective tool for policymakers and farmers to understand how land use conversion could impact consumptive use and demand. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => June [DTD_PUB] => 1 [DTY_PUB] => 2018 [PUB_START] => 2018-06-01 [PUB_END] => 2018-06-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [66] => Array ( [@attributes] => Array ( [id] => 245248811008 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248811009 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248811010 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248811011 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248811012 ) [FACULTY_NAME] => Array ( ) [FNAME] => Brian [MNAME] => Array ( ) [LNAME] => Wardlow [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248811013 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => R. [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248811014 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jason [MNAME] => A. [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248811015 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => Array ( ) [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248811016 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248811017 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248811018 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 210 [ISSUE] => Array ( ) [PAGENUM] => 387-402 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85044682057 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2018The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => June [DTD_PUB] => 1 [DTY_PUB] => 2018 [PUB_START] => 2018-06-01 [PUB_END] => 2018-06-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [67] => Array ( [@attributes] => Array ( [id] => 245248858112 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248858113 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ji [MNAME] => Hyun [LNAME] => Kim [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248858114 ) [FACULTY_NAME] => Array ( ) [FNAME] => Taehee [MNAME] => Array ( ) [LNAME] => Hwang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248858115 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248858116 ) [FACULTY_NAME] => Array ( ) [FNAME] => Crystal [MNAME] => L. [LNAME] => Schaaf [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248858117 ) [FACULTY_NAME] => Array ( ) [FNAME] => Emery [MNAME] => Array ( ) [LNAME] => Boose [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248858118 ) [FACULTY_NAME] => Array ( ) [FNAME] => J. [MNAME] => William [LNAME] => Munger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Journal of Geophysical Research: Biogeosciences [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 123 [ISSUE] => 6 [PAGENUM] => 1960-1975 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85049846561 [EDITORS] => Array ( ) [ISBNISSN] => 21698961 21698953 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => ©2018. The Authors.The phenological response of vegetation to ongoing climate change may have great implications for hydrological regimes in the eastern United States. However, there have been few studies that analyze its resultant effect on catchment discharge dynamics, separating from dominant climatic controls. In this study, we examined the net effect of phenological variations on the long-term and interannual gross primary production (GPP) and evapotranspiration (ET) fluxes in a temperate deciduous forest, as well as on the catchment discharge behavior in a mixed deciduous-conifer forest catchment. First, we calibrated the spring and autumn leaf phenology models for the Harvard Forest in the northeastern United States, where the onsets of greenup and senescence have been significantly advanced and delayed, 10.3 and 6.0 days respectively, over the past two decades (1992–2011). We then integrated the phenology models into a mechanistic watershed ecohydrological model (RHESSys), which improved the interannual and long-term simulations of both the plot-scale daily GPP and ET fluxes and the catchment discharge dynamics. We found that the phenological changes amplified the long-term increases in GPP and ET driven by the climatic controls. In particular, the earlier greenup onsets resulted in increases in annual ET significantly, while the delayed senescence onsets had less influence. Consequently, the earlier greenup onsets reduced stream discharge not only during the growing season but also during the following dormant season due to soil water depletion. This study highlights the importance of understanding vegetation response to ongoing climate change in order to predict the future hydrological nonstationarity in this region. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => June [DTD_PUB] => 1 [DTY_PUB] => 2018 [PUB_START] => 2018-06-01 [PUB_END] => 2018-06-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [68] => Array ( [@attributes] => Array ( [id] => 291826808832 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => A Thermal-based Two-Source Energy Balance Model for Estimating Evapotranspiration over Complex Canopies [TITLE_SECONDARY] => EGU General Assembly Conference Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826808833 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826808834 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826808835 ) [FACULTY_NAME] => Array ( ) [FNAME] => Hector [MNAME] => Array ( ) [LNAME] => Nieto [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826808836 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ana [MNAME] => Array ( ) [LNAME] => Andreu [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826808837 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826808838 ) [FACULTY_NAME] => Array ( ) [FNAME] => Carmelo [MNAME] => Array ( ) [LNAME] => Cammalleri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826808839 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => Array ( ) [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826808840 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 291826808841 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826808842 ) [FACULTY_NAME] => Array ( ) [FNAME] => Alfonso [MNAME] => Array ( ) [LNAME] => Torres-Rua [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => 2973 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [69] => Array ( [@attributes] => Array ( [id] => 291826821120 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Applications for remotely sensed evapotranspiration data in monitoring water quality, water use, and water security [TITLE_SECONDARY] => EGU General Assembly Conference Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826821121 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826821122 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826821123 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gao [MNAME] => Array ( ) [LNAME] => Feng [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826821124 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826821125 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826821126 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826821127 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826821128 ) [FACULTY_NAME] => Array ( ) [FNAME] => Amir [MNAME] => Array ( ) [LNAME] => Sharifi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 291826821129 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826821130 ) [FACULTY_NAME] => Array ( ) [FNAME] => Thomas [MNAME] => Array ( ) [LNAME] => Holmes [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => 3617 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [70] => Array ( [@attributes] => Array ( [id] => 291826825216 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Assimilation of Remotely Sensed Evaporative Fraction for Improved Agricultural Irrigation Water Management [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826825217 ) [FACULTY_NAME] => 2292554 [FNAME] => Fangni [MNAME] => Array ( ) [LNAME] => Lei [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826825218 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wade [MNAME] => T [LNAME] => Crow [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826825219 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826825220 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826825221 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2017 [ISSUE] => Array ( ) [PAGENUM] => H53N--01 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [71] => Array ( [@attributes] => Array ( [id] => 291826831360 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Daily monitoring of 30 m crop condition over complex agricultural landscapes [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826831361 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826831362 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826831363 ) [FACULTY_NAME] => Array ( ) [FNAME] => Donghui [MNAME] => Array ( ) [LNAME] => Xie [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826831364 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826831365 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2017 [ISSUE] => Array ( ) [PAGENUM] => GC33D--1111 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [72] => Array ( [@attributes] => Array ( [id] => 291826845696 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Evaluation of Drought Impact on Evapotranspiration (ET) over a Forested Landscape in North Carolina, USA using daily Landsat-scale ET [TITLE_SECONDARY] => EGU General Assembly Conference Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826845697 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826845698 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826845699 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826845700 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826845701 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826845702 ) [FACULTY_NAME] => Array ( ) [FNAME] => Asko [MNAME] => Array ( ) [LNAME] => Noormets [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826845703 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ge [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array 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=> Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [73] => Array ( [@attributes] => Array ( [id] => 291826868224 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Investigating water use over the C hoptank R iver W atershed using a multisatellite data fusion approach [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826868225 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826868226 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826868227 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826868228 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826868229 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826868230 ) [FACULTY_NAME] => Array ( ) [FNAME] => Amirreza [MNAME] => Array ( ) [LNAME] => Sharifi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826868231 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gregory [MNAME] => W [LNAME] => McCarty [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826868232 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 291826868233 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826868234 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 291826868235 ) [FACULTY_NAME] => Array ( ) [FNAME] => Array ( ) [MNAME] => Array ( ) [LNAME] => others [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Water Resources Research [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 53 [ISSUE] => 7 [PAGENUM] => 5298--5319 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [74] => Array ( [@attributes] => Array ( [id] => 291826909184 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Satellite-based mapping of field-scale stress indicators for crop yield forecasting: an application over Mead, NE [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826909185 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826909186 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826909187 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826909188 ) [FACULTY_NAME] => Array ( ) [FNAME] => Brian [MNAME] => Array ( ) [LNAME] => Wardlow [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826909189 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826909190 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jason [MNAME] => Array ( ) [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826909191 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826909192 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2017 [ISSUE] => Array ( ) [PAGENUM] => GC33D--1100 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2017 [PUB_START] => 2017-01-01 [PUB_END] => 2017-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [75] => Array ( [@attributes] => Array ( [id] => 245248845824 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248845825 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248845826 ) [FACULTY_NAME] => Array ( ) [FNAME] => Zhongxin [MNAME] => Array ( ) [LNAME] => Chen [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248845827 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248845828 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248845829 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lisheng [MNAME] => Array ( ) [LNAME] => Song [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248845830 ) [FACULTY_NAME] => Array ( ) [FNAME] => Limin [MNAME] => Array ( ) [LNAME] => Wang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248845831 ) [FACULTY_NAME] => Array ( ) [FNAME] => Bo [MNAME] => Array ( ) [LNAME] => Hu [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248845832 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Computers and Geosciences [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 105 [ISSUE] => Array ( ) [PAGENUM] => 10-20 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85018967882 [EDITORS] => Array ( ) [ISBNISSN] => 00983004 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2017 Elsevier LtdLand surface temperature (LST) is a critical parameter in environmental studies and resource management. The MODIS LST data product has been widely used in various studies, such as drought monitoring, evapotranspiration mapping, soil moisture estimation and forest fire detection. However, cloud contamination affects thermal band observations and will lead to inconsistent LST results. In this study, we present a new Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) model that recovers clear sky LST for pixels covered by cloud using only clear-sky neighboring pixels from nearby dates. The reconstructed LST was validated using the original LST pixels. Model shows high accuracy for reconstructing one masked pixel with R2 of 0.995, bias of −0.02 K and RMSE of 0.51 K. Extended spatial reconstruction results show a better accuracy for flat areas with R2 of 0.72‒0.89, bias of −0.02–0.21 K, and RMSE of 0.92–1.16 K, and for mountain areas with R2 of 0.81–0.89, bias of −0.35–−1.52 K, and RMSE of 1.42‒2.24 K. The reconstructed areas show spatial and temporal patterns that are consistent with the clear neighbor areas. In the reconstructed LST and NDVI triangle feature space which is controlled by soil moisture, LST values distributed reasonably and correspond well to the real soil moisture conditions. Our approach shows great potential for reconstructing clear sky LST under cloudy conditions and provides consistent daily LST which are critical for daily drought monitoring. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => August [DTD_PUB] => 1 [DTY_PUB] => 2017 [PUB_START] => 2017-08-01 [PUB_END] => 2017-08-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [76] => Array ( [@attributes] => Array ( [id] => 245248827392 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248827393 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248827394 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248827395 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248827396 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248827397 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G. [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248827398 ) [FACULTY_NAME] => Array ( ) [FNAME] => Amirreza [MNAME] => Array ( ) [LNAME] => Sharifi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248827399 ) [FACULTY_NAME] => Array ( ) [FNAME] => Gregory [MNAME] => W. [LNAME] => McCarty [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248827400 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248827401 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248827402 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248827403 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => Array ( ) [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Water Resources Research [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 53 [ISSUE] => 7 [PAGENUM] => 5298-5319 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85021698920 [EDITORS] => Array ( ) [ISBNISSN] => 19447973 00431397 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2017. The Authors.The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step - an improvement from 27% errors using standard Landsat-only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed-scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => July (3rd Quarter/Summer) [DTD_PUB] => 1 [DTY_PUB] => 2017 [PUB_START] => 2017-07-01 [PUB_END] => 2017-07-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [77] => Array ( [@attributes] => Array ( [id] => 245248837632 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248837633 ) [FACULTY_NAME] => Array ( ) [FNAME] => Zhuosen [MNAME] => Array ( ) [LNAME] => Wang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248837634 ) [FACULTY_NAME] => Array ( ) [FNAME] => Crystal [MNAME] => B. [LNAME] => Schaaf [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248837635 ) [FACULTY_NAME] => Array ( ) [FNAME] => Qingsong [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248837636 ) [FACULTY_NAME] => Array ( ) [FNAME] => JiHyun [MNAME] => Array ( ) [LNAME] => Kim [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248837637 ) [FACULTY_NAME] => Array ( ) [FNAME] => Angela [MNAME] => M. [LNAME] => Erb [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248837638 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248837639 ) [FACULTY_NAME] => Array ( ) [FNAME] => Miguel [MNAME] => O. [LNAME] => Román [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248837640 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248837641 ) [FACULTY_NAME] => Array ( ) [FNAME] => Shelley [MNAME] => Array ( ) [LNAME] => Petroy [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248837642 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jeffrey [MNAME] => R. [LNAME] => Taylor [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248837643 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jeffrey [MNAME] => G. [LNAME] => Masek [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [11] => Array ( [@attributes] => Array ( [id] => 245248837644 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jeffrey [MNAME] => T. [LNAME] => Morisette [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [12] => Array ( [@attributes] => Array ( [id] => 245248837645 ) [FACULTY_NAME] => Array ( ) [FNAME] => Xiaoyang [MNAME] => Array ( ) [LNAME] => Zhang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [13] => Array ( [@attributes] => Array ( [id] => 245248837646 ) [FACULTY_NAME] => Array ( ) [FNAME] => Shirley [MNAME] => A. [LNAME] => Papuga [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => International Journal of Applied Earth Observation and Geoinformation [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 59 [ISSUE] => Array ( ) [PAGENUM] => 104-117 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85028694954 [EDITORS] => Array ( ) [ISBNISSN] => 1872826X 15698432 