积雪是冰冻圈重要的组成部分，积雪覆盖范围影响地气能量平衡，进而影响气候和环境变化。积雪面积是重要的积雪参数之一，是水文和气候模型的重要输入。本数据集针对中国积雪特性，基于MODIS反射率产品MOD/MYD09GA，利用不同土地覆盖类型条件下发展了多指数结合积雪判别算法，提高了林区和山区积雪面积精度，同时利用隐马尔科夫算法、多源数据融合方法实现了产品的完全去云，制备2000年以来空间分辨率为500m的逐日无云积雪面积数据集。该数据集以HDF5文件格式存储，每个HDF5文件包含18个数据要素，其中包括数据值（0=陆地，1=影像识别积雪， 2=去云插补积雪，3=雪深插补积雪，4=水体，255=填充值）、数据起始日期、经纬度等。同时为了快速预览积雪分布情况，逐日文件包含积雪面积缩略图，以jpg格式存储。此外本数据集还包含了用户使用手册。本数据集将根据卫星遥感数据和算法更新情况实时更新，并采用完全开放共享。
", "ds_source": "MODIS逐日表面反射率产品MOD09GA,MYD09GA来自于美国国家航空航天局（NASA），数据格式为hdf格式，空间分辨率为500m。
", "ds_process_way": "利用Landsat-8 OIL数据作为真值，结合MODIS土地覆盖分类产品MCD12Q1，基于MODIS反射率产品MOD09GA和MYD09GA，获取不同地表覆盖类型条件下的积雪决策树分类算法，基于GEE平台，获取初级产品。初级产品经过隐马尔科夫算法和雪深数据插值方法进行去云处理，基于python语言开发运行程序，最终获取研究区逐日无云积雪面积产品。
", "ds_quality": "数据质量良好。", "ds_acq_start_time": "2000-02-27 00:00:00", "ds_acq_end_time": "2026-07-21 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 142.0, "ds_acq_lat_south": 16.0, "ds_acq_lon_west": 72.0, "ds_acq_lat_north": 56.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 18581666996, "ds_files_count": 15960, "ds_format": "HDF5", "ds_space_res": "500", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "经纬度（GLL）投影", "ds_thumbnail": "be3a4134-2e5c-467f-8a5e-b1c0ed6cc341.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [], "quality_level": 3, "publish_time": "2022-05-10 15:35:14", "last_updated": "2026-07-30 10:21:52", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.I-SNOW.2020.9", "i18n": { "en": { "title": "China's MODIS daily cloudless 500m snow cover area product data set (2000–Present)", "ds_format": "HDF5", "ds_source": "
MODIS day-by-day surface reflectance products MOD09GA,MYD09GA from the National Aeronautics and Space Administration (NASA), the data format is hdf format, the spatial resolution is 500m.
", "ds_quality": "The data quality is good.", "ds_ref_way": "", "ds_abstract": "Snow cover is an important component of the cryosphere. The extent of snow cover affects the Earth-atmosphere energy balance, thereby influencing climate and environmental change. Snow cover area is one of the key snow parameters and serves as a critical input for hydrological and climate models.\r\n\r\nThis dataset is tailored to the snow characteristics of China. Based on the MODIS reflectance products MOD/MYD09GA, a multi-index combined snow discrimination algorithm was developed under different land cover types to improve the accuracy of snow cover area in forested and mountainous regions. Furthermore, the Hidden Markov Model algorithm and multi-source data fusion methods were employed to achieve complete cloud removal, producing a daily cloud-free snow cover area dataset at a spatial resolution of 500 m since 2000.\r\n\r\nThe dataset is stored in HDF5 file format. Each HDF5 file contains 18 data elements, including data values (0 = land, 1 = snow identified from imagery, 2 = cloud-gap-filled snow, 3 = snow-depth-interpolated snow, 4 = water, 255 = fill value), data start date, latitude, longitude, and others. For quick preview of snow distribution, each daily file includes a snow cover area thumbnail image stored in JPG format. In addition, a user manual is provided with this dataset. The dataset will be updated in real time based on updates to satellite remote sensing data and algorithms, and is made available under a fully open-access policy.", "ds_time_res": "", "ds_acq_place": "China's Land Territory", "ds_space_res": "", "ds_projection": "GLL", "ds_process_way": "
Using Landsat-8 OIL data as the true value, combined with MODIS land cover classification product MCD12Q1, based on MODIS albedo products MOD09GA and MYD09GA, the snow accumulation decision tree classification algorithms under different conditions of surface cover types were obtained, and based on the GEE platform, the primary products were obtained.\r\n
The primary products were de-clouded by Hidden Markov Algorithm and snow depth data interpolation method, and the running program was developed based on python language to finally obtain the day-by-day cloud-free snow area products in the study area.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": true, "allow_update_data": true, "ds_topic_tags": [ "积雪面积", "去云", "逐日", "MODIS" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026 ], "ds_contributors": [], "ds_meta_authors": [], "ds_managers": [], "category": "积雪" }