{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T22:19:50Z","timestamp":1777587590653,"version":"3.51.4"},"reference-count":86,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T00:00:00Z","timestamp":1570579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"with the financial support of the Swiss Federal Office for the Environment (FOEN) who supports the Swiss Data Cube","award":["xxxxxx"],"award-info":[{"award-number":["xxxxxx"]}]},{"name":"by European Commission \u201cHorizon 2020 Program\u201d ECOPOTENTIALl project","award":["641762"],"award-info":[{"award-number":["641762"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One of the key variables to study is snow cover, since it represents an essential driver of many ecological, hydrological and socioeconomic processes in mountains. As remotely sensed data can contribute to filling the gap of sparse in-situ stations in high-altitude environments, a methodology for snow cover detection through time series analyses using Landsat satellite observations stored in an Open Data Cube is described in this paper, and applied to a case study on the Gran Paradiso National Park, in the western Italian Alps. In particular, this study presents a proof of concept of the preliminary version of the snow observation from space algorithm applied to Landsat data stored in the Swiss Data Cube. Implemented in an Earth Observation Data Cube environment, the algorithm can process a large amount of remote sensing data ready for analysis and can compile all Landsat series since 1984 into one single multi-sensor dataset. Temporal filtering methodology and multi-sensors analysis allows one to considerably reduce the uncertainty in the estimation of snow cover area using high-resolution sensors. The study highlights that, despite this methodology, the lack of available cloud-free images still represents a big issue for snow cover mapping from satellite data. Though accurate mapping of snow extent below cloud cover with optical sensors still represents a challenge, spatial and temporal filtering techniques and radar imagery for future time series analyses will likely allow one to reduce the current cloud cover issue.<\/jats:p>","DOI":"10.3390\/data4040138","type":"journal-article","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T11:25:57Z","timestamp":1570620357000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Snow Cover Evolution in the Gran Paradiso National Park, Italian Alps, Using the Earth Observation Data Cube"],"prefix":"10.3390","volume":"4","author":[{"given":"Charlotte","family":"Poussin","sequence":"first","affiliation":[{"name":"Institute for Environmental Sciences, University of Geneva, Bd Carl-Vogt 66, CH-1205 Geneva, Switzerland"},{"name":"Department of F.-A. Forel for Environment and Water Sciences, Faculty of Sciences, University of Geneva, Bd Carl-Vogt 66, CH-1205 Geneva, Switzerland"},{"name":"UNEP\/GRID-Geneva, 11 ch. des An\u00e9mones, CH-1219 Ch\u00e2telaine, Switzerland"}]},{"given":"Yaniss","family":"Guigoz","sequence":"additional","affiliation":[{"name":"Institute for Environmental Sciences, University of Geneva, Bd Carl-Vogt 66, CH-1205 Geneva, Switzerland"},{"name":"UNEP\/GRID-Geneva, 11 ch. des An\u00e9mones, CH-1219 Ch\u00e2telaine, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1683-5267","authenticated-orcid":false,"given":"Elisa","family":"Palazzi","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), corso Fiume 4, 10133 Torino, Italy"}]},{"given":"Silvia","family":"Terzago","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), corso Fiume 4, 10133 Torino, Italy"}]},{"given":"Bruno","family":"Chatenoux","sequence":"additional","affiliation":[{"name":"Institute for Environmental Sciences, University of Geneva, Bd Carl-Vogt 66, CH-1205 Geneva, Switzerland"},{"name":"UNEP\/GRID-Geneva, 11 ch. des An\u00e9mones, CH-1219 Ch\u00e2telaine, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-8865","authenticated-orcid":false,"given":"Gregory","family":"Giuliani","sequence":"additional","affiliation":[{"name":"Institute for Environmental Sciences, University of Geneva, Bd Carl-Vogt 66, CH-1205 Geneva, Switzerland"},{"name":"UNEP\/GRID-Geneva, 11 ch. des An\u00e9mones, CH-1219 Ch\u00e2telaine, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,9]]},"reference":[{"key":"ref_1","unstructured":"Core Writing Team, Pachauri, R.K., and Meyer, L.A. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC. 9291691437."},{"key":"ref_2","unstructured":"Masson-Delmotte, V., Zhai, H.