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2017 The AuthorsSeasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => July (3rd Quarter/Summer) [DTD_PUB] => 1 [DTY_PUB] => 2017 [PUB_START] => 2017-07-01 [PUB_END] => 2017-07-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [78] => Array ( [@attributes] => Array ( [id] => 245248817152 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Impact of tile drainage on evapotranspiration in South Dakota, USA, based on high spatiotemporal resolution evapotranspiration time series from a multisatellite data fusion system [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248817153 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248817154 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248817155 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248817156 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248817157 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248817158 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tilden [MNAME] => Array ( ) [LNAME] => Meyers [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248817159 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wade [MNAME] => Array ( ) [LNAME] => Crow [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248817160 ) [FACULTY_NAME] => Array ( ) [FNAME] => Raymond [MNAME] => Array ( ) [LNAME] => Finocchiaro [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248817161 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jason [MNAME] => Array ( ) [LNAME] => Otkin [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248817162 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 245248817163 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 10 [ISSUE] => 6 [PAGENUM] => 2550-2564 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85017191689 [EDITORS] => Array ( ) [ISBNISSN] => 21511535 19391404 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2016 IEEE.Soil drainage is a widely used agricultural practice in themidwestUSA to remove excess soilwater to potentially improve the crop yield. Research shows an increasing trend in baseflow and streamflow in the midwest over the last 60 years, which may be related to artificial drainage. Subsurface drainage (i.e., tile) in particular may have strongly contributed to the increase in these flows, because of its extensive use and recent gain in the popularity as a yield-enhancement practice. However, howevapotranspiration (ET) is impacted by tile drainage on a regional level is not welldocumented. To explore spatial and temporal ET patterns and their relationship to tile drainage, we applied an energy balancebased multisensor data fusion method to estimate daily 30-m ET over an intensively tile-drained area in South Dakota, USA, from 2005 to 2013. Results suggest that tile drainage slightly decreases the annual cumulative ET, particularly during the early growing season. However, higher mid-season crop water use suppresses the extent of the decrease of the annual cumulative ET that might be anticipated fromwidespread drainage. The regional water balance analysis during the growing season demonstrates good closure, with the average residual from 2005 to 2012 as low as -3 mm. As an independent check of the simulated ET at the regional scale, the water balance analysis lends additional confidence to the study. The results of this study improve our understanding of the influence of agricultural drainage practices on regional ET, and can affect future decision making regarding tile drainage systems. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => June [DTD_PUB] => 1 [DTY_PUB] => 2017 [PUB_START] => 2017-06-01 [PUB_END] => 2017-06-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [79] => Array ( [@attributes] => Array ( [id] => 245248794624 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248794625 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248794626 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248794627 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248794628 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => R. [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248794629 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kathryn [MNAME] => A. [LNAME] => Semmens [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248794630 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248794631 ) [FACULTY_NAME] => Array ( ) [FNAME] => Asko [MNAME] => Array ( ) [LNAME] => Noormets [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248794632 ) [FACULTY_NAME] => Array ( ) [FNAME] => Randolph [MNAME] => H. [LNAME] => Wynne [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248794633 ) [FACULTY_NAME] => Array ( ) [FNAME] => Valerie [MNAME] => A. [LNAME] => Thomas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 245248794634 ) [FACULTY_NAME] => Array ( ) [FNAME] => Ge [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Hydrology and Earth System Sciences [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 21 [ISSUE] => 2 [PAGENUM] => 1017-1037 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85013082960 [EDITORS] => Array ( ) [ISBNISSN] => 16077938 10275606 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © Author(s) 2017. CC Attribution 3.0 License.As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and biological processes and is directly affected by water and land management, land use change and climate variability. Satellite remote sensing provides an effective means for diagnosing ET patterns over heterogeneous landscapes; however, limitations on the spatial and temporal resolution of satellite data, combined with the effects of cloud contamination, constrain the amount of detail that a single satellite can provide. In this study, we describe an application of a multi-sensor ET data fusion system over a mixed forested/agricultural landscape in North Carolina, USA, during the growing season of 2013. The fusion system ingests ET estimates from the Two-Source Energy Balance Model (TSEB) applied to thermal infrared remote sensing retrievals of land surface temperature from multiple satellite platforms: hourly geostationary satellite data at 4 km resolution, daily 1 km imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) and biweekly Landsat thermal data sharpened to 30 m. These multiple ET data streams are combined using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to estimate daily ET at 30m resolution to investigate seasonal water use behavior at the level of individual forest stands and land cover patches. A new method, also exploiting the STARFM algorithm, is used to fill gaps in the Landsat ET retrievals due to cloud cover and/or the scan-line corrector (SLC) failure on Landsat 7. The retrieved daily ET time series agree well with observations at two AmeriFlux eddy covariance flux tower sites in a managed pine plantation within the modeling domain: US-NC2 located in a mid-rotation (20-year-old) loblolly pine stand and USNC3 located in a recently clear-cut and replanted field site. Root mean square errors (RMSEs) for NC2 and NC3 were 0.99 and 1.02mmday-1, respectively, with mean absolute errors of approximately 29% at the daily time step, 12% at the monthly time step and 0.7% over the full study period at the two flux tower sites. Analyses of water use patterns over the plantation indicate increasing seasonal ET with stand age for young to mid-rotation stands up to 20 years, but little dependence on age for older stands. An accounting of consumptive water use by major land cover classes representative of the modeling domain is presented, as well as relative partitioning of ET between evaporation (E) and transpiration (T ) components obtained with the TSEB. The study provides new insights about the effects of management and land use change on water yield over forested landscapes. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => February [DTD_PUB] => 17 [DTY_PUB] => 2017 [PUB_START] => 2017-02-17 [PUB_END] => 2017-02-17 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [80] => Array ( [@attributes] => Array ( [id] => 291826819072 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Agricultural Applications for Remotely Sensed Evapotranspiration Data in Monitoring Water Use, Water Quality, and Water Security [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826819073 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826819074 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826819075 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826819076 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826819077 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826819078 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826819079 ) [FACULTY_NAME] => Array ( ) [FNAME] => Amirreza [MNAME] => Array ( ) [LNAME] => Sharifi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826819080 ) [FACULTY_NAME] => Array ( ) [FNAME] => Thomas [MNAME] => R [LNAME] => Holmes [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 291826819081 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2016 [ISSUE] => Array ( ) [PAGENUM] => U12A--04 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2016 [PUB_START] => 2016-01-01 [PUB_END] => 2016-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [81] => Array ( [@attributes] => Array ( [id] => 291826876416 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826876417 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kathryn [MNAME] => A [LNAME] => Semmens [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826876418 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826876419 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826876420 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826876421 ) [FACULTY_NAME] => Array ( ) [FNAME] => Joseph [MNAME] => G [LNAME] => Alfieri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826876422 ) [FACULTY_NAME] => Array ( ) [FNAME] => Lynn [MNAME] => Array ( ) [LNAME] => McKee [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826876423 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => H [LNAME] => Prueger [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 291826876424 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => R [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 291826876425 ) [FACULTY_NAME] => Array ( ) [FNAME] => Carmelo [MNAME] => Array ( ) [LNAME] => Cammalleri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [9] => Array ( [@attributes] => Array ( [id] => 291826876426 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [10] => Array ( [@attributes] => Array ( [id] => 291826876427 ) [FACULTY_NAME] => Array ( ) [FNAME] => Array ( ) [MNAME] => Array ( ) [LNAME] => others [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 185 [ISSUE] => Array ( ) [PAGENUM] => 155--170 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2016 [PUB_START] => 2016-01-01 [PUB_END] => 2016-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [82] => Array ( [@attributes] => Array ( [id] => 245248798720 ) [CONTYPE] => Journal Article [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248798721 ) [FACULTY_NAME] => Array ( ) [FNAME] => Zhuosen [MNAME] => Array ( ) [LNAME] => Wang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248798722 ) [FACULTY_NAME] => Array ( ) [FNAME] => Angela [MNAME] => M. [LNAME] => Erb [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248798723 ) [FACULTY_NAME] => Array ( ) [FNAME] => Crystal [MNAME] => B. [LNAME] => Schaaf [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248798724 ) [FACULTY_NAME] => Array ( ) [FNAME] => Qingsong [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248798725 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yan [MNAME] => Array ( ) [LNAME] => Liu [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248798726 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248798727 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yanmin [MNAME] => Array ( ) [LNAME] => Shuai [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248798728 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kimberly [MNAME] => A. [LNAME] => Casey [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248798729 ) [FACULTY_NAME] => Array ( ) [FNAME] => Miguel [MNAME] => O. [LNAME] => Román [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Remote Sensing of Environment [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 185 [ISSUE] => Array ( ) [PAGENUM] => 71-83 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/84960977944 [EDITORS] => Array ( ) [ISBNISSN] => 00344257 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2016 Elsevier Inc.Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100 m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high-burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500 m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => November [DTD_PUB] => 1 [DTY_PUB] => 2016 [PUB_START] => 2016-11-01 [PUB_END] => 2016-11-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [83] => Array ( [@attributes] => Array ( [id] => 245248831488 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Longterm daily fieldscale evapotranspiration estimation using multisatellite data fusion in an intensively drained agricultural area in South Dakota, USA [TITLE_SECONDARY] => International Geoscience and Remote Sensing Symposium (IGARSS) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248831489 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248831490 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => C. [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248831491 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248831492 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P. [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248831493 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248831494 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tilden [MNAME] => Array ( ) [LNAME] => Meyers [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248831495 ) [FACULTY_NAME] => Array ( ) [FNAME] => Tim [MNAME] => Array ( ) [LNAME] => Wilson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248831496 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248831497 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2016-November [ISSUE] => Array ( ) [PAGENUM] => 3547-3550 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85007504731 [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2016 IEEE.Subsurface tile drainage is a widely used agricultural practice in Midwestern USA to remove excess water to improve crop yield. Research shows an increasing trend of baseflow over the last 60 years and may be due to this artificial drainage activity. The influence of tile drainage on streamflow has been studied using in situ measurements and hydrological models. Evapotranspiration (ET) is an important component of hydrologic cycle. However, the impact of tile drainage on ET, or on other components of the water budget, is not well documented. In this study, we applied an energy balance based multi-sensor (GOES, MODIS and Landsat) data fusion method to estimate daily 30 m ET over an intensively drained area in South Dakota, USA to assess the model performance and further explore the spatial and temporal ET patterns through 2004-2013 and their relationship to drain installation. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => November [DTD_PUB] => 1 [DTY_PUB] => 2016 [PUB_START] => 2016-11-01 [PUB_END] => 2016-11-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [84] => Array ( [@attributes] => Array ( [id] => 245248835584 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Mapping evapotranspiration at multiple scales using multi-sensor data fusion [TITLE_SECONDARY] => International Geoscience and Remote Sensing Symposium (IGARSS) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248835585 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248835586 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248835587 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248835588 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248835589 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248835590 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248835591 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [7] => Array ( [@attributes] => Array ( [id] => 245248835592 ) [FACULTY_NAME] => Array ( ) [FNAME] => Thomas [MNAME] => Array ( ) [LNAME] => Holmes [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [8] => Array ( [@attributes] => Array ( [id] => 245248835593 ) [FACULTY_NAME] => Array ( ) [FNAME] => Wayne [MNAME] => Array ( ) [LNAME] => Dulaney [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2016-November [ISSUE] => Array ( ) [PAGENUM] => 226-229 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85007504808 [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2016 IEEE.Thermal-infrared remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a multi-scale LST-based energy balance model built using a Two-Source Energy Balance (TSEB) algorithm, which solves for the soil/substrate and canopy temperatures and flux partitioning. A regional modeling system applies the TSEB to time-differential LST measurements from geostationary satellites, providing coarse ET estimates which can be downscaled to finer spatial resolutions using data from polar orbiting satellites. This modeling system, along with strategies for fusing information from multiple satellite platforms and wavebands, has been used to generate ET maps from field to global scales. We describe applications for high spatiotemporal resolution ET retrievals in assessing impacts of human activities and climate change on water resources and agricultural production. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => November [DTD_PUB] => 1 [DTY_PUB] => 2016 [PUB_START] => 2016-11-01 [PUB_END] => 2016-11-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [85] => Array ( [@attributes] => Array ( [id] => 245248849920 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Study of water use in agricultural landscapes at high spatiotemporal resulotion [TITLE_SECONDARY] => International Geoscience and Remote Sensing Symposium (IGARSS) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248849921 ) [FACULTY_NAME] => Array ( ) [FNAME] => Yang [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248849922 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248849923 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248849924 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248849925 ) [FACULTY_NAME] => Array ( ) [FNAME] => Liang [MNAME] => Array ( ) [LNAME] => Sun [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2016-November [ISSUE] => Array ( ) [PAGENUM] => 7172-7175 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/85007439484 [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2016 IEEE.A detailed spatially explicit evapotranspiration (ET) mapping at daily basis is of substantial benefit for agricultural water management. An integrated multi-sensor approach that combines the benefits of the high spatial resolution of Landsat and the high temporal resolution of MODIS and geostationary satellites to provide daily field-scale ET estimates is evaluated over two different agricultural landscapes. The ET data fusion methodology described here can provide detailed information about daily and seasonal water use patterns. This information can be of utility for irrigation managers at the scale of individual fields as well as for regional monitoring of water use toward allocation and conservation efforts. [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => November [DTD_PUB] => 1 [DTY_PUB] => 2016 [PUB_START] => 2016-11-01 [PUB_END] => 2016-11-01 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [86] => Array ( [@attributes] => Array ( [id] => 291826872320 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Mapping Evapotranspiration in the Alps through