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The dataset includes 47,728 samples of 128×128 MODIS pixel tiles with consistent FSC reference labels built from Landsat and Sentinel imagery. Twenty feature variables and topographic, land cover, and geolocation factors support point- and tile-scale spatial AI modeling. A “Four Layers-Four Domains-Fifteen Attributes” protocol evaluates reliability and AI-readiness, and multiple use cases demonstrate benchmarking, validation of MODIS FSC products, and nationwide seamless mapping, with openly available documentation and metadata.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/chinaai-fsc-a-comprehensive-ai-ready-modis-fractional-snow-cover-dataset-for-china-2000-2022/500836/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/chinaai-fsc-a-comprehensive-ai-ready-modis-fractional-snow-cover-dataset-for-china-2000-2022/500836.png","ImageObject",300,407,{"name":92,"@type":93},"Indoniesian Boy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does ChinaAI-FSC address for fractional snow cover monitoring?","Question",{"text":112,"@type":113},"ChinaAI-FSC targets the critical gap in long-term, high-quality fractional snow cover records by offering a large-scale, standardized, AI-ready dataset spanning 2000–2022.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the FSC reference labels generated in ChinaAI-FSC?",{"text":117,"@type":113},"High-resolution Landsat (5/7/8/9) and Sentinel-2 imagery are used to provide consistent FSC reference labels for the MODIS-based samples.",{"name":119,"@type":110,"acceptedAnswer":120},"What features and variables does the dataset provide for AI modeling?",{"text":121,"@type":113},"The dataset includes 20 feature variables, including MODIS surface reflectance bands 1–7, topographic attributes, forest and land cover information, and geolocation factors.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},500836,1791908573,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090760608,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","ChinaAI-FSC: A Comprehensive AI-Ready MODIS Fractional Snow Cover Dataset for China (2000-2022)  \nJinliang Hou 1, Mingkai Zhang 1,2, Xiaohua Hao 1, Jifu Guo3, Peng Dou 1, Ying Zhang4*, Chunlin Huang 1,5*  \n1 Heihe Remote Sensing Experimental Research Station, State Key Laboratory of Cryospheric Science and Frozen Soil  \n5 Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China  \n2 University of Chinese Academy of Sciences, Beijing, 100094, China  \n3 College of Information Science and Technology, Gansu Agricultural University , Lanzhou 730070, China  \n4 State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China  \n10 5 Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China  \nCorrespondence to: Ying Zhang ([zhang_y@lzb.ac.cn](zhang_y@lzb.ac.cn)), Chunlin Huang ([huangcl@lzb.ac.cn](huangcl@lzb.ac.cn))  \nAbstract. We present ChinaAI-FSC, the first large-scale, standardized, AI-ready fractional snow cover (FSC) sample collection for China, spanning 22 snow seasons from 2000 to 2022 and addressing a critical gap in long-term snow monitoring.  \n15 The dataset consists of 47,728 samples (each 128×128 MODIS-pixel tiles), where high-resolution Landsat-5/7/8/9 and Sentinel-2 imagery provide consistent FSC reference labels. A total of 20 feature variables, including MODIS surface reflectance (bands 1-7), topographic attributes, forest and land cover information, and geolocation factors, were extracted to enable both point-scale and tile-scale spatially contextualized AI modelling. A structured and transparent workflow, encompassing systematic sample preparation, rigorous quality control, spatiotemporal sample partitioning, and standardized  \n20 metadata, ensures reproducibility, physical consistency, and interoperability across machine learning and deep learning applications. Dataset reliability and AI-readiness were systematically evaluated using a novel “Four Layers-Four DomainsFifteen Attributes (4L-4D-15A)” assessment protocol, covering data, information, system, and application dimensions. The quality, reliability, and usability of ChinaAI-FSC were demonstrated through three representative use cases: (1) benchmarking of six ML/DL models (ANN, SVR, RF, CNN, UNet, and ResNet),(2) validation of the standard MODIS FSC product, and (3)  \n25 nationwide seamless FSC mapping. By providing harmonized, validated, and well-documented samples, ChinaAI-FSC establishes a unified foundation for AI-driven snow cover mapping, long-term monitoring, and cryosphere–hydrological modelling, promoting reproducible, interoperable, and next-generation research in cryospheric science. The dataset is publicly available from the National Tibetan Plateau Data Center (TPDC) (Hou et al., 2025a) at [https://doi.org/10.11888/Cryos.tpdc.303034](https://doi.org/10.11888/Cryos.tpdc.303034) (also accessible via [https://cstr.cn/18406.11.Cryos.tpdc.303034](https://cstr.cn/18406.11.Cryos.tpdc.303034)) and from Zenodo  \n30 (Hou et al., 2025b) at [https://doi.org/10.5281/zenodo.17707386](https://doi.org/10.5281/zenodo.17707386) .  \nKey words: Fractional Snow Cover (FSC); AI-Ready; ML/DL; MODIS  \n1 Background and Motivation  \nFractional Snow Cover (FSC) is a fundamental indicator for monitoring snowpack dynamics, as it quantifies the proportion of snow within a pixel, providing a continuous measure of snow extent that goes beyond binary snow/no-snow classifications.  \n35 Scientifically, FSC is a critical variable linking snowpack dynamics with energy and water exchanges at the land-atmosphere interface. It strongly influences surface albedo and shortwave radiation absorption, thereby affecting the timing of snowmelt , soil moisture evolution, and local to regional atmospheric circulation (Hall & Riggs, 2007; Frei et al., 2012) . Accurate FSC information enhances the representation","cbCaioUyIFUdVqlF","https://ap.wps.com/l/cbCaioUyIFUdVqlF","pdf",2980381,38,"English","# Background and Motivation\n## Fractional Snow Cover (FSC) and its significance\n## Challenges in operational FSC estimation\n## Early FSC retrieval approaches","[{\"question\":\"What problem does ChinaAI-FSC address for fractional snow cover monitoring?\",\"answer\":\"ChinaAI-FSC targets the critical gap in long-term, high-quality fractional snow cover records by offering a large-scale, standardized, AI-ready dataset spanning 2000–2022.\"},{\"question\":\"How are the FSC reference labels generated in ChinaAI-FSC?\",\"answer\":\"High-resolution Landsat (5/7/8/9) and Sentinel-2 imagery are used to provide consistent FSC reference labels for the MODIS-based samples.\"},{\"question\":\"What features and variables does the dataset provide for AI modeling?\",\"answer\":\"The dataset includes 20 feature variables, including MODIS surface reflectance bands 1–7, topographic attributes, forest and land cover information, and geolocation factors.\"}]","ChinaAI-FSC: A Comprehensive AI-Ready MODIS Fractional Snow Cover Dataset for China - 2000-2022 | PDF",1791015363,96]