{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T02:55:45Z","timestamp":1774752945446,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China programs","award":["41901379"],"award-info":[{"award-number":["41901379"]}]},{"name":"National Natural Science Foundation of China programs","award":["42001310"],"award-info":[{"award-number":["42001310"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm\/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm\/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm\/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively).<\/jats:p>","DOI":"10.3390\/rs14225876","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:33:32Z","timestamp":1669005212000},"page":"5876","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Xianghong","family":"Che","sequence":"first","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Beijing 100830, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4470-3616","authenticated-orcid":false,"given":"Hankui K.","family":"Zhang","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7221-2246","authenticated-orcid":false,"given":"Qing","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6919-569X","authenticated-orcid":false,"given":"Zutao","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Stanford University, Stanford, CA 94305, USA"}]},{"given":"Jiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Beijing 100830, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.1002\/2016WR020175","article-title":"The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources","volume":"53","author":"Fisher","year":"2017","journal-title":"Water Resour. 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