Two-Source Energy-Balance Models and Multi-Satellite Data Fusion: Scale Effects in Heterogeneous Landscapes [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826872321 ) [FACULTY_NAME] => Array ( ) [FNAME] => Mariapina [MNAME] => Array ( ) [LNAME] => Castelli [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826872322 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826872323 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826872324 ) [FACULTY_NAME] => Array ( ) [FNAME] => Georg [MNAME] => Array ( ) [LNAME] => Wohlfahrt [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826872325 ) [FACULTY_NAME] => Array ( ) [FNAME] => Giacomo [MNAME] => Array ( ) [LNAME] => Bertoldi [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826872326 ) [FACULTY_NAME] => Array ( ) [FNAME] => Enrico [MNAME] => Array ( ) [LNAME] => Tomelleri [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 291826872327 ) [FACULTY_NAME] => Array ( ) [FNAME] => Caludia [MNAME] => Array ( ) [LNAME] => Notarnicola [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2015 [ISSUE] => Array ( ) [PAGENUM] => H31J--05 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2015 [PUB_START] => 2015-01-01 [PUB_END] => 2015-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [87] => Array ( [@attributes] => Array ( [id] => 291826880512 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Multi-scale satellite assessment of water availability and agricultural drought: from field to global scales [TITLE_SECONDARY] => ASABE 1st Climate Change Symposium: Adaptation and Mitigation Conference Proceedings [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826880513 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826880514 ) [FACULTY_NAME] => Array ( ) [FNAME] => C [MNAME] => Array ( ) [LNAME] => Hian [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826880515 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826880516 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826880517 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => Array ( ) [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => Array ( ) [ISSUE] => Array ( ) [PAGENUM] => 1--1 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2015 [PUB_START] => 2015-01-01 [PUB_END] => 2015-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [88] => Array ( [@attributes] => Array ( [id] => 291826919424 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Upscaling and Downscaling of Land Surface Fluxes with Surface Temperature [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826919425 ) [FACULTY_NAME] => Array ( ) [FNAME] => William [MNAME] => P [LNAME] => Kustas [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826919426 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826919427 ) [FACULTY_NAME] => Array ( ) [FNAME] => Christopher [MNAME] => Array ( ) [LNAME] => Hain [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826919428 ) [FACULTY_NAME] => Array ( ) [FNAME] => John [MNAME] => D [LNAME] => Albertson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826919429 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 291826919430 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => Array ( ) [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 2015 [ISSUE] => Array ( ) [PAGENUM] => H54B--06 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2015 [PUB_START] => 2015-01-01 [PUB_END] => 2015-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [89] => Array ( [@attributes] => Array ( [id] => 291826921472 ) [CONTYPE] => Conference Proceeding [CONTYPEOTHER] => Array ( ) [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Using a data fusion method to estimate daily stand-scale evapotranspiration over a managed pine plantation in North Carolina, USA [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826921473 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826921474 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) 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=> Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => Array ( ) [EDITORS] => Array ( ) [ISBNISSN] => Array ( ) [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Array ( ) [PUBLICAVAIL] => Array ( ) [ABSTRACT] => Array ( ) [KEYWORDS] => Array ( ) [FULL_TEXT] => Array ( ) [DTM_EXPSUB] => Array ( ) [DTD_EXPSUB] => Array ( ) [DTY_EXPSUB] => Array ( ) [EXPSUB_START] => Array ( ) [EXPSUB_END] => Array ( ) [DTM_SUB] => Array ( ) [DTD_SUB] => Array ( ) [DTY_SUB] => Array ( ) [SUB_START] => Array ( ) [SUB_END] => Array ( ) [DTM_ACC] => Array ( ) [DTD_ACC] => Array ( ) [DTY_ACC] => Array ( ) [ACC_START] => Array ( ) [ACC_END] => Array ( ) [DTM_PUB] => Array ( ) [DTD_PUB] => Array ( ) [DTY_PUB] => 2015 [PUB_START] => 2015-01-01 [PUB_END] => 2015-12-31 [COMMUNITY_ENGAGEMENT] => Array ( ) [MSU_MISSION] => Array ( ) [SCHTEACH_REF] => Array ( ) [EXPLANATION] => Array ( ) [USER_REFERENCE_CREATOR] => Yes ) [90] => Array ( [@attributes] => Array ( [id] => 245248813056 ) [CONTYPE] => Other [CONTYPEOTHER] => Magazine/Trade Publication [EXTRELATED] => Array ( ) [PRESENT] => Array ( ) [INTERNATIONAL] => Array ( ) [STATUS] => Published [TITLE] => Fusing Landsat and MODIS Data for Vegetation Monitoring [TITLE_SECONDARY] => Array ( ) [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 245248813057 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng [MNAME] => Array ( ) [LNAME] => Gao [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 245248813058 ) [FACULTY_NAME] => Array ( ) [FNAME] => Thomas [MNAME] => Array ( ) [LNAME] => Hilker [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 245248813059 ) [FACULTY_NAME] => Array ( ) [FNAME] => Xiaolin [MNAME] => Array ( ) [LNAME] => Zhu [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 245248813060 ) [FACULTY_NAME] => Array ( ) [FNAME] => Martha [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 245248813061 ) [FACULTY_NAME] => Array ( ) [FNAME] => Jeffrey [MNAME] => Array ( ) [LNAME] => Masek [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [5] => Array ( [@attributes] => Array ( [id] => 245248813062 ) [FACULTY_NAME] => Array ( ) [FNAME] => Peijuan [MNAME] => Array ( ) [LNAME] => Wang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [6] => Array ( [@attributes] => Array ( [id] => 245248813063 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) ) [PUBLISHER] => IEEE Geoscience and Remote Sensing Magazine [PUBCTYST] => Array ( ) [PUBCNTRY] => Array ( ) [VOLUME] => 3 [ISSUE] => 3 [PAGENUM] => 47-60 [DOI] => Array ( ) [CRIS_NUM_CAT] => Array ( ) [CRIS_NUM] => Array ( ) [AREA] => Array ( ) [WEB_ADDRESS] => https://api.elsevier.com/content/abstract/scopus_id/84943803653 [EDITORS] => Array ( ) [ISBNISSN] => 21686831 24732397 [PMCID] => Array ( ) [AUDIENCE] => Array ( ) [REFEREED] => Yes [PUBLICAVAIL] => Array ( ) [ABSTRACT] => © 2013 IEEE.Crop condition and natural vegetation monitoring require high resolution remote sensing imagery in both time and space - a requirement that cannot currently be satisfied by any single Earth observing sensor in isolation. The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery from coarse resolution sensors such as MODIS are typically superior to finer resolution data in terms of their revisit frequency, they lack spatial detail to capture surface features for many applications. The Landsat satellite series provides medium spatial resolution (30m) imagery which is well suited to capturing surface details, but a long revisit cycle (16-day) has limited its use in describing daily surface changes. Data fusion approaches provide an alternative way to utilize observations from multiple sensors so that the fused results can provide higher value than can an individual sensor alone. In this paper, we review the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and two extended data fusion models (STAARCH and ESTARFM) that have been used to fuse MODIS and Landsat data. The fused MODISLandsat results inherit the spatial details of Landsat (30 m) and the temporal revisit frequency of MODIS (daily). The theoretical basis of the fusion approach is described and recent applications are presented. While these approaches can produce imagery with high spatiotemporal resolution, they still rely on the availability of actual satellite images and the quality of ingested remote sensing products. As a result, data fusion is useful for bridging gaps between medium resolution image acquisitions, but cannot replace actual satellite missions. 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Fusion Approach in Agricultural and Forested Sites in the US [TITLE_SECONDARY] => AGU Fall Meeting Abstracts [INTELLCONT_AUTH] => Array ( [0] => Array ( [@attributes] => Array ( [id] => 291826829313 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826829314 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826829315 ) [FACULTY_NAME] => Array ( ) [FNAME] => KA [MNAME] => Array ( ) [LNAME] => Semmens [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826829316 ) [FACULTY_NAME] => 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[INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [1] => Array ( [@attributes] => Array ( [id] => 291826903042 ) [FACULTY_NAME] => Array ( ) [FNAME] => MC [MNAME] => Array ( ) [LNAME] => Anderson [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [2] => Array ( [@attributes] => Array ( [id] => 291826903043 ) [FACULTY_NAME] => Array ( ) [FNAME] => Kathryn [MNAME] => A [LNAME] => Semmens [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [3] => Array ( [@attributes] => Array ( [id] => 291826903044 ) [FACULTY_NAME] => 2390334 [FNAME] => Yun [MNAME] => Array ( ) [LNAME] => Yang [INSTITUTION] => Array ( ) [ROLE] => Author [MSU_ID] => Array ( ) [DEP] => Array ( ) [STUDENT_LEVEL] => Array ( ) ) [4] => Array ( [@attributes] => Array ( [id] => 291826903045 ) [FACULTY_NAME] => Array ( ) [FNAME] => Feng 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Dr. Yun Yang | Forestry | College of Forest Resources | Mississippi State University

