{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T14:31:46Z","timestamp":1790778706252,"version":"4.1.0"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T00:00:00Z","timestamp":1524009600000},"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>The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI) and spectral unmixing (SU). These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product) for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow) at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates), the Pyrenees (30 dates), and the Moroccan Atlas (24 dates). This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version of MOD10A1 (Collection 6) compared to the older version (Collection 5) are significant. Products based on SU provide a good alternative and more accurate retrieval of the snow fraction where wider ranges of land covers are concerned. The fusion process and its resulting 250 m spatial resolution product improve snow line retrieval. False detection in mixed pixels, probably due to the spectral variability associated with the various materials in the spectral mixture, has been identified as an area that will require improvement.<\/jats:p>","DOI":"10.3390\/rs10040619","type":"journal-article","created":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T03:51:13Z","timestamp":1524023473000},"page":"619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7028-3606","authenticated-orcid":false,"given":"Th\u00e9o","family":"Masson","sequence":"first","affiliation":[{"name":"Institute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie","family":"Dumont","sequence":"additional","affiliation":[{"name":"M\u00e9t\u00e9o-France, CNRS, Centre National de Recherches M\u00e9T\u00e9Orologiques\/Centre D\u2019\u00e9Tudes de la neige (CNRM\/CEN), F-38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9656-9087","authenticated-orcid":false,"given":"Mauro Dalla","family":"Mura","sequence":"additional","affiliation":[{"name":"Institute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Sirguey","sequence":"additional","affiliation":[{"name":"National School of Surveying, University Otago, Dunedin 9054, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4996-6768","authenticated-orcid":false,"given":"Simon","family":"Gascoin","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRA\/IRD\/UPS, 31401 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Pierre","family":"Dedieu","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, IRD, Institut des G\u00e9osciences de l\u2019Environnement (IGE), F-38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-2875","authenticated-orcid":false,"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[{"name":"Institute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1029\/RG020i001p00067","article-title":"Optical properties of snow","volume":"20","author":"Warren","year":"1982","journal-title":"Rev. Geophys."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/36.700993","article-title":"Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1","volume":"36","author":"Barnes","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/36.701075","article-title":"The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research","volume":"36","author":"Justice","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","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_7","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_8","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_9","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_10","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_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hapke, B. (2012). Theory of Reflectance and Emittance Spectroscopy, Cambridge University Press.","DOI":"10.1017\/CBO9781139025683"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/LGRS.2007.908884","article-title":"Improving MODIS spatial resolution for snow mapping using wavelet fusion and ARSIS concept","volume":"5","author":"Sirguey","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","first-page":"49","article-title":"Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation","volume":"66","author":"Ranchin","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1080\/02757250009532394","article-title":"Progress in bidirectional reflectance modeling and applications for surface particulate media: Snow and soils","volume":"18","author":"Nolin","year":"2000","journal-title":"Remote Sens. Rev."},{"key":"ref_19","first-page":"1","article-title":"Overview of NASA\u2019s MODIS and VIIRS Snow-Cover Earth System Data Records","volume":"2017","author":"Riggs","year":"2017","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Feranec, J., Soukup, T., Hazeu, G., and Jaffrain, G. (2016). European Landscape Dynamics: CORINE Land Cover Data, CRC Press.","DOI":"10.1201\/9781315372860"},{"key":"ref_21","unstructured":"Channan, S., Collins, K., and Emanuel, W. (2014). Global Mosaics of the Standard MODIS Land Cover Type Data, University of Maryland and the Pacific Northwest National Laboratory."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/JSTARS.2009.2039698","article-title":"On the relationship between temperature and MODIS snow cover retrieval errors in the Western US","volume":"3","author":"Dong","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.5194\/hess-19-2337-2015","article-title":"A snow cover climatology for the Pyrenees from MODIS snow products","volume":"19","author":"Gascoin","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","article-title":"Comparison and evaluation of methods for liver segmentation from CT datasets","volume":"28","author":"Heimann","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iordache, M.D., Plaza, A., and Bioucas-Dias, J. (2010, January 14\u201316). On the use of spectral libraries to perform sparse unmixing of hyperspectral data. Proceedings of the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594888"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/JSTARS.2011.2181340","article-title":"Automated extraction of image-based endmember bundles for improved spectral unmixing","volume":"5","author":"Somers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MSP.2013.2279177","article-title":"Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing","volume":"31","author":"Zare","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/LGRS.2010.2046134","article-title":"A novel approach based on fisher discriminant null space for decomposition of mixed pixels in hyperspectral imagery","volume":"7","author":"Jin","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/JSTSP.2010.2088377","article-title":"Non-linear spectral unmixing by geodesic simplex volume maximization","volume":"5","author":"Heylen","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TSP.2015.2486746","article-title":"Hyperspectral unmixing with spectral variability using a perturbed linear mixing model","volume":"64","author":"Thouvenin","year":"2016","journal-title":"IEEE Trans. Signal Proc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Drumetz, L., Henrot, S., Veganzones, M.A., Chanussot, J., and Jutten, C. (2015, January 2\u20135). Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability. Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2015), Tokyo, Japan.","DOI":"10.1109\/WHISPERS.2015.8075417"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/619\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:09Z","timestamp":1760194869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,18]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040619"],"URL":"https:\/\/doi.org\/10.3390\/rs10040619","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,18]]}}}