雪水当量（SWE）是评价雪水文、气候调节和水资源管理的关键参数。针对中国积雪分布区，基于多源遥感数据融合与机器学习降尺度技术，综合考虑积雪范围、地形和地理位置等影响积雪分布的环境因子，发展精细化雪水当量降尺度模型，制备1980-2020年逐日500m雪水当量数据集。利用地面观测站点数据对雪水当量产品进行精度验证，R=0.83, RMSE=10.98mm。该数据集以.tif文件格式存储，时间分辨率daily，空间分辨率500m。", "ds_source": "
25km雪水当量数据集：http://data.tpdc.ac.cn；\n
25km雪深数据集：http://data.tpdc.ac.cn；\n
5kmMODIS、AVHRRz积雪覆盖范围数据集：http://www.ncdc.ac.cn；\n
地面观测站点数据：https://www.ncdc.ac.cn/portal/；\n
90m数字地形模型数据：https://srtm.csi.cgiar.org", "ds_process_way": "
基于25km雪水当量融合产品和雪深产品，利用时间滤波技术重建25km完整时空序列雪水当量数据集， 结合地面观测真值、光学遥感数据和环境协变量构建随机森林降尺度模型，生成中国逐日500m雪水当量数据。", "ds_quality": "
质量良好。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.08277777777778, "ds_acq_lat_south": 3.85, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": -277.0, "ds_acq_alt_high": 8792.0, "ds_share_type": "apply-access", "ds_total_size": 366435313393, "ds_files_count": 14794, "ds_format": "*.tif", "ds_space_res": "500m", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "a826c5f9-8613-48b1-a062-04b94a35c4f1.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510", "170.55" ], "quality_level": 3, "publish_time": "2025-09-08 15:30:57", "last_updated": "2026-05-20 11:05:15", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.LZU.DB6967.2025", "i18n": { "en": { "title": "Daily 500m Snow Water Equivalent Product in China (1980–2020)", "ds_format": "*.tif", "ds_source": "
25km Snow Water Equivalent datasets: http://data.tpdc.ac.cn；\r\n
25km Snow Depth Products: http://data.tpdc.ac.cn；\r\n
5km MODIS、AVHRR Snow Cover Extent Products: http://www.ncdc.ac.cn；Ground \r\n
Observation: https://www.ncdc.ac.cn/portal/；\r\n
90m DEM：https://srtm.csi.cgiar.org", "ds_quality": "
Good quality.", "ds_ref_way": "", "ds_abstract": "
Snow Water Equivalent (SWE) is a key parameter for evaluating snow hydrology, climate regulation, and water resource management. For China's snow-covered regions, a refined SWE downscaling model was developed by integrating multi-source remote sensing data with machine learning downscaling techniques. This model comprehensively considers environmental factors influencing snow distribution, including snow extent, topography, and geographic location, to generate a daily 500m SWE dataset covering 1980–2020. The accuracy of the SWE product was validated using ground observation station data, yielding an R=0.83 and RMSE=10.98 mm. The dataset is stored in .tif format with a daily temporal resolution and 500m spatial resolution.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Based on 25 km snow water equivalent fusion products and snow depth products, a complete spatiotemporal dataset of 25 km snow water equivalent was reconstructed using temporal filtering techniques. A random forest downscaling model was developed by integrating ground observation references, optical remote sensing data, and environmental covariates to generate daily 500 m snow water equivalent data for China.", "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": false, "ds_topic_tags": [ "雪水当量", "SWE", "积雪" ], "ds_subject_tags": [ "水文学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "黄晓东", "email": "huangxd@lzu.edu.cn", "work_for": "兰州大学草地农业科技学院", "country": "中国" }, { "true_name": "李雨馨", "email": "yuxinli2024@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李雨馨", "email": "yuxinli2024@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "黄晓东", "email": "huangxd@lzu.edu.cn", "work_for": "兰州大学草地农业科技学院", "country": "中国" } ], "category": "积雪" }