{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:10:29Z","timestamp":1780938629682,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T00:00:00Z","timestamp":1676764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72174031"],"award-info":[{"award-number":["72174031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["YS202004"],"award-info":[{"award-number":["YS202004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Youth Scholar Program of Beijing Academy of Science and Technology","award":["72174031"],"award-info":[{"award-number":["72174031"]}]},{"name":"Youth Scholar Program of Beijing Academy of Science and Technology","award":["YS202004"],"award-info":[{"award-number":["YS202004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process regression models by combining MODIS data, DEM data, and meteorological station data to estimate the NLSAT in China\u2019s mainland and compare them with actual NLSAT observations. The results show that there is a significant correlation between the model estimates and the actual temperature observations. Among the tested models, the random forest performed the best, followed by the support vector machine and the Gaussian process regression, then the neural network, the long short-term memory, and the bi-directional long short-term memory models. Overall, for estimates in different seasons, the best results were obtained in winter, followed by spring, autumn, and summer successively. According to different geographic areas, random forest was the best model for Northeast, Northwest, North, Southwest, and Central China, and the support vector machine was the best model for South and East China.<\/jats:p>","DOI":"10.3390\/rs15041136","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T01:36:37Z","timestamp":1676856997000},"page":"1136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Comparative Study of Several Popular Models for Near-Land Surface Air Temperature Estimation"],"prefix":"10.3390","volume":"15","author":[{"given":"Dewei","family":"Yang","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaobo","family":"Zhong","sequence":"additional","affiliation":[{"name":"Institute of Urban Systems Engineering, Beijing Academy of Science and Technology, Beijing 100035, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Mei","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlan","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Niu","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqi","family":"Zhong","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,19]]},"reference":[{"key":"ref_1","first-page":"540","article-title":"A Review on Air Temperature Estimation by Satellite Thermal Infrared Remote Sensing","volume":"29","author":"Zhang","year":"2014","journal-title":"J. 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