{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:56:59Z","timestamp":1782575819712,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"advanced research on civil space technology during the 14th Five-Year Plan","award":["NMICJY202305"],"award-info":[{"award-number":["NMICJY202305"]}]},{"name":"advanced research on civil space technology during the 14th Five-Year Plan","award":["202302035765"],"award-info":[{"award-number":["202302035765"]}]},{"name":"advanced research on civil space technology during the 14th Five-Year Plan","award":["91437105"],"award-info":[{"award-number":["91437105"]}]},{"name":"advanced research on civil space technology during the 14th Five-Year Plan","award":["92037000"],"award-info":[{"award-number":["92037000"]}]},{"name":"advanced research on civil space technology during the 14th Five-Year Plan","award":["42205153"],"award-info":[{"award-number":["42205153"]}]},{"name":"National Meteorological Information Center of China Meteorological","award":["NMICJY202305"],"award-info":[{"award-number":["NMICJY202305"]}]},{"name":"National Meteorological Information Center of China Meteorological","award":["202302035765"],"award-info":[{"award-number":["202302035765"]}]},{"name":"National Meteorological Information Center of China Meteorological","award":["91437105"],"award-info":[{"award-number":["91437105"]}]},{"name":"National Meteorological Information Center of China Meteorological","award":["92037000"],"award-info":[{"award-number":["92037000"]}]},{"name":"National Meteorological Information Center of China Meteorological","award":["42205153"],"award-info":[{"award-number":["42205153"]}]},{"name":"GHFUND C","award":["NMICJY202305"],"award-info":[{"award-number":["NMICJY202305"]}]},{"name":"GHFUND C","award":["202302035765"],"award-info":[{"award-number":["202302035765"]}]},{"name":"GHFUND C","award":["91437105"],"award-info":[{"award-number":["91437105"]}]},{"name":"GHFUND C","award":["92037000"],"award-info":[{"award-number":["92037000"]}]},{"name":"GHFUND C","award":["42205153"],"award-info":[{"award-number":["42205153"]}]},{"name":"National Nature Science Foundation of China","award":["NMICJY202305"],"award-info":[{"award-number":["NMICJY202305"]}]},{"name":"National Nature Science Foundation of China","award":["202302035765"],"award-info":[{"award-number":["202302035765"]}]},{"name":"National Nature Science Foundation of China","award":["91437105"],"award-info":[{"award-number":["91437105"]}]},{"name":"National Nature Science Foundation of China","award":["92037000"],"award-info":[{"award-number":["92037000"]}]},{"name":"National Nature Science Foundation of China","award":["42205153"],"award-info":[{"award-number":["42205153"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle\u2013nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05\u00b0 to 0.01\u00b0 for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 \u00b0C\/0.12 \u00b0C\/0.72 \u00b0C, BIAS of \u22120.02 \u00b0C\/0.02 \u00b0C\/0.002 \u00b0C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model\u2019s feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements.<\/jats:p>","DOI":"10.3390\/rs15215084","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T11:34:47Z","timestamp":1698147287000},"page":"5084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Spatial Downscaling of Near-Surface Air Temperature Based on Deep Learning Cross-Attention Mechanism"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1318-0057","authenticated-orcid":false,"given":"Zhanfei","family":"Shen","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiang","family":"Shi","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2496-0295","authenticated-orcid":false,"given":"Runping","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruian","family":"Tie","sequence":"additional","affiliation":[{"name":"Institute of Aerospace Information, Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingling","family":"Ge","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1007\/s13351-019-9042-9","article-title":"Development and evaluation of hourly and kilometer resolution retrospective and real-time surface meteorological blended forcing dataset (SMBFD) in China","volume":"33","author":"Han","year":"2019","journal-title":"J. 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