{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:34:28Z","timestamp":1776335668059,"version":"3.51.2"},"reference-count":53,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["42075142"],"award-info":[{"award-number":["42075142"]}]},{"name":"National Science Foundation of China","award":["24ZDYF0017"],"award-info":[{"award-number":["24ZDYF0017"]}]},{"name":"National Science Foundation of China","award":["FY-APP-2022.0609"],"award-info":[{"award-number":["FY-APP-2022.0609"]}]},{"name":"National Science Foundation of China","award":["KYTD202330"],"award-info":[{"award-number":["KYTD202330"]}]},{"name":"Sichuan Science and Technology program","award":["42075142"],"award-info":[{"award-number":["42075142"]}]},{"name":"Sichuan Science and Technology program","award":["24ZDYF0017"],"award-info":[{"award-number":["24ZDYF0017"]}]},{"name":"Sichuan Science and Technology program","award":["FY-APP-2022.0609"],"award-info":[{"award-number":["FY-APP-2022.0609"]}]},{"name":"Sichuan Science and Technology program","award":["KYTD202330"],"award-info":[{"award-number":["KYTD202330"]}]},{"name":"China Meteorological Administration through the FengYun Application Pioneering Project","award":["42075142"],"award-info":[{"award-number":["42075142"]}]},{"name":"China Meteorological Administration through the FengYun Application Pioneering Project","award":["24ZDYF0017"],"award-info":[{"award-number":["24ZDYF0017"]}]},{"name":"China Meteorological Administration through the FengYun Application Pioneering Project","award":["FY-APP-2022.0609"],"award-info":[{"award-number":["FY-APP-2022.0609"]}]},{"name":"China Meteorological Administration through the FengYun Application Pioneering Project","award":["KYTD202330"],"award-info":[{"award-number":["KYTD202330"]}]},{"name":"CUIT Science and Technology Innovation Capacity Enhancement Program Project","award":["42075142"],"award-info":[{"award-number":["42075142"]}]},{"name":"CUIT Science and Technology Innovation Capacity Enhancement Program Project","award":["24ZDYF0017"],"award-info":[{"award-number":["24ZDYF0017"]}]},{"name":"CUIT Science and Technology Innovation Capacity Enhancement Program Project","award":["FY-APP-2022.0609"],"award-info":[{"award-number":["FY-APP-2022.0609"]}]},{"name":"CUIT Science and Technology Innovation Capacity Enhancement Program Project","award":["KYTD202330"],"award-info":[{"award-number":["KYTD202330"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network\u2019s perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson\u2019s correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.<\/jats:p>","DOI":"10.3390\/s24185972","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T11:36:37Z","timestamp":1726486597000},"page":"5972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Jiaqi","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1080\/014311699212885","article-title":"Estimating surface air temperatures, from Meteosat land surface temperatures, using an empirical solar zenith angle model","volume":"20","author":"Cresswell","year":"1999","journal-title":"INT J. 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