-O.P., P\u00f6rtner, D., Roberts, J., Skea, P.R., Shukla, A., Pirani, W., Moufouma-Okia, C., P\u00e9an, R., and Pidcock, S. (2018). Global Warming of 1.5 \u00b0C: Summary for Policy Makers. Global Warming of 1.5 \u00b0C. An IPCC Special Report on the Impacts of Global Warming of 1.5 \u00b0C above Pre-Industrial Levels And Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty, IPCC."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mountain Research Initiative EDW Working Group, Pepin, N., Bradley, R.S., Diaz, H.F., Baraer, M., Caceres, E.B., Forsythe, N., Fowler, H., Greenwood, G., and Hashmi, M.Z. (2015). Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang., 5, 424.","DOI":"10.1038\/nclimate2563"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1007\/s00382-015-2692-0","article-title":"Variability in projected elevation dependent warming in boreal midlatitude winter in CMIP5 climate models and its potential drivers","volume":"46","author":"Rangwala","year":"2016","journal-title":"Clim. Dyn."},{"key":"ref_5","unstructured":"(2019, March 12). Global Climate Report for January 2019, Available online: https:\/\/www.ncdc.noaa.gov\/sotc\/global\/201901."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10584-017-1971-7","article-title":"Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level","volume":"143","author":"Donnelly","year":"2017","journal-title":"Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1175\/BAMS-D-13-00047.1","article-title":"The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy","volume":"95","author":"Bojinski","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Salzano, R., Salvatori, R., Valt, M., Giuliani, G., Chatenoux, B., and Ioppi, L. (2019). Automated Classification of Terrestrial Images: The Contribution to the Remote Sensing of Snow Cover. Geosciences, 9.","DOI":"10.3390\/geosciences9020097"},{"key":"ref_9","unstructured":"Hall, D.K., Riggs, G.A., Salomonson, V.V., Barton, J., Casey, K., Chien, J., DiGirolamo, N., Klein, A., Powell, H., and Tait, A.J.N.G. (2001). Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.3189\/002214311796406077","article-title":"Recent advances in remote sensing of seasonal snow","volume":"56","author":"Nolin","year":"2010","journal-title":"J. Glaciol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5463","DOI":"10.3390\/rs5115463","article-title":"A merging algorithm for regional snow mapping over eastern Canada from AVHRR and SSM\/I data","volume":"5","author":"Chokmani","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2712","DOI":"10.1016\/j.scitotenv.2018.10.128","article-title":"Ground-based evaluation of MODIS snow cover product V6 across China: Implications for the selection of NDSI threshold","volume":"651","author":"Zhang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_13","unstructured":"Barnes, J.C., and Smallwood, M.D. (1975). Synopsis of Current Satellite Snow Mapping Techniques, with Emphasis on the Application of Near-Infrared Data."},{"key":"ref_14","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_15","unstructured":"Hall, D.K. (1995). Satellite Snow-Cover Mapping: A Brief Review."},{"key":"ref_16","first-page":"1255","article-title":"Variations in snow cover and snowline altitude in Baspa Basin","volume":"96","author":"Kaur","year":"2009","journal-title":"Curr. Sci."},{"key":"ref_17","unstructured":"Lemke, P., Ren, J., Alley, R.B., Allison, I., Carrasco, J., Flato, G., Fujii, Y., Kaser, G., Mote, P., and Thomas, R.H. (2007). Observations: Changes in Snow, Ice and Frozen Ground, IPCC."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"355","DOI":"10.3189\/172756402781817941","article-title":"The new remote-sensing-derived Swiss glacier inventory: I. Methods","volume":"34","author":"Paul","year":"2002","journal-title":"Ann. Glaciol."},{"key":"ref_19","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 US with Landsat","volume":"117","author":"Selkowitz","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6529","DOI":"10.1080\/01431161.2013.803631","article-title":"Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery","volume":"34","author":"Yin","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Baumann, P., Misev, D., Merticariu, V., Huu, B.P., and Bell, B. (2018, January 22\u201327). Datacubes: A Technology Survey. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518920"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mathieu, P.-P., and Aubrecht, C. (2018). Fostering Cross-Disciplinary Earth Science Through Datacube Analytics. Earth Observation Open Science and Innovation, Springer International Publishing.","DOI":"10.1007\/978-3-319-65633-5"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Killough, B. (2018, January 22\u201327). Overview of the Open Data Cube Initiative. