{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:47:45Z","timestamp":1768819665761,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory","award":["311021008"],"award-info":[{"award-number":["311021008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dome A is the summit of the Antarctic plateau, where the Chinese Kunlun inland station is located. Due to its unique location and high altitude, Dome A provides an important observatory site in analyzing global climate change. However, before the arrival of the Chinese Antarctic expedition in 2005, near-surface air temperatures had not been recorded in the region. In this study, we used meteorological parameters, such as ice surface temperature, radiation, wind speed, and cloud type, to build a reliable model for air temperature estimation. Three models (linear regression, random forest, and deep neural network) were developed based on various input datasets: seasonal factors, skin temperature, shortwave radiation, cloud type, longwave radiation from AVHRR-X products, and wind speed from MERRA-2 reanalysis data. In situ air temperatures from 2010 to 2015 were used for training, while 2005\u20132009 and 2016\u20132020 measurements were used for model validation. The results showed that random forest and deep neural network outperformed the linear regression model. In both methods, the 2005\u20132009 estimates (average bias = 0.86 \u00b0C and 1 \u00b0C) were more accurate than the 2016\u20132020 values (average bias = 1.04 \u00b0C and 1.26 \u00b0C). We conclude that the air temperature at Dome A can be accurately estimated (with an average bias less than 1.3 \u00b0C and RMSE around 3 \u00b0C) from meteorological parameters using random forest or a deep neural network.<\/jats:p>","DOI":"10.3390\/rs14041045","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:48:41Z","timestamp":1645476521000},"page":"1045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Application of Machine Learning for Simulation of Air Temperature at Dome A"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1065-7754","authenticated-orcid":false,"given":"Xiaoping","family":"Pang","sequence":"first","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2231-0611","authenticated-orcid":false,"given":"Chuang","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519000, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9044-2950","authenticated-orcid":false,"given":"Bin","family":"He","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Fan","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0036-917X","authenticated-orcid":false,"given":"Meng","family":"Qu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Polar Science of Ministry of Natural Resources, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-6598","authenticated-orcid":false,"given":"Minghu","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather and Institute of Polar Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s11430-010-0017-z","article-title":"A one-dimensional heat transfer model of the Antarctic Ice Sheet and modeling of snow temperatures at Dome A, the summit of Antarctic Plateau","volume":"53","author":"Chen","year":"2010","journal-title":"Sci. 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