{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T11:52:10Z","timestamp":1771501930324,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T00:00:00Z","timestamp":1605052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971325"],"award-info":[{"award-number":["41971325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801283"],"award-info":[{"award-number":["41801283"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science &amp; Technology Basic Resources Investigation Program of China","award":["2017FY100502"],"award-info":[{"award-number":["2017FY100502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial\u2013spectral\u2013environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.<\/jats:p>","DOI":"10.3390\/rs12223693","type":"journal-article","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T19:08:28Z","timestamp":1605121708000},"page":"3693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The Spatial\u2013Spectral\u2013Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8756-0104","authenticated-orcid":false,"given":"Hongyu","family":"Zhao","sequence":"first","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730070, China"}]},{"given":"Xiaohua","family":"Hao","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730070, China"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7583-6003","authenticated-orcid":false,"given":"Hongyi","family":"Li","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730070, China"}]},{"given":"Guanghui","family":"Huang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6681-3640","authenticated-orcid":false,"given":"Donghang","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1499-2728","authenticated-orcid":false,"given":"Bo","family":"Su","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"}]},{"given":"Huajin","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]},{"given":"Xiaojing","family":"Hu","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1002\/joc.3370050602","article-title":"Climatology of the terrestrial seasonal water cycle","volume":"5","author":"Willmott","year":"1985","journal-title":"J. Climatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17131","DOI":"10.1029\/97JD00201","article-title":"Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation","volume":"102","author":"Vermote","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1002\/(SICI)1099-1085(19991230)13:18<2977::AID-HYP12>3.0.CO;2-#","article-title":"CO2 in Arctic snow cover: Landscape form, in-pack gas concentration gradients, and the implications for the estimation of gaseous fluxes","volume":"13","author":"Jones","year":"1999","journal-title":"Hydrol. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1175\/1520-0450(1999)038<1474:IASDSA>2.0.CO;2","article-title":"Interrelationships among Snow Distribution, Snowmelt, and Snow Cover Depletion: Implications for Atmospheric, Hydrologic, and Ecologic Modeling","volume":"38","author":"Liston","year":"1999","journal-title":"J. Appl. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1007\/s00382-012-1545-3","article-title":"Alpine snow cover in a changing climate: A regional climate model perspective","volume":"41","author":"Steger","year":"2013","journal-title":"Clim. Dyn."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Su, B., Xiao, C., Chen, D., Qin, D., and Ding, Y. (2019). Cryosphere Services and Human Well-Being. Sustainability, 11.","DOI":"10.3390\/su11164365"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(95)00137-P","article-title":"Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data","volume":"54","author":"Hall","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19569","DOI":"10.1029\/1999JD900232","article-title":"A parameterization of snowpack and frozen ground intended for NCEP weather and climate models","volume":"104","author":"Koren","year":"1999","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"G\u00f6tzinger, J., and B\u00e1rdossy, A. (2008). Generic error model for calibration and uncertainty estimation of hydrological models. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006691"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/S0034-4257(03)00134-2","article-title":"Consideration of the errors inherent in mapping historical glacier positions in Austria from the ground and space (1893\u20132001)","volume":"86","author":"Hall","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1146\/annurev.earth.32.101802.120404","article-title":"Multispectral and hyperspectral remote sensing of alpine snow properties","volume":"32","author":"Dozier","year":"2004","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Parajka, J., and Bl\u00f6schl, G. (2008). Spatio-temporal combination of MODIS images\u2013potential for snow cover mapping. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006204"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/JSTARS.2018.2879666","article-title":"Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau","volume":"12","author":"Hao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.rse.2003.10.016","article-title":"Estimating fractional snow cover from MODIS using the normalized difference snow index","volume":"89","author":"Salomonson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2014.09.026","article-title":"Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network","volume":"156","author":"Hirschboeck","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.rse.2017.11.021","article-title":"Retrieval of fractional snow covered area from MODIS data by multi-variate adaptive regression splines","volume":"205","author":"Kuter","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"121","DOI":"10.3189\/S0260305500012702","article-title":"Mapping alpine snow using a spectral mixture modeling technique","volume":"17","author":"Nolin","year":"1993","journal-title":"Ann. Glaciol."