Dr. Yun Yang

Dr. Yun  Yang

Title

  • Assistant Professor

Contact Information

yy285@msstate.edu
Office: 662-325-7948
Thompson Hall, Rm 349

Overview

Education

  • University of Massachusetts, Doctor of Philosophy (Ph.D.), Environmental Science
  • University of Massachusetts, Master of Science (M.S.), Environmental Science
  • Beijing Normal University, Bachelor of Science (B.S.), Geography

Research Interests

Field-scale evapotranspiration mapping using remotely sensed data with cloud computing
Multi-sensor data fusion for improved spatiotemporal sampling
Vegetation health monitoring for agriculture and natural resources management

Websites

Publications

Year Publications
2024

Ahmad, S., Holmes, T., Kumar, S., Lahmers, T., Liu, P., Nie, W., Getirana, A., Bindlish, R., Guzman, A., Melton, F., Locke, K., Yang, Y. 2024. Droughts dominate ecosystem recovery from fires in the Western US. Nature Ecology and Evolution 8(229-238).

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2023

Knipper, K., Yang, Y., Anderson, M., Bambach, N., Kustas, W., McElrone, A., Gao, F., Alsina, M. M. 2023. Decreased latency in landsat-derived land surface temperature products: A case for near-real-time evapotranspiration estimation in California. Agricultural Water Management 283:108316.

