雪深（Snow Depth，SD）是表征积雪状态的重要物理参数，也是雪水文过程、气候调节及水资源管理研究的关键基础数据。针对青藏高原复杂地形条件下传统被动微波雪深产品空间分辨率低的问题，基于多源遥感数据融合与降尺度混合技术，综合考虑积雪覆盖范围、地形因子和地理位置等影响雪深空间分布的关键环境变量，研制了1980-2022年逐日500 m空间分辨率的精细化雪深数据集。\n
采用空间代表性较高的地面气象观测站点数据对数据产品进行了系统精度验证，RMSE为2.75 cm，较原始被动微波雪深产品精度提升了33.9%，并显著改善了对浅雪高估。\n
该数据集以GeoTIFF格式存储，时间分辨率为逐日，空间分辨率为500 m，时间范围为1980-2022年。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 105.01611111111112, "ds_acq_lat_south": 26.0, "ds_acq_lon_west": 66.99972222222222, "ds_acq_lat_north": 46.00388888888889, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 104815720188, "ds_files_count": 0, "ds_format": "GeoTIFF", "ds_space_res": "500米", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "edf32887-3738-4586-907c-510dd5e2bc8f.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "None", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-07-13 09:53:29", "last_updated": "2026-07-13 09:53:29", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.snow.db7636.2026", "i18n": { "en": { "title": "Daily 500 m snow depth product in Tibetan Plateau from 1980 to 2022", "ds_format": "GeoTIFF", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
Snow depth (SD) is a key physical parameter describing snow cover conditions and provides essential information for snow hydrology, climate regulation, and water resource management. To overcome the coarse spatial resolution of conventional passive microwave snow depth products over the complex terrain of the Tibetan Plateau, a daily 500 m snow depth dataset was developed spanning 1980–2022 using multi-source remote sensing data fusion and a hybrid downscaling approach. The downscaling model incorporates key environmental variables controlling snow depth distribution, including snow cover extent, topographic factors, and geographic location.\r\n
The dataset was systematically validated using ground-based meteorological observations with high spatial representativeness. It achieved a root mean square error (RMSE) of 2.75 cm, representing a 33.9% improvement over the original passive microwave product, while substantially reducing the overestimation of shallow snow.\r\n
The dataset is provided in GeoTIFF format with a daily temporal resolution, 500 m spatial resolution, and complete coverage for 1980–2022.", "ds_time_res": "", "ds_acq_place": "Qinghai-Tibet Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "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": [ "雪深", "青藏高原" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "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, 2021, 2022 ], "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": "马英", "email": "yma2019@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "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": "马英", "email": "yma2019@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" }