{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:39:53Z","timestamp":1775709593887,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFC3200303"],"award-info":[{"award-number":["2021YFC3200303"]}]},{"name":"National Key Research and Development Program of China","award":["52039004"],"award-info":[{"award-number":["52039004"]}]},{"name":"National Key Research and Development Program of China","award":["51979113"],"award-info":[{"award-number":["51979113"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFC3200303"],"award-info":[{"award-number":["2021YFC3200303"]}]},{"name":"National Natural Science Foundation of China","award":["52039004"],"award-info":[{"award-number":["52039004"]}]},{"name":"National Natural Science Foundation of China","award":["51979113"],"award-info":[{"award-number":["51979113"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable precipitation data are essential for studying water cycle patterns and climate change. However, there are always temporal or spatial errors in precipitation data from various sources. Most precipitation fusion methods are influenced by high-dimensional input features and do not make good use of the spatial correlation between precipitation and environmental variables. Thus, this study proposed a novel multi-source precipitation spatiotemporal fusion method for improving the spatiotemporal accuracy of precipitation. Specifically, the attention mechanism was used to first select critical input information to dimensionalize the inputs, and the Convolutional long-short-term memory network (ConvLSTM) was used to merge precipitation products and environmental variables spatiotemporally. The Yalong River in the southeastern part of the Tibetan Plateau was used as the case study area. The results show that: (1) Compared with the original precipitation products (IMERG, ERA5 and CHIRPS), the proposed method has optimal accuracy and good robustness, and its correlation coefficient (CC) reaches 0.853, its root mean square coefficient (RMSE) decreases to 3.53 mm\/d and its mean absolute error (MAE) decreases to 1.33 mm\/d. (2) The proposed method can reduce errors under different precipitation intensities and greatly improve the detection capability for strong precipitation. (3) The merged precipitation generated by the proposed method can be used to describe the rainfall\u2013runoff relationship and has good applicability. The proposed method may greatly improve the spatiotemporal accuracy of precipitation in complex terrain areas, which is important for scientific management and the allocation of water resources.<\/jats:p>","DOI":"10.3390\/rs15174160","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"4160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4205-0337","authenticated-orcid":false,"given":"Wei","family":"Fang","sequence":"first","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-0015","authenticated-orcid":false,"given":"Hui","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Guanjun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Zhanxing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China"}]},{"given":"Benjun","family":"Jia","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China"}]},{"given":"Qianyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127707","DOI":"10.1016\/j.jhydrol.2022.127707","article-title":"Optimally Integrating Multi-Source Products for Improving Long Series Precipitation Precision by Using Machine Learning Methods","volume":"609","author":"Zhao","year":"2022","journal-title":"J. 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