2023

Isaacson, B. N., Yang, Y., Anderson, M. C., Clark, K. L., Grabosky, J. C. 2023. The effects of forest composition and management on evapotranspiration in the New Jersey Pinelands. Agricultural and Forest Meteorology 339:109588.

2023

Tiwari, P., Poudel, K. P., Yang, J., Silva, B., Yang, Y., McConnell, M. D. 2023. Marginal agricultural land identification in the Lower Mississippi Alluvial Valley based on remote sensing and machine learning model. International Journal of Applied Earth Observation and Geoinformation 125:103568.

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2023

Volk, J. M., Huntington, J., Melton, F. S., Allen, R., Anderson, M. C., Fisher, J. B., Kilic, A., Senay, G., Halverson, G., Knipper, K., Minor, B., Pearson, C., Wang, T., Yang, Y., Evett, S., French, A. N., Jasoni, R., Kustas, W. 2023. Development of a benchmark Eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS. Agricultural and Forest Meteorology 331:109307.

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2022

Volk, J., Huntington, J., Melton, F., Allen, R., Anderson, M., Dunkerly, C., Fisher, J., Friedrichs, M., Hain, C., Halverson, G., others, . 2022. OpenET Satellite-based ET Intercomparisons with Ground-based Measurements: Phase II Results. Authorea Preprints

2022

Wang, Y., Zhou, Y., Franz, K. J., Zhang, X., Qi, J., Jia, G., Yang, Y. 2022. Irrigation plays significantly different roles in influencing hydrological processes in two breadbasket regions. Science of the Total Environment 844.

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2022

Kang, Y., Gao, F., Anderson, M., Kustas, W., Nieto, H., Knipper, K., Yang, Y., White, W., Alfieri, J., Torres-Rua, A., Alsina, M. M., Karnieli, A. 2022. Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation. Irrigation Science 40(4-5):531-551.

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2022

Xue, J., Anderson, M. C., Gao, F., Hain, C., Knipper, K. R., Yang, Y., Kustas, W. P., Yang, Y., Bambach, N., McElrone, A. J., Castro, S. J., Alfieri, J. G., Prueger, J. H., McKee, L. G., Hipps, L. E., del Mar Alsina, M. 2022. Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion. Irrigation Science 40(4-5):609-634.

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2022

Lee, S., Qi, J., McCarty, G. W., Anderson, M., Yang, Y., Zhang, X., Moglen, G. E., Kwak, D., Kim, H., Lakshmi, V., Kim, S. 2022. Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed. Agricultural Water Management 264.

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2022

Yang, Y., Anderson, M., Gao, F., Xue, J., Knipper, K., Hain, C. 2022. Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System. Remote Sensing 14(8).

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2021

Yang, Y., Anderson, M. C., Gao, F., Johnson, D. M., Yang, Y., Sun, L., Dulaney, W., Hain, C. R., Otkin, J. A., Prueger, J., others, . 2021. Phenological corrections to a field-scale, ET-based crop stress indicator: An application to yield forecasting across the US Corn Belt. Remote Sensing of Environment 257:112337.

2021

Yang, Y., Anderson, M. C., Gao, F., Wood, J. D., Gu, L., Hain, C. 2021. Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging. Remote Sensing of Environment 265:112640.

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2021

Xue, J., Anderson, M. C., Gao, F., Hain, C., Yang, Y., Knipper, K. R., Kustas, W. P., Yang, Y. 2021. Mapping daily evapotranspiration at field scale using the harmonized landsat and sentinel-2 dataset, with sharpened VIIRS as a sentinel-2 thermal proxy. Remote Sensing 13(17).

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2021

Aguilos, M., Sun, G., Noormets, A., Domec, J., McNulty, S., Gavazzi, M., Minick, K., Mitra, B., Prajapati, P., Yang, Y., King, J. 2021. Effects of land-use change and drought on decadal evapotranspiration and water balance of natural and managed forested wetlands along the southeastern US lower coastal plain. Agricultural and Forest Meteorology 303.

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2021

Kang, Y., Ozdogan, M., Gao, F., Anderson, M. C., White, W. A., Yang, Y., Yang, Y., Erickson, T. A. 2021. A data-driven approach to estimate leaf area index for Landsat images over the contiguous US. Remote Sensing of Environment 258.

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2021

Yang, Y., Anderson, M. C., Gao, F., Johnson, D. M., Yang, Y., Sun, L., Dulaney, W., Hain, C. R., Otkin, J. A., Prueger, J., Meyers, T. P., Bernacchi, C. J., Moore, C. E. 2021. Phenological corrections to a field-scale, ET-based crop stress indicator: An application to yield forecasting across the U.S. Corn Belt. Remote Sensing of Environment 257.

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2021

Sun, L., Gao, F., Xie, D., Anderson, M., Chen, R., Yang, Y., Yang, Y., Chen, Z. 2021. Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach. Remote Sensing of Environment 253.

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2021

Cawse-Nicholson, K., Anderson, M. C., Yang, Y., Yang, Y., Hook, S. J., Fisher, J. B., Halverson, G., Hulley, G. C., Hain, C., Baldocchi, D. D., Brunsell, N. A., Desai, A. R., Griffis, T. J., Novick, K. A. 2021. Evaluation of a CONUS-Wide ECOSTRESS DisALEXI Evapotranspiration Product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:10117-10133.

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2021

Anderson, M. C., Yang, Y., Xue, J., Knipper, K. R., Yang, Y., Gao, F., Hain, C. R., Kustas, W. P., Cawse-Nicholson, K., Hulley, G., Fisher, J. B., Alfieri, J. G., Meyers, T. P., Prueger, J., Baldocchi, D. D., Rey-Sanchez, C. 2021. Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales. Remote Sensing of Environment 252.

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2021

Melton, F. S., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Senay, G. B., Volk, J., Hain, C., Johnson, L., Ruhoff, A., Blankenau, P., Bromley, M., Carrara, W., Daudert, B., Doherty, C., Dunkerly, C., Friedrichs, M., Guzman, A., Halverson, G., Hansen, J., Harding, J., Kang, Y., Ketchum, D., Minor, B., Morton, C., Ortega-Salazar, S., Ott, T., Ozdogan, M., ReVelle, P. M., Schull, M., Wang, C., Yang, Y., Anderson, R. G. 2021. OpenET: Filling a Critical Data Gap in Water Management for the Western United States. Journal of the American Water Resources Association

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2020

Xue, J., Anderson, M. C., Gao, F., Hain, C., Sun, L., Yang, Y., Knipper, K. R., Kustas, W. P., Torres-Rua, A., Schull, M. 2020. Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances. Remote Sensing of Environment 251.

