{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:44:45Z","timestamp":1760150685128,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2021YFF0704400","41971351"],"award-info":[{"award-number":["2021YFF0704400","41971351"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFF0704400","41971351"],"award-info":[{"award-number":["2021YFF0704400","41971351"]}],"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>Evaporation is a critical process involved in energy and water balance at the Earth\u2019s surface and bears significant implications for water resource management, agricultural irrigation, and drought monitoring, among others. In this study, we focused on establishing a 1 km daily surface evaporation estimation for the Yangtze River Basin from 2010 to 2019 by using a machine learning method, and then analyzed its spatiotemporal patterns. The findings showed spatial heterogeneity in the Yangtze River Basin, indicating higher evaporation rates in the southwestern and southeastern regions in contrast to the western and northern areas. Additionally, the basin exhibited a strong spatial autocorrelation, indicating the influence of one spatial unit on the others. Furthermore, most regions in the basin displayed non-significant changes in surface evaporation, with some areas in the upper reaches exhibiting significant increases and a few regions near the source of the Yangtze River experiencing significant decreases. This study contributes to a better understanding of the spatial and temporal distribution of evaporation in the Yangtze River Basin, providing valuable insights for water resource management, environmental studies, and hydrological modeling in the region.<\/jats:p>","DOI":"10.3390\/rs16010057","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T08:53:01Z","timestamp":1703235181000},"page":"57","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimation and Spatiotemporal Analysis of Surface Evaporation in the Yangtze River Basin from 2010 to 2019"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6624-6693","authenticated-orcid":false,"given":"Zeqiang","family":"Chen","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Dongyang","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ke","family":"Wan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Wenzhe","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Infomation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3521-9972","authenticated-orcid":false,"given":"Nengcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D15","DOI":"10.1029\/2004JD004511","article-title":"A spatial analysis of pan evaporation trends in China, 1955\u20132000","volume":"109","author":"Liu","year":"2004","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","first-page":"718","article-title":"Changes in pan evaporation and actual evapotranspiration of the Yangtze River basin during 1960\u20142019","volume":"33","author":"Ye","year":"2022","journal-title":"Adv. Water Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.11834\/jrs.20211310","article-title":"Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration","volume":"25","author":"Liu","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/j.jhydrol.2016.09.060","article-title":"Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956\u20132015","volume":"542","author":"Fan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1061\/(ASCE)IR.1943-4774.0000315","article-title":"Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran)","volume":"137","author":"Shiri","year":"2011","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dong, L., Zeng, W., Wu, L., Lei, G., Chen, H., Srivastava, A.K., and Gaiser, T. (2021). Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm. Water, 13.","DOI":"10.3390\/w13030256"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.agwat.2010.08.001","article-title":"Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China","volume":"98","author":"Ding","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.jhydrol.2005.11.029","article-title":"Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment","volume":"327","author":"Xu","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s00704-006-0276-y","article-title":"Changes of pan evaporation and reference evapotranspiration in the Yangtze River basin","volume":"90","author":"Wang","year":"2007","journal-title":"Theor. Appl. Climatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1002\/cjg2.1744","article-title":"Pan Evaporation Change and Its Impact on Water Cycle over the Upper Reach of Yangtze River","volume":"55","author":"Rong","year":"2012","journal-title":"Chin. J. Geophys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.jhydrol.2018.09.055","article-title":"Daily pan evaporation modeling from local and cross-station data using three tree-basedmachine learning models","volume":"566","author":"Lu","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s00704-019-02871-3","article-title":"Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China","volume":"138","author":"Chen","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112256","DOI":"10.1016\/j.rse.2020.112256","article-title":"A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering","volume":"254","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_14","first-page":"5669","article-title":"A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e2019EA001006","DOI":"10.1029\/2019EA001006","article-title":"An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables","volume":"7","author":"Zhang","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_16","unstructured":"Cheng, J., Dong, S., and Shi, J. (2021). 1 km Seamless Land Surface Temperature Dataset of China (2002\u20132020), National Tibetan Plateau\/Third Pole Environment Data Center."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest:\u2009 A Classification and Regression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_21","unstructured":"Guang-Bin, H., Qin-Yu, Z., and Chee-Kheong, S. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat No04CH37541), Budapest, Hungary."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13132","DOI":"10.1038\/s41598-022-17263-3","article-title":"Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms","volume":"12","author":"Abed","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1061\/(ASCE)1084-0699(2006)11:1(65)","article-title":"Artificial Neural Network Models of Daily Pan Evaporation","volume":"11","author":"Keskin","year":"2006","journal-title":"J. Hydrol. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/57\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:26Z","timestamp":1760132426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010057"],"URL":"https:\/\/doi.org\/10.3390\/rs16010057","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,12,22]]}}}