{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:29:30Z","timestamp":1760232570580,"version":"build-2065373602"},"reference-count":96,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171121","42271145","41901088","42101120","GCMAC2206"],"award-info":[{"award-number":["42171121","42271145","41901088","42101120","GCMAC2206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Oceanic Atmospheric Chemistry and Global Change, Ministry of Natural Resources","award":["42171121","42271145","41901088","42101120","GCMAC2206"],"award-info":[{"award-number":["42171121","42271145","41901088","42101120","GCMAC2206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods\u2014neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)\u2014are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m \u00d7 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 \u00b0C, and BIAS of \u22120.36 \u00b0C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects.<\/jats:p>","DOI":"10.3390\/rs14225775","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1698-5679","authenticated-orcid":false,"given":"Jiahang","family":"Che","sequence":"first","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"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, China Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Qinglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"given":"Yetang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"given":"Weijun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3564-6787","authenticated-orcid":false,"given":"Yuzhe","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4742-7127","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"given":"Baojuan","family":"Huai","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s10584-005-9017-y","article-title":"The Arctic Amplification Debate","volume":"76","author":"Serreze","year":"2006","journal-title":"Clim. 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