{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T17:19:46Z","timestamp":1763399986656,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["820852","871128"],"award-info":[{"award-number":["820852","871128"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snow cover plays an important role in biotic and abiotic environmental processes, as well as human activities, on both regional and global scales. Due to the difficulty of in situ data collection in vast and inaccessible areas, the use of optical satellite imagery represents a useful support for snow cover mapping. At present, several operational snow cover algorithms and products are available. Even though most of them offer an up-to-daily time scale, they do not provide sufficient spatial resolution for studies requiring high spatial detail. By contrast, the Let-It-Snow (LIS) algorithm can produce high-resolution snow cover maps, based on the use of both the normalized-difference snow index (NDSI) and a digital elevation model. The latter is introduced to define a threshold value on the altitude, below which the presence of snow is excluded. In this study, we revised the LIS algorithm by introducing a new parameter, based on a threshold in the shortwave infrared (SWIR) band, and by modifying the overall algorithm workflow, such that the cloud mask selection can be used as an input. The revised algorithm has been applied to a case study in Gran Paradiso National Park. Unlike previous studies, we also compared the performance of both the original and the modified algorithms in the presence of cloud cover, in order to evaluate their effectiveness in discriminating between snow and clouds. Ground data collected by meteorological stations equipped with both snow gauges and solarimeters were used for validation purposes. The changes introduced in the revised algorithm can improve upon the overall classification accuracy obtained by the original LIS algorithm (i.e., up to 89.17 from 80.88%). The producer\u2019s and user\u2019s accuracy values obtained by the modified algorithm (89.12 and 95.03%, respectively) were larger than those obtained by the original algorithm (76.68 and 93.67%, respectively), thus providing a more accurate snow cover map.<\/jats:p>","DOI":"10.3390\/rs13101957","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T06:01:33Z","timestamp":1621317693000},"page":"1957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Revised Snow Cover Algorithm to Improve Discrimination between Snow and Clouds: A Case Study in Gran Paradiso National Park"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2370-7768","authenticated-orcid":false,"given":"Chiara","family":"Richiardi","sequence":"first","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c\/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy"}]},{"given":"Palma","family":"Blonda","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c\/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4675-5746","authenticated-orcid":false,"given":"Fabio Michele","family":"Rana","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c\/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0401-3100","authenticated-orcid":false,"given":"Mattia","family":"Santoro","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3304-5355","authenticated-orcid":false,"given":"Cristina","family":"Tarantino","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c\/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1140-0483","authenticated-orcid":false,"given":"Saverio","family":"Vicario","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c\/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-4884","authenticated-orcid":false,"given":"Maria","family":"Adamo","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), c\/o Interateneo Physics Department, Via Amendola 173, 70126 Bari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8045","DOI":"10.1029\/2018WR023190","article-title":"Direct Insertion of NASA Airborne Snow Observatory-Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model","volume":"54","author":"Hedrick","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.5194\/tc-14-2495-2020","article-title":"Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble","volume":"14","author":"Mudryk","year":"2020","journal-title":"Cryosphere"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.5194\/tc-11-1647-2017","article-title":"Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment","volume":"11","author":"Stigter","year":"2017","journal-title":"Cryosphere"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100224","DOI":"10.1016\/j.jort.2019.100224","article-title":"Greenland winter tourism in a changing climate","volume":"27","author":"Schrot","year":"2019","journal-title":"J. Outdoor Recreat. Tour."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Qiao, D., and Wang, N. (2019). Relationship between winter snow cover dynamics, climate and spring grassland vegetation phenology in inner Mongolia, China. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010042"},{"key":"ref_6","first-page":"1","article-title":"How are turbulent sensible heat fluxes and snow melt rates affected by a changing snow cover fraction?","volume":"6","author":"Lehning","year":"2018","journal-title":"Front. Earth Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10666-018-9641-3","article-title":"A General Equilibrium Assessment of Climate Change Impacts on Swiss Winter Tourism with Adaptation","volume":"24","author":"Gonseth","year":"2019","journal-title":"Environ. Model. Assess."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.jhydrol.2018.04.027","article-title":"Remote sensing, hydrological modeling and in situ observations in snow cover research: A review","volume":"561","author":"Dong","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_9","first-page":"74","article-title":"Tourism megatrends, a literature review focused on nature-based tourism","volume":"42","author":"Elmahdy","year":"2017","journal-title":"MINA Fagrapp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1038\/s41558-018-0311-x","article-title":"Snow cover is a neglected driver of Arctic biodiversity loss","volume":"8","author":"Niittynen","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, X., Li, N., and Pan, Y. (2020). Gap-Filling of a MODIS normalized difference snow index product based on the similar pixel selecting algorithm: A case study on the Qinghai-Tibetan Plateau. Remote Sens., 12.","DOI":"10.3390\/rs12071077"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41558-018-0318-3","article-title":"Estimating snow-cover trends from space","volume":"8","author":"Bormann","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_14","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_15","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":"4257","author":"Dozier","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Matiu, M., Jacob, A., and Notarnicola, C. (2020). Daily MODIS snow cover maps for the european alps from 2002 onwards at 250 m horizontal resolution along with a nearly cloud-free version. Data, 5.","DOI":"10.3390\/data5010001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.rse.2012.02.018","article-title":"Validation of a modified snow cover retrieval algorithm from historical 1-km AVHRR data over the European Alps","volume":"121","author":"Jonas","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.rse.2012.04.010","article-title":"An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale","volume":"123","author":"Mattila","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"110","DOI":"10.3390\/rs5010110","article-title":"Snow cover maps from MODIS images at 250 m resolution, part 1: Algorithm description","volume":"5","author":"Notarnicola","year":"2013","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1080\/0028825X.1986.10409942","article-title":"Frequency of cloud cover on New Zealand mountains in relation to subalpine vegetation","volume":"24","author":"Wardle","year":"1986","journal-title":"N. Zeal. J. Bot."