{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:49:20Z","timestamp":1775472560360,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,27]],"date-time":"2018-03-27T00:00:00Z","timestamp":1522108800000},"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>Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody\u2019s algorithm, the South China algorithm, Kelly\u2019s algorithm, Singh\u2019s algorithm, Hall\u2019s algorithm and Neal\u2019s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh\u2019s algorithm, Hall\u2019s algorithm and Neal\u2019s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody\u2019s algorithm, the South China algorithm and Kelly\u2019s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody\u2019s algorithm, the South China algorithm and Kelly\u2019s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD &lt; 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms.<\/jats:p>","DOI":"10.3390\/rs10040524","type":"journal-article","created":{"date-parts":[[2018,3,27]],"date-time":"2018-03-27T12:17:24Z","timestamp":1522153044000},"page":"524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C\/MWRI Data in China"],"prefix":"10.3390","volume":"10","author":[{"given":"Xiaojing","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University and Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9847-9034","authenticated-orcid":false,"given":"Lingmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University and Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5930-3183","authenticated-orcid":false,"given":"Shengli","family":"Wu","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shirui","family":"Hao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University and Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1563-1388","authenticated-orcid":false,"given":"Gongxue","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University and Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University and Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liang, S. (2008). Advances in Land Remote Sensing: System, Modeling, Inversion and Application, Springer Science & Business Media.","DOI":"10.1007\/978-1-4020-6450-0_1"},{"key":"ref_2","first-page":"452","article-title":"Remote sensing of drought: Progress, challenges and opportunities","volume":"53","author":"AghaKouchak","year":"2015","journal-title":"Geophys. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.asr.2011.12.021","article-title":"A review of global satellite-derived snow products","volume":"50","author":"Frei","year":"2012","journal-title":"Adv. Space Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4094","DOI":"10.1080\/01431161.2011.640964","article-title":"Remote sensing of snow\u2014A review of available methods","volume":"33","author":"Dietz","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1002\/(SICI)1097-0088(19991130)19:14<1535::AID-JOC438>3.0.CO;2-J","article-title":"Northern hemisphere snow extent: Regional variability 1972\u20131994","volume":"19","author":"Frei","year":"1999","journal-title":"J. Clim."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0034-4257(89)90101-6","article-title":"Spectral signature of alpine snow cover from the landsat thematic mapper","volume":"28","author":"Dozier","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0034-4257(02)00095-0","article-title":"MODIS snow-cover products","volume":"83","author":"Hall","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/j.rse.2009.01.001","article-title":"Retrieval of subpixel snow covered area, grain size, and albedo from MODIS","volume":"113","author":"Painter","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"6","article-title":"An automatic algorithm on estimating sub-pixel snow cover from MODIS","volume":"32","author":"Shi","year":"2012","journal-title":"Quat. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/36.7716","article-title":"Surface identification using satellite microwave radiometers","volume":"26","author":"Grody","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2016.05.010","article-title":"Global snow cover estimation with microwave brightness temperature measurements and one-class in situ observations","volume":"182","author":"Xu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","unstructured":"Kongoli, C., Romanov, P., and Ferraro, R. (2012). Snow cover monitoring from remote sensing satellites: Possibilities for drought assessment. Remote Sensing of Drought: Innovative Monitoring Approaches, CRC Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s11430-015-5225-0","article-title":"Review of snow water equivalent microwave remote sensing","volume":"59","author":"Shi","year":"2016","journal-title":"Sci. China Earth Sci."},{"key":"ref_14","first-page":"307","article-title":"The AMSR-E snow depth algorithm: Description and initial results","volume":"29","author":"Kelly","year":"2009","journal-title":"J. Remote Sens."},{"key":"ref_15","first-page":"12","article-title":"Snow cover identification with SSM\/I data in china","volume":"18","author":"Li","year":"2007","journal-title":"J. Appl. Meteorol. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/36.481908","article-title":"Global identification of snowcover using SSM\/I measurements","volume":"34","author":"Grody","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pan, J., Jiang, L., and Zhang, L. (2012, January 22\u201327). In Wet snow detection in the south of china by passive microwave remote sensing. Proceedings of the 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352523"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/S0034-4257(00)00121-8","article-title":"Retrieval of snow water equivalent using passive microwave brightness temperature data","volume":"74","author":"Singh","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3189\/172756402781817770","article-title":"Assessment of the relative accuracy of hemispheric-scale snow-cover maps","volume":"34","author":"Hall","year":"2002","journal-title":"Ann. Glaciol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/36.58970","article-title":"Land-surface-type classification using microwave brightness temperatures from the special sensor microwave\/imager","volume":"28","author":"Neale","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3750","DOI":"10.1016\/j.rse.2008.05.010","article-title":"Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements","volume":"112","author":"Liang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1002\/(SICI)1099-1085(199808\/09)12:10\/11<1537::AID-HYP679>3.0.CO;2-A","article-title":"The interactive multisensor snow and ice mapping system","volume":"12","author":"Ramsay","year":"1998","journal-title":"Hydrol. Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1002\/hyp.6720","article-title":"Enhancements to, and Forthcoming Developments in the Interactive Multisensor Snow and Ice Mapping System (IMS)","volume":"21","author":"Helfrich","year":"2007","journal-title":"Hydrol. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2017.04.023","article-title":"Global multisensor automated satellite-based snow and ice mapping system (GMASI) for cryosphere monitoring","volume":"196","author":"Romanov","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Luojus, K., Pulliainen, J., Takala, M., Kangwa, M., Smolander, T., Wiesmann, A., Derksen, C., Metsamaki, S., Salminen, M., and Solberg, R. (2013). Globsnow-2 Product User Guide Version 1.0, ESA\/ESRIN. European Space Agency Study Contract Report, ESRIN Contract 21703\/08\/I-EC."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1007\/s11430-013-4798-8","article-title":"Improvement of snow depth retrieval for fy3b-mwri in china","volume":"57","author":"Jiang","year":"2014","journal-title":"Sci. China Earth Sci."},{"key":"ref_27","unstructured":"Maeda, T., and Taniguchi, Y. (2013). Descriptions of GCOM-W1 AMSR-2 Level 1R and Level 2 Algorithms, Japan Aerospace Exploration Agency Earth Observation Research Center."},{"key":"ref_28","unstructured":"Chang, A.T., and Rango, A. (2000). Algorithm Theoretical Basis Document (ATBD) for the AMSR-E Snow Water Equivalent Algorithm, NASA\/GSFC."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.rse.2017.01.023","article-title":"A 38-year (1978\u20132015) northern hemisphere daily snow cover extent product derived using consistent objective criteria from satellite-borne optical sensors","volume":"191","author":"Hori","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1175\/1520-0450(2000)039<1866:AMOSCO>2.0.CO;2","article-title":"Automated monitoring of snow cover over North America with multispectral satellite data","volume":"39","author":"Romanov","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, X., Jiang, L., Yang, J., and Pan, J. (2014). Validation of ice mapping system snow cover over southern china based on landsat enhanced thematic mapper plus imagery. J. Appl. Remote Sens., 8.","DOI":"10.1117\/1.JRS.8.084680"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.advwatres.2012.03.002","article-title":"Assessment of methods for mapping snow cover from MODIS","volume":"51","author":"Rittger","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1175\/JTECH-D-15-0100.1","article-title":"An in-depth evaluation of heritage algorithms for snow cover and snow depth using AMSR-E and AMSR2 measurements","volume":"32","author":"Lee","year":"2015","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1175\/2010JAMC2568.1","article-title":"New geostationary satellite-based snow-cover algorithm","volume":"50","author":"Siljamo","year":"2011","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7423","DOI":"10.1029\/91JD00045","article-title":"Classification of snow cover and precipitation using the special sensor microwave imager","volume":"96","author":"Grody","year":"1991","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1029\/RG022i002p00195","article-title":"An overview of passive microwave snow research and results","volume":"22","author":"Foster","year":"1984","journal-title":"Rev. Geophys. Space Phys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1175\/JHM447.1","article-title":"Evaluation and comparison of MODIS and IMS snow-cover estimates for the continental united states using station data","volume":"6","author":"Brubaker","year":"2005","journal-title":"J. Hydrometeorol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, H., Hao, X., Huang, X., Hou, J., Che, T., Dai, L., Liang, T., Huang, C., and Li, H. (2014). Remote sensing for snow hydrology in China: Challenges and perspectives. J. Appl. Remote Sens., 8.","DOI":"10.1117\/1.JRS.8.084687"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3247","DOI":"10.1002\/hyp.10427","article-title":"Evaluation of Snow Products over the Tibetan Plateau","volume":"29","author":"Yang","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/S0034-4257(03)00097-X","article-title":"Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000\u20132001 snow year","volume":"86","author":"Klein","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.advwatres.2016.05.015","article-title":"Spatial estimates of snow water equivalent from reconstruction","volume":"94","author":"Rittger","year":"2016","journal-title":"Adv. Water Resour."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1029\/2002JD003142","article-title":"Mapping and monitoring of the snow cover fraction over North America","volume":"108","author":"Romanov","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3723","DOI":"10.1002\/hyp.1231","article-title":"An assessment of the differences between three satellite snow cover mapping techniques","volume":"16","author":"Bitner","year":"2002","journal-title":"Hydrol. Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/s12040-013-0339-2","article-title":"Decision tree approach for classification of remotely sensed satellite data using open source support","volume":"122","author":"Sharma","year":"2013","journal-title":"J. Erath Syst. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pan, J. (2012). Study of the Snow Detection Decision Tree Algorithm Using Passive Microwave Remote Sensing Technology in the South of China. [Master\u2019s Thesis, Beijing Normal University].","DOI":"10.1109\/IGARSS.2012.6352523"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Walker, A.E., and Goodison, B.E. (1993). Discrimination of a wet snowcover using passive microwave satellite data. Ann. Glaclol., 17.","DOI":"10.3189\/S026030550001301X"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wu, S., and Chen, J. (2016, January 10\u201315). Instrument performance and cross calibration of FY-3C MWRI. Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729095"},{"key":"ref_48","unstructured":"Yang, J., Luojus, K., Lemmetyinen, J., Jiang, L., and Pulliainen, J. (2014, January 13\u201318). Comparison of SSMIS, AMSR-E and MWRI brightness temperature data. Proceedings of the Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, QC, Canada."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/524\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:58:39Z","timestamp":1760194719000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,27]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040524"],"URL":"https:\/\/doi.org\/10.3390\/rs10040524","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,27]]}}}