{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T21:57:31Z","timestamp":1774735051545,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T00:00:00Z","timestamp":1711843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Excellent Young Scientist Fund of Natural Science Foundation of Hebei Province","award":["D2023205012"],"award-info":[{"award-number":["D2023205012"]}]},{"name":"Excellent Young Scientist Fund of Natural Science Foundation of Hebei Province","award":["42101382"],"award-info":[{"award-number":["42101382"]}]},{"name":"Excellent Young Scientist Fund of Natural Science Foundation of Hebei Province","award":["42201407"],"award-info":[{"award-number":["42201407"]}]},{"name":"Excellent Young Scientist Fund of Natural Science Foundation of Hebei Province","award":["ZR2020QD016"],"award-info":[{"award-number":["ZR2020QD016"]}]},{"name":"Excellent Young Scientist Fund of Natural Science Foundation of Hebei Province","award":["ZR2022QD120"],"award-info":[{"award-number":["ZR2022QD120"]}]},{"name":"National Natural Science Foundation of China","award":["D2023205012"],"award-info":[{"award-number":["D2023205012"]}]},{"name":"National Natural Science Foundation of China","award":["42101382"],"award-info":[{"award-number":["42101382"]}]},{"name":"National Natural Science Foundation of China","award":["42201407"],"award-info":[{"award-number":["42201407"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QD016"],"award-info":[{"award-number":["ZR2020QD016"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2022QD120"],"award-info":[{"award-number":["ZR2022QD120"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["D2023205012"],"award-info":[{"award-number":["D2023205012"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["42101382"],"award-info":[{"award-number":["42101382"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["42201407"],"award-info":[{"award-number":["42201407"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["ZR2020QD016"],"award-info":[{"award-number":["ZR2020QD016"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["ZR2022QD120"],"award-info":[{"award-number":["ZR2022QD120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately predicting actual evapotranspiration (ETa) at the regional scale is crucial for efficient water resource allocation and management. While previous studies mainly focused on predicting site-scale ETa, in-depth studies on regional-scale ETa are relatively scarce. This study aims to address this issue by proposing a MulSA-ConvLSTM model, which combines the multi-headed self-attention module with the Pyramidally Attended Feature Extraction (PAFE) method. By extracting feature information and spatial dependencies in various dimensions and scales, the model utilizes remote sensing data from ERA5-Land and TerraClimate to attain regional-scale ETa prediction in Shandong, China. The MulSA-ConvLSTM model enhances the efficiency of capturing the trend of ETa successfully, and the prediction results are more accurate than those of the other contrast models. The Pearson\u2019s correlation coefficient between observed and predicted values reaches 0.908. The study has demonstrated that MulSA-ConvLSTM yields superior performance in forecasting various ETa scenarios and is more responsive to climatic changes than other contrast models. By using a convolutional network feature extraction method, the PAFE method extracts global features via various convolutional kernels. The customized MulSAM module allows the model to concentrate on data from distinct subspaces, focusing on feature changes in multiple directions. The block-based training method is employed for the large-scale regional ETa prediction, proving to be effective in mitigating the constraints posed by limited hardware resources. This research provides a novel and effective method for accurately predicting regional-scale ETa.<\/jats:p>","DOI":"10.3390\/rs16071235","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:28:00Z","timestamp":1711891680000},"page":"1235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of Large-Scale Regional Evapotranspiration Based on Multi-Scale Feature Extraction and Multi-Headed Self-Attention"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9818-3285","authenticated-orcid":false,"given":"Xin","family":"Zheng","sequence":"first","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Sha","family":"Zhang","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Shanshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6294-4131","authenticated-orcid":false,"given":"Jiaojiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Xianye","family":"Meng","sequence":"additional","affiliation":[{"name":"Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Yun","family":"Bai","sequence":"additional","affiliation":[{"name":"Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1016\/j.agwat.2008.03.009","article-title":"Integration of economic and hydrologic models: Exploring conjunctive irrigation water use strategies in the Volta Basin","volume":"95","author":"Bharati","year":"2008","journal-title":"Agric. 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