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.14358\/PERS.85.2.109","article-title":"Landsat orbital repeat frequency and cloud contamination: A case study for eastern united states","volume":"85","author":"Goward","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","first-page":"102172","article-title":"Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series","volume":"92","author":"Kowalski","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Poussin, C., Guigoz, Y., Palazzi, E., Terzago, S., Chatenoux, B., and Giuliani, G. (2019). Snow Cover Evolution in the Gran Paradiso Observation Data Cube. Data, 4.","DOI":"10.3390\/data4040138"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dedieu, J.P., Carlson, B.Z., Bigot, S., Sirguey, P., Vionnet, V., and Choler, P. (2016). On the importance of high-resolution time series of optical imagery for quantifying the effects of snow cover duration on alpine plant habitat. Remote Sens., 8.","DOI":"10.3390\/rs8060481"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"17246","DOI":"10.3390\/rs71215882","article-title":"An effective method for snow-cover mapping of dense coniferous forests in the upper Heihe River Basin using Landsat Operational Land Imager data","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.3390\/rs4051134","article-title":"Validation of NOAA-interactive multisensor snow and Ice Mapping System (IMS) by comparison with ground-based measurements over continental United States","volume":"4","author":"Chen","year":"2012","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Piazzi, G., Tanis, C.M., Kuter, S., Simsek, B., Puca, S., and Arslan, A.N. (2019). Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography. Geosciences, 9.","DOI":"10.3390\/geosciences9030129"},{"key":"ref_31","first-page":"294","article-title":"Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps","volume":"9","author":"Foppa","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1000107","DOI":"10.1117\/12.2240935","article-title":"MACCS-ATCOR joint algorithm (MAJA)","volume":"10001","author":"Lonjou","year":"2016","journal-title":"Remote Sens. Clouds Atmos. XXI"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"3","article-title":"Sen2Cor for Sentinel-2","volume":"1042704","author":"Pflug","year":"2017","journal-title":"SPIE Remote Sens."},{"key":"ref_35","unstructured":"Gascoin, S., Grizonnet, M., Klempka, T., and Salgues, G. (2021, January 13). Theia Land Data Centre Algorithm Theoritical Basis Documentation for an Operational Snow Cover Extent Product from Sentinel-2 and Landsat-8 Data (Let-It-Snow). Available online: https:\/\/zenodo.org\/record\/1414452."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of copernicus Sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and FMask processors using reference cloud masks generated with a supervised active learning procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100010","DOI":"10.1016\/j.srs.2020.100010","article-title":"Comparison of cloud detection algorithms for Sentinel-2 imagery","volume":"2","author":"Tarrio","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4004","DOI":"10.1364\/AO.37.004004","article-title":"Correction of satellite imagery over mountainous terrain","volume":"37","author":"Richter","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_39","first-page":"691","article-title":"Evaluation of different topographic correction methods for landsat imagery","volume":"13","author":"Hantson","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","unstructured":"(2021, January 13). Theia Snow L2B and L3B. Available online: https:\/\/theia.cnes.fr\/atdistrib\/rocket\/#\/search?collection=Snow."},{"key":"ref_41","unstructured":"Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R. (2021, January 13). MAJA ATBD Algorithm Theoretical Basis Document. Available online: https:\/\/zenodo.org\/record\/1209633."},{"key":"ref_42","unstructured":"Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2015). Remote Sensing and Image Interpretation, John Wiley & Sons. [7th ed.]."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2009.03.014","article-title":"Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority","volume":"113","author":"Foody","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Foody, G. (2010). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1111\/j.1477-9730.2010.00574_2.x"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pembury Smith, M.Q.R., and Ruxton, G.D. (2020). Effective use of the McNemar test. Behav. Ecol. Sociobiol., 74.","DOI":"10.1007\/s00265-020-02916-y"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Santoro, M., Mazzetti, P., and Nativi, S. (2020). The VLab framework: An orchestrator component to support data to knowledge transition. Remote Sens., 12.","DOI":"10.3390\/rs12111795"},{"key":"ref_47","unstructured":"(2021, January 13). ESA Land Cover CCI Product User Guide Version 2. Tech. Rep., Available online: Maps.elie.ucl.ac.be\/CCI\/viewer\/download\/ESACCI-LC-Ph2-PUGv2_2.0.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Guo, Z., Geng, L., Shen, B., Wu, Y., Chen, A., and Wang, N. (2021). Spatiotemporal variability in the glacier snowline altitude across high mountain asia and potential driving factors. Remote Sens., 13.","DOI":"10.3390\/rs13030425"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Deng, G., Tang, Z., Hu, G., Wang, J., Sang, G., and Li, J. (2021). Spatiotemporal Dynamics of Snowline Altitude and Their Responses to Climate Change in the Tienshan Mountains, Central Asia, during 2001\u20132019. Sustainability, 13.","DOI":"10.3390\/su13073992"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kukawska, E., Lewinski, S., Krupinski, M., Malinowski, R., Nowakowski, A., Rybicki, M., and Kotarba, A. (2017, January 27\u201329). Multitemporal Sentinel-2 data\u2014Remarks and observations. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035212"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"H\u00e4rer, S., Bernhardt, M., Siebers, M., and Schulz, K. (2017). On the need of a time and location dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales. Cryosph. Discuss., 1\u201327.","DOI":"10.5194\/tc-2017-177"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:03:03Z","timestamp":1760162583000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13101957"],"URL":"https:\/\/doi.org\/10.3390\/rs13101957","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,5,18]]}}}