{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T09:31:12Z","timestamp":1777109472369,"version":"3.51.4"},"reference-count":76,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Second Tibetan Plateau Scientific Expedition and Research (STEP) Program","award":["2019QZKK010206\u201302"],"award-info":[{"award-number":["2019QZKK010206\u201302"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research (STEP) Program","award":["XJU2022BS056"],"award-info":[{"award-number":["XJU2022BS056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The spatial and temporal resolution of remote sensing products in land surface temperature (LST) studies can be improved using the downscaling method. This is a crucial area of research as it provides basic data for the study of climate change. However, there have been few studies evaluating the applicability of downscaling methods using underlying surfaces of varying complexities. In this study, we focused on the semi\u2013homogeneous underlying surface of Gurbantunggut Desert and evaluated the applicability of five classical, passive microwave, downscaling methods based on the machine learning of Catboost, using 365 days of AMSR\u20132 and MODIS data in 2019, which can be scanned once during the day and night. Our results showed four main points: (1) The correlation coefficients between feature vectors and the LST of the semi\u2013homogeneous underlying surface were clearly different from those of the surrounding oases. The correlation coefficient of the semi\u2013homogeneous underlying surface was high, and that of the surrounding oases was low. (2) At the same frequency, the correlation coefficient between vertically polarized BT and LST was greater than that between horizontally polarized BT and LST. Considering the semi\u2013heterogeneous underlying surface, 23.8 GHz and 36.5 GHz may be more suitable for passive microwave LST retrieval than 89 GHz according to physical mechanisms. (3) The fine\u2013scale LST downscaling accuracy achieved with all BT channels of AMSR\u20132 was higher than that achieved with the other four classical models. The day and night RMSE values verified with MYD11A1 data were 2.82 K and 1.38 K, respectively. (4) The correlation coefficients between downscaled LST and the soil temperature of the top layer of the site were the highest, with daytime\u2013nighttime R2 values of 0.978 and 0.970, and RMSE values of 3.42 and 4.99 K, respectively. The all\u2013channel\u2013based LST downscaling method is very effective and can provide a theoretical foundation for the acquisition of all\u2013weather, multi\u2013layer soil temperature.<\/jats:p>","DOI":"10.3390\/rs15102626","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T06:32:58Z","timestamp":1684391578000},"page":"2626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Applicability Assessment of Passive Microwave LST Downscaling over Semi\u2013Homogeneous Desert Underlying Surface Based on Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7059-368X","authenticated-orcid":false,"given":"Yongkang","family":"Li","sequence":"first","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China"},{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"},{"name":"Field Scientific Experiment Base of Akedala Atmospheric Background Station, China Meteorological Administration, Altay 836500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5763-2656","authenticated-orcid":false,"given":"Yang","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Resaurces and Environment, Xinjiang Agricultural Unversity, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5851-7569","authenticated-orcid":false,"given":"Jiao","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"},{"name":"Field Scientific Experiment Base of Akedala Atmospheric Background Station, China Meteorological Administration, Altay 836500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1080\/01431169408954338","article-title":"The 1 km resolution global data set: Needs of the International Geosphere Biosphere Programme","volume":"15","author":"Townshend","year":"1994","journal-title":"Int. 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