高空间分辨率雪水当量对水文、生态和灾害研究至关重要。然而，被动微波雪水当量产品（10/25 km）因其空间分辨率较粗已无法满足现代高精度高分辨率的需求。本研究融合了最新校准的增强分辨率亮度温度与光学积雪面积比例和积雪覆盖日数等数据，基于深度学习FT-Transformer模型反演了三江源积雪期内（当年10月至次年4月）5 km空间分辨率的逐日雪深数据，通过逐月平均的积雪密度数据将雪深转化为5 km空间分辨率的雪水当量数据。为三江源的积雪资源监测提供了可靠的数据基础。", "ds_source": "
（1）校准的增强分辨率亮温数据（The Calibrated Enhanced Resolution Brightness Temperature，CETB）由美国国家雪冰数据中心提供（https://nsidc.org/data/NSIDC-0630/versions/1）。 该数据涵盖了1978年以来不同卫星的观测亮温数据，时间分辨率为1d，空间分辨率为6.25 km/3.125 km。\n
（2）积雪面积比例数据来源于文献（https://www.sciencedirect.com/science/article/pii/S0924271624003265）， 该数据时间分辨率为1 d，空间分辨率为5 km。积雪覆盖日数数据源于国家冰川冻土沙漠科学数据中心（http://www.ncdc.ac.cn）， 该数据时间分辨率为1 d，空间分辨率为500 m。\n
（3）DEM数据由国家地理空间情报局（NGA）和国家航空航天局（NASA）运营的航天飞机雷达地形测绘任务（SRTM）提供（http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp）， 空间分辨率为90 m。
（4）土地利用类型数据源于MCD12Q1 V061数据集（https://earthexplorer.usgs.gov/）， 采用其中IGBP分类标准的年度土地覆盖类型，该数据时间分辨率为1 yr，空间分辨率为500 m。\n
（5）积雪密度数据源于国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn)的青藏高原月度多年平均积雪密度格点数据集，该数据包含12景每月5 km的积雪密度图。", "ds_process_way": "
（1）利用Python平台统一批量处理各种数据源的空间分辨率为5 km，以此构建雪深反演的数据输入；（2）通过深度学习模型训练和参数优化实现多种数据融合的雪深反演模型；（3）使用训练保存的模型反演三江源雪深数据；（4）进行水体掩膜，然后通过前后日平均填补被动微波辐射计的轨道间隙；（5）将雪深数据乘以积雪密度获取雪水当量。", "ds_quality": "
数据质量良好，由于雪水当量主要源于雪深通过逐月平均的积雪密度转化，而雪深的精度经验证良好：采用均方根误差（RMSE）、平均绝对误差（MAE）和相关系数（R）三个标准指标表示雪深误差，利用2000-2020年的地面实测雪深进行评估。验证结果表明，三江源地区的5 km雪深的RMSE位于8~8.5cm、MAE位于5.6~6.5 cm，R大于0.7,相较于中国长时间序列的雪深数据（25 km）相比（RMSE位于10~11.5 cm、MAE位于7.5~8.3 cm，R大于0.45）具有更优的精度。结果表明该反演的5 km雪深在三江源地区地区具有良好的精度。因此，转化得到的雪水当量数据集可作为评价该地区积雪资源的可靠数据基础。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "三江源", "ds_acq_lon_east": 103.0, "ds_acq_lat_south": 31.0, "ds_acq_lon_west": 88.0, "ds_acq_lat_north": 38.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 72853631, "ds_files_count": 14977, "ds_format": "Tiff", "ds_space_res": "5000m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "83dac452-a702-47a6-92e9-6cda097bb863.png", "ds_thumb_from": 0, "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" ], "quality_level": 3, "publish_time": "1-01-01 15:15:24", "last_updated": "2025-04-24 15:45:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER-SNOW.DB6815.2025", "i18n": { "en": { "title": "Long-term daily snow water equivalent dataset for the Three-River Source region from 1980 to 2020", "ds_format": "Tiff", "ds_source": "
（1）The Calibrated Enhanced Resolution Brightness Temperature (CETB) data are provided by the National Snow and Ice Data Center (https://nsidc.org/data/NSIDC-0630/versions/1). The Calibrated Enhanced Resolution Brightness Temperature (CETB) data is provided by the National Snow and Ice Data Center. This data covers observed bright temperature data from different satellites since 1978 with a temporal resolution of 1d and a spatial resolution of 6.25 km/3.125 km.