},{"key":"ref_19","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_20","first-page":"251","article-title":"Subpixel snow mapping of the Qinghai\u2013Tibet Plateau using MODIS data","volume":"18","author":"Zhu","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","unstructured":"Appel, I.L., and Salomonson, V.V. (2002, January 24\u201328). Estimate of fractional snow cover using MODIS data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1109\/TGRS.2006.876029","article-title":"Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results","volume":"44","author":"Salomonson","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"423","article-title":"Accuracy Validation and Cloud Obscuration Removal of MODIS Fractional Snow Cover Products over Tibetan Plateau","volume":"28","author":"Tang","year":"2013","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1002\/hyp.10134","article-title":"MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+","volume":"29","author":"Crawford","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_25","first-page":"23","article-title":"Machine learning in manufacturing: Advantages, challenges, and applications","volume":"4","author":"Wuest","year":"2016","journal-title":"Prod. Manuf. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1016\/j.rse.2011.07.018","article-title":"Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance","volume":"115","author":"Dobreva","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Veganzones, M.A., and Grana, M. (2008, January 3\u20135). Endmember extraction methods: A short review. Knowledge-Based Intelligent Information and Engineering Systems. Proceedings of the 12th International Conference, KES 2008, Zagreb, Croatia."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1109\/TIP.2016.2579259","article-title":"Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability","volume":"25","author":"Drumetz","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.rse.2003.06.004","article-title":"Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data","volume":"88","author":"Vikhamar","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"737","DOI":"10.3189\/002214309789470996","article-title":"Indices for estimating fractional snow cover in the western Tibetan Plateau","volume":"55","author":"Shreve","year":"2009","journal-title":"J. Glaciol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0034-4257(02)00098-6","article-title":"Subpixel mapping of snow cover in forests by optical remote sensing","volume":"84","author":"Vikhamar","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S0034-4257(02)00187-6","article-title":"Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data","volume":"85","author":"Painter","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.rse.2008.09.008","article-title":"Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: Methodology and accuracy assessment","volume":"113","author":"Sirguey","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"Selkowitz, D. (2015, January 14\u201318). The USGS Landsat Snow Covered Area Science Data Products. Proceedings of the 2015 Fall Meeting Program, San Francisco, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Selkowitz, D., Forster, R., Hayes, D., Goswami, S., Grosse, G., Jones, B., Gloaguen, R., and Thenkabail, P. (2015). An Automated Approach for Mapping Persistent Ice and Snow Cover over High Latitude Regions. Remote Sens., 8.","DOI":"10.3390\/rs8010016"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.isprsjprs.2016.04.001","article-title":"Automated mapping of persistent ice and snow cover across the western U.S. with Landsat","volume":"117","author":"Selkowitz","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Liang, S. (2018). 4.06-Snow Cover Mapping. Comprehensive Remote Sensing, Elsevier.","DOI":"10.1016\/B978-0-12-409548-9.10625-6"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TGRS.2002.802494","article-title":"Spatial\/spectral endmember extraction by multidimensional morphological operations","volume":"40","author":"Plaza","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/LGRS.2011.2107877","article-title":"Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing","volume":"8","author":"Martin","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.rse.2011.01.019","article-title":"C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter","volume":"115","author":"Reese","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1002\/(SICI)1099-1085(199808\/09)12:10\/11<1723::AID-HYP691>3.0.CO;2-2","article-title":"Improving snow cover mapping in forests through the use of a canopy reflectance model","volume":"12","author":"Klein","year":"1998","journal-title":"Hydrol. Process."},{"key":"ref_44","first-page":"310","article-title":"Combination of NDSI and NDFSI for snow cover mapping in a mountainous and forested region","volume":"21","author":"Wang","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.rse.2007.02.019","article-title":"Integration of spatial\u2013spectral information for the improved extraction of endmembers","volume":"110","author":"Rogge","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1117\/1.1766301","article-title":"New hyperspectral discrimination measure for spectral characterization","volume":"43","author":"Yingzi","year":"2004","journal-title":"Opt. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259609532303","article-title":"A review of mixture modeling techniques for sub-pixel land cover estimation","volume":"13","author":"Ichoku","year":"1996","journal-title":"Remote Sens. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in Spectral Mixture Analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.034","article-title":"Incorporating spatial information in spectral unmixing: A review","volume":"149","author":"Shi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Masson, T., Dumont, M., Mura, M.D., Sirguey, P., Gascoin, S., Dedieu, J.-P., and Chanussot, J. (2018). An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10040619"},{"key":"ref_53","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:31:56Z","timestamp":1760178716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,11]]},"references-count":53,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223693"],"URL":"https:\/\/doi.org\/10.3390\/rs12223693","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,11]]}}}