{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:19:09Z","timestamp":1760145549402,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2243203"],"award-info":[{"award-number":["U2243203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter \u03c9 is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between \u03c9 and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter \u03c9 for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter \u03c9 established for the three subwatersheds were reasonable (R2 = 0.60, 0.80, and 0.40), and parameter \u03c9 was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development.<\/jats:p>","DOI":"10.3390\/rs16152777","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T16:37:17Z","timestamp":1722271037000},"page":"2777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improvements to a Crucial Budyko-Fu Parameter and Evapotranspiration Estimates via Vegetation Optical Depth over the Yellow River Basin"],"prefix":"10.3390","volume":"16","author":[{"given":"Xingyi","family":"Wang","sequence":"first","affiliation":[{"name":"The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China"},{"name":"College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4067-298X","authenticated-orcid":false,"given":"Jiaxin","family":"Jin","sequence":"additional","affiliation":[{"name":"The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China"},{"name":"College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China"},{"name":"Jiangsu Key Laboratory of Watershed Soil and Water Processes, Nanjing 211100, China"},{"name":"National Earth System Science Data Center, National Science and Technology Infrastructure of China, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"ref_1","first-page":"1051","article-title":"Generalized water resources assessment based on watershed hydrologic cycle model \u2160. 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