{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:57:38Z","timestamp":1773316658591,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5\u00b0 to 0.25\u00b0, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5\u00b0 to 0.05\u00b0 by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at \u221220.86 mm\/yr and \u221221.79 mm\/yr (\u221214.53 mm\/yr and \u221215.46 mm\/yr), respectively, and the Nash\u2013Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.<\/jats:p>","DOI":"10.3390\/rs13234760","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4760","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhiwei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"},{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"},{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"},{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"School of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Wenjie","family":"Yin","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Xiaoping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8848-887X","authenticated-orcid":false,"given":"Gangqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Centre, China Geological Survey, Tianjin 300170, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","first-page":"143","article-title":"Impact of water withdrawals from groundwater and surface water on continental water storage variations","volume":"59\u201360","author":"Portmann","year":"2012","journal-title":"J. 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