Snow cover is an important part of the cryosphere, and the extent of snow cover affects the balance of earth-atmosphere energy, which in turn affects climate and environmental changes. Snow cover area is one of the important snow cover parameters and an important input to hydrological and climate models. The China MODIS daily cloud-free 500m snow cover area product (2000-present) dataset produced by Hao Xiaohua's team from the Northwest Institute of Ecology, Environmental Resources, China Academy of Sciences was officially released at the National Glacier, Permafrost and Desert Scientific Data Center. This dataset is an updated version of China's MODIS daily cloudless 500m snow cover area product (2000-2020). Starting from 2023, we use the updated MOD/MYD09GA V061 compared to the previously used MOD/MYD09GA SR V006 raw data, and use the updated China snow depth time series dataset (1979-2025) in the snow depth interpolation step. Aiming at the characteristics of snow cover in China, this dataset is based on the MODIS reflectance product MOD/MYD09GA. It develops a multi-index combined snow cover discrimination algorithm under different land cover types to improve the accuracy of snow cover area in forest and mountainous areas. At the same time, the hidden Markov algorithm and multi-source data fusion method are used to achieve complete cloud removal of the product, and a daily cloud-free snow cover area data set with a spatial resolution of 500m since 2000 is prepared. This dataset is stored in the format of an HDF5 file. Each HDF5 file contains 18 data elements, including data values (0= land, 1= image recognition of snow, 2= cloud removal and interpolation of snow, 3= snow depth interpolation of snow, 4= water body, 255= filling value), data start date, latitude and longitude, etc. At the same time, in order to quickly preview the snow distribution, the daily file contains a thumbnail of the snow area and is stored in jpg format. In addition, this dataset also includes a user manual. The current dataset is updated until November 30, 2025. In the future, this dataset will be updated in real time based on satellite remote sensing data and algorithm updates, and will be fully open and shared. Scientific researchers, university teachers and students and business units are welcome to download and use it for free. Literature citation format: 1. Hao Xiaohua, Huang Guanghui, Zheng Zhaojun, Sun Xingliang, Ji Wenzheng, Zhao Hongyu, Wang Jian, Li Hongyi, Wang Xiaoyan. Development and validation of a new MODIS snow-cover-extent product over China. Hydrology and Earth System Science, 2022, 26, 1937–1952. https://doi.org/10.5194/hess-26-1937-2022 2. Zhao, Q.; Hao, X.; Shao, D.; Ji, W.; Huang, G.; Zhao, Z.; Zhang, J. Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework. Remote Sens. 2025, 17, 3992. https://doi.org/10.3390/rs17243992 Data reference format: 1. Hao Xiaohua, Huang Guanghui, Zheng Zhaojun, Sun Xingliang, Ji Wenzheng, Zhao Hongyu, Wang Jian, Li Hongyi, Wang Xiaoyan. Development and validation of a new MODIS snow-cover-extent product over China. Hydrology and Earth System Science, 2022, 26, 1937–1952. https://doi.org/10.5194/hess-26-1937-2022 2. Zhao, Q.; Hao, X.; Shao, D.; Ji, W.; Huang, G.; Zhao, Z.; Zhang, J. Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework. Remote Sens. 2025, 17, 3992. https://doi.org/10.3390/rs17243992 1. Hao Xiaohua, Sun Xingliang, Ji Wenzheng, Wang Xiaoyan, Gao Yang, Zhao Qin, Zhao Hongyu, Wang Jian, Li Hongyi. China's MODIS daily cloudless 500m snow cover area products (2000-present). National Glacier, Frozen Soil and Desert Scientific Data Center (www.ncdc.ac.cn), 2022. https://cstr.cn/CSTR:11738.11.ncdc.I-SNOW.2020.9 2. Hao Xiaohua, Sun Xingliang, Ji Wenzheng, Wang Xiaoyan, Gao Yang, Zhao Qin, Zhao Hongyu, Wang Jian, Li Hongyi. China's MODIS daily cloudless 500m snow cover area products (2000-present). National Glacier, Frozen Soil and Desert Scientific Data Center (www.ncdc.ac.cn), 2022. https://www.doi.org/10.12072/ncdc.I-SNOW.db0001.2020