高空间分辨率雪深对水文、生态和灾害研究至关重要。然而，被动微波雪深产品（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。与中国长时间序列的雪深数据（25 km）相比，反演的5 km雪深具有更优的精度，RMSE通常在8.5 cm以下。为三江源的积雪资源监测提供了可靠的数据基础。
", "ds_source": "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1 d，空间分辨率为6.25 km/3.125 km。
\n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。
\nDEM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。
\n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。
", "ds_process_way": "（1）利用python平台统一批量处理各种数据源的时空分辨率为逐日5 km，以此构建雪深反演的数据输入；
\n（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；
\n（3）使用训练保存的模型反演三江源雪深数据；
\n（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。
", "ds_quality": "采用均方根误差（RMSE）、平均绝对误差（MAE）和相关系数（R）三个标准指标表示反演的雪深误差，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，本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": 249041166, "ds_files_count": 8488, "ds_format": "Tiff", "ds_space_res": "5000m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "8095027c-03a7-4c6c-9a46-21e024e45374.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" ], "quality_level": 3, "publish_time": "1-01-01 15:15:33", "last_updated": "2025-04-24 16:12:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER-SNOW.DB6816.2025", "i18n": { "en": { "title": "Daily Snow Depth Dataset for the Three-River Source Region (1980–2020)", "ds_format": "Tiff", "ds_source": "（1）Calibrated Enhanced-Resolution Brightness Temperature (CETB): Provided by the National Snow and Ice Data Center (NSIDC, https://nsidc.org/data/NSIDC-0630/versions/1). Covers satellite-observed brightness temperatures since 1978, with 1-day temporal resolution and 6.25 km/3.125 km spatial resolution.
\n(2)Snow Cover Fraction (SCF): Sourced from https://www.sciencedirect.com/science/article/pii/S0924271624003265, with 1-day temporal and 5 km spatial resolution.
\n（3）Snow-Covered Days (SCD): Obtained from the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn), with 1-day temporal and 500 m spatial resolution.\n
\n（4）DEM: Derived from the Shuttle Radar Topography Mission (SRTM, http://srtm.csi.cgiar.org), 90 m spatial resolution.
\n（5）Land Use: Based on the MCD12Q1 V061 dataset (https://earthexplorer.usgs.gov/), using IGBP annual land cover classification (500 m spatial, 1-year temporal resolution).
", "ds_quality": "Three standard indicators, root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R), were used to represent the snow depth error of the inversion, which was evaluated using the ground-truthed snow depths from 2000 to 2020. The validation results show that the RMSE of 5 km snow depth in the Three-River Source Region is in the range of 8-8.5 cm, the MAE is in the range of 5.6-6.5 cm, and the R is greater than 0.7, which is a better accuracy than that of the long time series of snow depth data (25 km) in China (the RMSE is in the range of 10-11.5 cm, the MAE is in the range of 7.5-8.3 cm, and the R is greater than 0.45). The results indicate that the 5 km snow depth of this inversion has good accuracy in the Three-River Source Region. Therefore, this dataset can be used as a reliable data base for evaluating the snow resources in this region.
", "ds_ref_way": "", "ds_abstract": "High-spatial-resolution snow depth data are critical for hydrological, ecological, and disaster research. However, passive microwave snow depth products (10/25 km) no longer meet modern demands for high precision and resolution. This study integrates newly calibrated Enhanced-Resolution Brightness Temperature (CETB) with optical snow cover fraction (SCF) and snow-covered days (SCD) data. Using a deep learning FT-Transformer model, we inverted daily snow depth data at 5 km spatial resolution during snow seasons (October–April) for the Three-River Source region. Compared to China’s long-term snow depth data (25 km), the 5 km snow depth product demonstrates superior accuracy, with RMSE typically below 8.5 cm, providing a reliable foundation for snow resource monitoring in the region.
", "ds_time_res": "日", "ds_acq_place": "The Three-River Source region", "ds_space_res": "5000m", "ds_projection": "", "ds_process_way": "(1) Unified batch processing of various data sources with a temporal and spatial resolution of 5 km day by day using the python platform is used to construct the data input for snow depth inversion;
\n(2) The snow depth inversion model with multiple data fusion is realized by deep learning model training and parameter optimization;
\n(3) Inversion of snow depth data from Sanjiangyuan using the training-saved model;
\n(4) Performing a water body mask and then filling the passive microwave radiometer track gap by averaging before and after the day.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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": [ "深度学习", "被动微波", "雪深", "长时序" ], "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" }