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517694"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2017.03.015","article-title":"The Australian Geoscience Data Cube\u2014Foundations and lessons learned","volume":"202","author":"Lewis","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Strobl, P., Baumann, P., Lewis, A., Szantoi, Z., Killough, B., Purss, M., Craglia, M., Nativi, S., Held, A., and Dhu, T. (2017, January 28\u201330). The Six Faces of The Datacube. Proceedings of the Conference on Big Data from Space (BIDS\u20192017), Toulouse, France."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/17538947.2014.1003106","article-title":"Big Data Analytics for Earth Sciences: The EarthServer approach","volume":"9","author":"Baumann","year":"2016","journal-title":"Int. J. Dig. Earth"},{"key":"ref_29","unstructured":"Camara, G., Assis, L.F., Ribeiro, G., Ferreira, K.R., Llapa, E., and Vinhas, L. (November, January 31). Big earth observation data analytics: Matching requirements to system architectures. Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Burlingame, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.future.2017.11.007","article-title":"A versatile data-intensive computing platform for information retrieval from big geospatial data","volume":"81","author":"Soille","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lehmann, A., Nativi, S., Mazzetti, P., Maso, J., Serral, I., Spengler, D., Niamir, A., McCallum, I., Lacroix, P., and Patias, P. (2019). GEOEssential\u2014Mainstreaming workflows from data sources to environment policy indicators with essential variables. Int. J. Dig. Earth, 1\u201317.","DOI":"10.1080\/17538947.2019.1585977"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sudmanns, M., Tiede, D., Lang, S., Bergstedt, H., Trost, G., Augustin, H., Baraldi, A., and Blaschke, T. (2019). Big Earth data: Disruptive changes in Earth observation data management and analysis?. Int. J. Dig. Earth, 1\u201319.","DOI":"10.1080\/17538947.2019.1585976"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","unstructured":"Hou, J., Huang, C., Zhang, Y., Guo, J., and Gu, J. (2019). Gap-Filling of Modis Fractional Snow Cover Products Via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques. Remote Sens., 11.","DOI":"10.3390\/rs11010090"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.5194\/hess-17-1809-2013","article-title":"Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach","volume":"17","author":"Gupta","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_37","unstructured":"(2019, May 31). Gran Paradiso National Park\u2014Italy. Available online: https:\/\/deims.org\/e33c983a-19ad-4f40-a6fd-1210ee0b3a4b."},{"key":"ref_38","unstructured":"(2019, May 15). Ecopotential Project. Available online: https:\/\/www.ecopotential-project.eu\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1002\/joc.1377","article-title":"HISTALP\u2014historical instrumental climatological surface time series of the Greater Alpine Region","volume":"27","author":"Auer","year":"2007","journal-title":"Int. J. Climatol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/asr-6-39-2011","article-title":"Development of a long-term dataset of solid\/liquid precipitation","volume":"6","author":"Chimani","year":"2011","journal-title":"Adv. Sci. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2013.04.004","article-title":"Multitemporal snow cover mapping in mountainous terrain for Landsat climate data record development","volume":"135","author":"Crawford","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1175\/JHM-D-16-0188.1","article-title":"Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps","volume":"18","author":"Marty","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1080\/20964471.2017.1402490","article-title":"Digital earth Australia\u2014Unlocking new value from earth observation data","volume":"1","author":"Dhu","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1080\/20964471.2017.1404232","article-title":"A view-based model of data-cube to support big earth data systems interoperability","volume":"1","author":"Nativi","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rizvi, S.R., Killough, B., Cherry, A., and Gowda, S. (2018, January 22\u201327). The Ceos Data Cube Portal: A User-Friendly, Open Source Software Solution for the Distribution, Exploration, Analysis, and Visualization of Analysis Ready Data. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518727"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Woodcock, R., Paget, M., Wang, P., and Held, A. (2018, January 22\u201327). Accelerating Industry Innovation Using the Open Data Cube in Australia. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519170"},{"key":"ref_47","unstructured":"(2019, May 31). Landsat Missions, Available online: https:\/\/www.usgs.gov\/land-resources\/nli\/landsat."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1080\/20964471.2017.1398903","article-title":"Building an Earth Observations Data Cube: Lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)","volume":"1","author":"Giuliani","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Giuliani, G., Chatenoux, B., Honeck, E., and Richard, J. (2018, January 22\u201327). Towards Sentinel-2 Analysis Ready Data: A Swiss Data Cube Perspective. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517954"},{"key":"ref_50","unstructured":"(2019, May 15). Sentinel-1 Satellites Observe Snow Melting Processes. Available online: https:\/\/earth.esa.int\/web\/sentinel\/missions\/sentinel-1\/news\/-\/article\/sentinel-1-satellites-observe-snow-melting-processes."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Nagler, T., Rott, H., Ossowska, J., Schwaizer, G., Small, D., Malnes, E., Luojus, K., Mets\u00e4m\u00e4ki, S., and Pinnock, S. (2018, January 22\u201327). Snow Cover Monitoring by Synergistic Use of Sentinel-3 Slstr and Sentinel-L Sar Data. Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518203"},{"key":"ref_52","unstructured":"(2019, July 15). Committee on Earth Observations Satellites (CEOS). Available online: http:\/\/ceos.org\/ard\/users.html."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Masek, J., Ju, J., Roger, J.-C., Skakun, S., Claverie, M., and Dungan, J. (2018, January 22\u201327). Harmonized Landsat\/Sentinel-2 Products for Land Monitoring. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517760"},{"key":"ref_54","unstructured":"(2019, May 31). Landsat Collection 1 Level-1 Quality Assessment Band, Available online: https:\/\/www.usgs.gov\/land-resources\/nli\/landsat\/landsat-collection-1-level-1-quality-assessment-band?qt-science_support_page_related_con=0-qt-science_support_page_related_con."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.rse.2015.11.003","article-title":"Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia","volume":"174","author":"Mueller","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1016\/j.scitotenv.2014.04.078","article-title":"Shifting mountain snow patterns in a changing climate from remote sensing retrieval","volume":"493","author":"Dedieu","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"319","DOI":"10.5194\/hess-13-319-2009","article-title":"Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products","volume":"13","author":"Tong","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/1999RG000076","article-title":"Measuring snow and glacier ice properties from satellite","volume":"39","author":"Winther","year":"2001","journal-title":"Rev. Geophys."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2019WR024932","article-title":"Cloud Masking for Landsat 8 and MODIS Terra Over Snow-Covered Terrain: Error Analysis and Spectral Similarity Between Snow and Cloud","volume":"55","author":"Stillinger","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_60","unstructured":"(2019, May 31). CFMask Algorithm, Available online: https:\/\/www.usgs.gov\/land-resources\/nli\/landsat\/cfmask-algorithm."},{"key":"ref_61","unstructured":"(2019, May 31). Global Surface Water Explorer. Available online: https:\/\/global-surface-water.appspot.com\/."},{"key":"ref_62","unstructured":"Kyle, H., Curran, R., Barnes, W., and Escoe, D. (1978, January 28\u201330). A cloud physics radiometer. Proceedings of the 3rd Conference on Atmospheric Radiation, Davis, CA, USA."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TGRS.1984.350628","article-title":"Snow reflectance from Landsat-4 thematic mapper","volume":"GE-22","author":"Dozier","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","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_65","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 2000-2001 snow year","volume":"86","author":"Klein","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"493","DOI":"10.5194\/essd-11-493-2019","article-title":"Theia Snow collection: High-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data","volume":"11","author":"Gascoin","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1080\/01431160500497820","article-title":"Algorithm to monitor snow cover using AWiFS data of RESOURCESAT-1 for the Himalayan region","volume":"27","author":"Kulkarni","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2013.08.026","article-title":"Using atmospherically-corrected Landsat imagery to measure glacier area change in the Cordillera Blanca, Peru from 1987 to 2010","volume":"140","author":"Burns","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_69","unstructured":"Grumman, N.J.R.B. (2010). VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD), Northrup Grumman Aerospace Systems."