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However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54\u201398.08%, a low range underestimation error (MU) of 0.15\u20133.49% and an acceptable range overestimation error (MO) of 0.84\u20135.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52\u201392.40%, the omission error (OE) of 1.42\u201310.28% and the commission error (CE) of 5.97\u201317.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.<\/jats:p>","DOI":"10.3390\/rs16010192","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T10:36:59Z","timestamp":1704191819000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5498-0196","authenticated-orcid":false,"given":"Gang","family":"Deng","sequence":"first","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhiguang","family":"Tang","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5721-9247","authenticated-orcid":false,"given":"Chunyu","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6681-3640","authenticated-orcid":false,"given":"Donghang","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"ref_1","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. 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