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2020

Lei, F., Crow, W. T., Kustas, W. P., Dong, J., Yang, Y., Knipper, K. R., Anderson, M. C., Gao, F., Notarnicola, C., Greifeneder, F., McKee, L. M., Alfieri, J. G., Hain, C., Dokoozlian, N. 2020. Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sensing of Environment 239.

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2020

Yang, Y., Anderson, M., Gao, F., Hain, C., Noormets, A., Sun, G., Wynne, R., Thomas, V., Sun, L. 2020. Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data. Remote Sensing of Environment 238.

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2019

Knipper, K. R., Kustas, W. P., Anderson, M. C., Alfieri, J. G., Prueger, J. H., Hain, C. R., Gao, F., Yang, Y., McKee, L. G., Nieto, H., Hipps, L. E., Alsina, M. M., Sanchez, L. 2019. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science 37(3):431-449.

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2019

Anderson, M., Diak, G., Gao, F., Knipper, K., Hain, C., Eichelmann, E., Hemes, K. S., Baldocchi, D., Kustas, W., Yang, Y. 2019. Impact of insolation data source on remote sensing retrievals of evapotranspiration over the California delta. Remote Sensing 11(3).

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2018

Castelli, M., Anderson, M., Yang, Y., Wohlfahrt, G., Bertoldi, G., Niedrist, G., Hammerle, A., Zhao, P., Zebisch, M., Notarnicola, C. 2018. Two-source energy balance modeling of evapotranspiration in Alpine grasslands. Remote Sensing of Environment 209:327--342.

2018

Kustas, W. P., Anderson, M. C., Alfieri, J. G., Knipppper, K., Torres-Rua, A., Parry, C. K., Nieto, H., Agam, N., White, W. A., Gao, F., McKee, L., Prueger, J. H., Hipppps, L. E., Los, S., Alsina, M. M., Sanchez, L., Sams, B., Dokoozlian, N., McKee, M., Jones, S., Yang, Y., Wilson, T. G., Lei, F., McElrone, A., Heitman, J. L., Howard, A. M., Post, K., Melton, F., Hain, C. 2018. The grape remote sensing atmospheric profile and evapotranspiration experiment. Bulletin of the American Meteorological Society 99(9):1791-1812.

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2018

Anderson, M., Gao, F., Knipper, K., Hain, C., Dulaney, W., Baldocchi, D., Eichelmann, E., Hemes, K., Yang, Y., Medellin-Azuara, J., Kustas, W. 2018. Field-scale assessment of land and water use change over the California delta using remote sensing. Remote Sensing 10(6).

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2018

Yang, Y., Anderson, M. C., Gao, F., Wardlow, B., Hain, C. R., Otkin, J. A., Alfieri, J., Yang, Y., Sun, L., Dulaney, W. 2018. Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA. Remote Sensing of Environment 210:387-402.

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2018

Kim, J. H., Hwang, T., Yang, Y., Schaaf, C. L., Boose, E., Munger, J. W. 2018. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. Journal of Geophysical Research: Biogeosciences 123(6):1960-1975.

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2017

Sun, L., Anderson, M. C., Gao, F., Hain, C., Alfieri, J. G., Sharifi, A., McCarty, G. W., Yang, Y., Yang, Y., Kustas, W. P., others, . 2017. Investigating water use over the C hoptank R iver W atershed using a multisatellite data fusion approach. Water Resources Research 53(7):5298--5319.

2017

Sun, L., Chen, Z., Gao, F., Anderson, M., Song, L., Wang, L., Hu, B., Yang, Y. 2017. Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data. Computers and Geosciences 105:10-20.

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2017

Sun, L., Anderson, M. C., Gao, F., Hain, C., Alfieri, J. G., Sharifi, A., McCarty, G. W., Yang, Y., Yang, Y., Kustas, W. P., McKee, L. 2017. Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach. Water Resources Research 53(7):5298-5319.

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2017

Wang, Z., Schaaf, C. B., Sun, Q., Kim, J., Erb, A. M., Gao, F., Román, M. O., Yang, Y., Petroy, S., Taylor, J. R., Masek, J. G., Morisette, J. T., Zhang, X., Papuga, S. A. 2017. Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product. International Journal of Applied Earth Observation and Geoinformation 59:104-117.

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2017

Yang, Y., Anderson, M., Gao, F., Hain, C., Kustas, W., Meyers, T., Crow, W., Finocchiaro, R., Otkin, J., Sun, L., Yang, Y. 2017. Impact of tile drainage on evapotranspiration in South Dakota, USA, based on high spatiotemporal resolution evapotranspiration time series from a multisatellite data fusion system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(6):2550-2564.

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2017

Yang, Y., Anderson, M. C., Gao, F., Hain, C. R., Semmens, K. A., Kustas, W. P., Noormets, A., Wynne, R. H., Thomas, V. A., Sun, G. 2017. Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion. Hydrology and Earth System Sciences 21(2):1017-1037.

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2016

Semmens, K. A., Anderson, M. C., Kustas, W. P., Gao, F., Alfieri, J. G., McKee, L., Prueger, J. H., Hain, C. R., Cammalleri, C., Yang, Y., others, . 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment 185:155--170.

2016

Wang, Z., Erb, A. M., Schaaf, C. B., Sun, Q., Liu, Y., Yang, Y., Shuai, Y., Casey, K. A., Román, M. O. 2016. Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sensing of Environment 185:71-83.

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2011

Tenenbaum, D. E., Yang, Y., Zhou, W. 2011. A comparison of object-oriented image classification and transect sampling methods for obtaining land cover information from digital orthophotography. GIScience \& Remote Sensing 48(1):112--129.

Extension Publications

Year Publications

Graduate Students

  • Ian Sartorio
  • WeiNa Duan
  • Gaurav Baral

Society Memberships

  • American Geophysical Union
  • American Meteorological Society