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.5194\/tc-12-1629-2018","article-title":"On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales","volume":"12","author":"Bernhardt","year":"2018","journal-title":"Cryosphere"},{"key":"ref_71","unstructured":"Riggs, G.A., Hall, D.K., and Salomonson, V.V. (1994, January 8\u201312). A snow index for the Landsat thematic mapper and moderate resolution imaging spectroradiometer. Proceedings of the IGARSS\u201994-1994 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.rse.2007.05.016","article-title":"Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China","volume":"112","author":"Wang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Dietz, A.J., Kuenzer, C., and Dech, S. (2015). Analysis of Snow Cover Time Series\u2013Opportunities and Techniques. Remote Sensing Time Series, Springer.","DOI":"10.1007\/978-3-319-15967-6"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1024458411589","article-title":"Climate change in mountain regions a review of possible impacts","volume":"59","author":"Beniston","year":"2003","journal-title":"Clim. Chang."},{"key":"ref_75","unstructured":"ASTER Global Digital Elevation Map (2019, May 31). ASTER GDEM is a Product of METI and NASA, Available online: https:\/\/asterweb.jpl.nasa.gov\/gdem.asp."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s00704-015-1676-7","article-title":"Impact of climate change in Switzerland on socioeconomic snow indices","volume":"127","author":"Schmucki","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"73","DOI":"10.5194\/tc-8-73-2014","article-title":"A satellite-based snow cover climatology (1985\u20132011) for the European Alps derived from AVHRR data","volume":"8","author":"Jonas","year":"2014","journal-title":"Cryosphere"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.rse.2010.02.017","article-title":"Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA","volume":"114","author":"Gao","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Parajka, J., and Bl\u00f6schl, G. (2008). Spatio-temporal combination of MODIS images-potential for snow cover mapping. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006204"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.5194\/hess-13-1361-2009","article-title":"Cloud removal methodology from MODIS snow cover product","volume":"13","author":"Gafurov","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jhydrol.2009.11.042","article-title":"A regional snow-line method for estimating snow cover from MODIS during cloud cover","volume":"381","author":"Parajka","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_82","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_83","unstructured":"Qobilov, T., Pertziger, F., Vasilina, L., and Baumgartner, M. (2001). Operational Technology for Snow-Cover Mapping in the Central Asian Mountains Using NOAA-AVHRR Data."},{"key":"ref_84","unstructured":"Dietz, A.J., Hu, Z., and Tsai, Y.-L. (2018, January 27\u201329). Remote Sensing of Snow Cover in The Alps-an Overview of Opportunities and Constraints. Proceedings of the EO4Alps on the Alps from Space Workshop, Innsbruck, Austria."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.accre.2015.09.007","article-title":"Earth observation big data for climate change research","volume":"6","author":"Guo","year":"2015","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.advwatres.2012.12.009","article-title":"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland","volume":"55","author":"Bavay","year":"2013","journal-title":"Adv. Water Resour."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/4\/138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:28:40Z","timestamp":1760189320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/4\/138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,9]]},"references-count":86,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["data4040138"],"URL":"https:\/\/doi.org\/10.3390\/data4040138","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,9]]}}}