ChinaAI-FSC: A Comprehensive AI-Ready MODIS Fractional Snow Cover Dataset for China (2000–2022)

Abstract. We present ChinaAI-FSC, the first large-scale, standardized, AI-ready fractional snow cover (FSC) sample collection for mainland China, spanning 22 snow seasons from 2000 to 2022 and addressing a critical gap in long-term snow monitoring. 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 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 Domains-Fifteen 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) 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) at https://doi.org/10.11888/Cryos.tpdc.303034 (also accessible via https://cstr.cn/18406.11.Cryos.tpdc.303034) and from Zenodo at https://doi.org/10.5281/zenodo.17707386.