{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:34Z","timestamp":1787029534051,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T00:00:00Z","timestamp":1433203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Long-term snow depth\/snow water equivalent (SWE) products derived from passive microwave remote sensing data are fundamental for climatological and hydrological studies. However, the temporal continuity of the products is affected by the updating or replacement of passive microwave sensors or satellite platforms. In this study, we inter-calibrated brightness temperature (Tb) data obtained from the Special Sensor Microwave Imager (SSM\/I) and the Special Sensor Microwave Imager\/Sounder (SSMI\/S). Then, we evaluated the consistency of the snow cover area (SCA) and snow depth derived from the Scanning Multichannel Microwave Radiometer (SMMR), SSM\/I and SSMI\/S. The results indicated that (1) the spatial pattern of the SCA derived from the SMMR and SSM\/I data was more consistent after calibration than before; (2) the relative biases in the SCA and snow depth in China between the SSM\/I and SSMI\/S data decreased from 42.42% to 1.65% and from 66.18% to \u22121.5%, respectively; and (3) the SCA and snow depth derived from the SSM\/I data carried on F08, F11 and F13 were highly consistent. To obtain consistent snow depth and SCA products, inter-sensor calibrations between SMMR, SSM\/I and SSMI\/S are important. In consideration of the snow data product continuation, we suggest that the brightness temperature data from all sensors be calibrated based on SSMI\/S.<\/jats:p>","DOI":"10.3390\/rs70607212","type":"journal-article","created":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T11:38:36Z","timestamp":1433245116000},"page":"7212-7230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":152,"title":["Inter-Calibrating SMMR, SSM\/I and SSMI\/S Data to Improve the Consistency of Snow-Depth Products in China"],"prefix":"10.3390","volume":"7","author":[{"given":"Liyun","family":"Dai","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Che","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences,  Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7237-5579","authenticated-orcid":false,"given":"Yongjian","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1175\/JCLI3694.1","article-title":"Snow cover distribution, variability, and response to climate change in Western China","volume":"19","author":"Qin","year":"2006","journal-title":"J. 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