{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T18:43:09Z","timestamp":1782326589005,"version":"3.54.5"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T00:00:00Z","timestamp":1611273600000},"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 main objective of this study was to monitor wet snow conditions from Sentinel-1 over a season, to examine its variation over time by cross-checking wet snow with independent snow and weather estimates, and to study its distribution taking into account terrain characteristics such as elevation, orientation, and slope. One of our motivations was to derive useful representations of daily or seasonal snow changes that would help to easily identify wet snow elevations and determine melt-out days in an area of interest. In this work, a well-known approach in the literature is used to estimate the extent of wet snow cover continuously over a season and an analysis of the influence of complex mountain topography on snow distribution is proposed taking into account altitude, slope, and aspect of the terrain. The Sentinel-1 wet snow extent product was compared with Sentinel-2 snow products for cloud free scenes. We show that while there are good agreements between the two satellite products, differences exist, especially in areas of forests and glaciers where snow is underestimated. This underestimation must be considered alongside the areas of geometric distortion that were excluded from our study. We analysed retrievals at the scale of our study area by examining wet snow Altitude\u2013Orientation diagrams for different classes of slopes and also wet snow Altitude\u2013Time diagrams for different classes of orientations. We have shown that this type of representation is very useful to get an overview of the snow distribution as it allows to identify very easily wet snow lines for different orientations. For an orientation of interest, the Altitude\u2013Time diagrams can be used to track the evolution of snow to locate altitudes and dates of snow loss. We also show that ascending\/descending Sentinel-1 image time series are complementary to monitor wet snow over the French alpine areas to highlight wet snow altitude ranges and identify melt-out days. Links have also been made between Sentinel-1 responses (wet snow) and snow\/meteorological events carefully listed over the entire 2017\u20132018 season.<\/jats:p>","DOI":"10.3390\/rs13030381","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T11:13:53Z","timestamp":1611314033000},"page":"381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3499-2557","authenticated-orcid":false,"given":"Fatima","family":"Karbou","sequence":"first","affiliation":[{"name":"Univ. Grenoble Alpes, Universit\u00e9 de Toulouse, M\u00e9t\u00e9o-France, CNRS, CNRM, Centre d\u2019\u00c9tudes de la Neige, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8760-1017","authenticated-orcid":false,"given":"Ga\u00eblle","family":"Veyssi\u00e8re","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, Universit\u00e9 de Toulouse, M\u00e9t\u00e9o-France, CNRS, CNRM, Centre d\u2019\u00c9tudes de la Neige, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9cile","family":"Coleou","sequence":"additional","affiliation":[{"name":"M\u00e9t\u00e9o-France, DirOP, Cellule Montagne Nivologie, 38400 Saint Martin d\u2019Heres, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Dufour","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, Universit\u00e9 de Toulouse, M\u00e9t\u00e9o-France, CNRS, CNRM, Centre d\u2019\u00c9tudes de la Neige, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1801-684X","authenticated-orcid":false,"given":"Isabelle","family":"Gouttevin","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, Universit\u00e9 de Toulouse, M\u00e9t\u00e9o-France, CNRS, CNRM, Centre d\u2019\u00c9tudes de la Neige, 38000 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Durand","sequence":"additional","affiliation":[{"name":"Centre Nat. d\u2019Etudes Spatiales, 31400 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4996-6768","authenticated-orcid":false,"given":"Simon","family":"Gascoin","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Toulouse, CNRS\/CNES\/IRD\/INRA\/UPS, 31400 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Grizonnet","sequence":"additional","affiliation":[{"name":"Centre Nat. d\u2019Etudes Spatiales, 31400 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.coldregions.2017.09.013","article-title":"On forecasting wet-snow avalanche activity using simulated snow cover data","volume":"144","author":"Bellaire","year":"2017","journal-title":"Cold Reg. 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