（2）The snow area ratio data were obtained from the literature https://www.sciencedirect.com/science/article/pii/ S0924271624003265, which has a temporal resolution of 1 d and a spatial resolution of 5 km. Snow cover days data were obtained from the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn), which has a temporal resolution of 1 d and a spatial resolution of 500 m.
（3）The DEM data were obtained from the National Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA). ) and the Shuttle Radar Topography Mapping Mission (SRTM) operated by the National Aeronautics and Space Administration (NASA) (http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp), with a spatial resolution of 90 m.
（4）Land-use type data were derived from the MCD12Q1 V061 dataset ( https://earthexplorer.usgs.gov/), using the annual land cover types of its IGBP classification standard, which has a temporal resolution of 1 yr and a spatial resolution of 500 m. The land use type data are available from the MCD12Q1 V061 dataset (https://earthexplorer.usgs.gov/).
（5） Snow density data sourced from the National Cryosphere Desert Data Center（ http://www.ncdc.ac.cn ）The grid data set of monthly and multi-year average snow cover density on the Qinghai Tibet Plateau includes 12 snow cover density maps of 5 km per month.", "ds_quality": "
The data quality is well, as the snow water equivalent mainly comes from the conversion of snow depth through monthly average snow density, and the accuracy of snow depth has been empirically proven to be good: three standard indicators, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R), are used to represent the snow depth error, and ground measured snow depth from 2000 to 2020 is used for evaluation. The verification results indicate that the RMSE and MAE of the 5 km snow depth in the Three-River Source Region are located between 8-8.5 cm and 5.6-6.5 cm, respectively, with an R greater than 0.7. Compared with the long-term snow depth data in China (25 km), the RMSE is located between 10-11.5 cm, MAE is located between 7.5-8.3 cm, and R is greater than 0.45, which has better accuracy. The results indicate that the 5 km snow depth obtained from the inversion has good accuracy in the Three-River Source region. Therefore, the transformed snow water equivalent dataset can serve as a reliable data basis for evaluating snow resources in the region.", "ds_ref_way": "", "ds_abstract": "
High spatial resolution snow water equivalent (SWE) is critical for hydrological, ecological, and disaster research. However, passive microwave SWE products (10/25 km) with coarse spatial resolution can no longer meet modern demands for high precision and fine resolution. This study integrated newly calibrated enhanced-resolution brightness temperature data with optical snow area fraction and snow cover days, employing the deep learning FT-Transformer model to retrieve daily snow depth data at 5 km spatial resolution during the snow cover period (October to April) in the Three-River Source Region. The snow depth was subsequently converted into 5 km spatial resolution SWE data using monthly averaged snow density. This work establishes a robust data foundation for snow resource monitoring in the Three-River Source Region.
", "ds_time_res": "日", "ds_acq_place": "The Three-River Source region", "ds_space_res": "5000m", "ds_projection": "", "ds_process_way": "(1) Using the python platform to unify the batch processing of various data sources with a spatial and temporal resolution of 5 km day by day as a way to construct data inputs for snow depth retrieval; (2) Implementing a snow depth retrieval model with multiple data fusion through deep learning model training and parameter optimization; (3) Estimating snow depth data using the training-saved model for the Three-River Source Region; (4) Mask the water body and then fill the orbital gap of the passive microwave radiometer by averaging the day before and after; (5) Multiply snow depth data by snow density to obtain snow water equivalent.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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, "ds_topic_tags": [ "雪水当量", "长时序", "积雪密度", "被动微波" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "三江源" ], "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": "赵子胜", "email": "zhaozisheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李杭璇", "email": "2023521174@link.tyut.edu.cn", "work_for": "太原理工大学", "country": "中国" }, { "true_name": "钟歆玥", "email": "xyzhong@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "吴晓东", "email": "wuxd@lzb.ac.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵子胜", "email": "zhaozisheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }