雪深（Snow Depth，SD）是表征积雪厚度的关键参数，对理解区域水循环、能量平衡及气候变化影响具有重要意义。针对现有遥感、再分析和模拟的积雪深度产品存在较大不确定性，以及在复杂地形区域精度不足等问题。本项目利用随机森林算法，结合AMSR-E，AMSR2，NHSD和GlobSnow、ERA-Interim、MERRA2等雪深产品和相关环境因子变量，用地面观测站点的实测雪深数据训练与验证模型，最终融合生成1980-2019年北极（北纬66°34以北）空间分辨率为0.25°的日尺度雪深数据产品。通过实测雪深数据验证发现，其相关系数（R2）达0.79，均方根误差（RMSE）和平均绝对误差（MAE）分别为8.5cm 和3.5cm。本数据集可为北极水文模拟、陆面过程模型的数据同化提供重要的数据支撑。", "ds_source": "
AMSRE 是由美国国家航空航天局（NASA）地球观测系统的Aqua卫星和日本宇宙航空研究开发机构（JAXA）的GCOM-W1卫星上的微波扫描辐射计收集的数据 (https://nsidc.org/data/ae_dysno)，来自NASA的AMSRE（AMSR-E）提供从2002年6月19日到2011年10月3日的雪深数据集，而来自JAXA的AMSRE（AMSR2）则从2012 年7月2日至今持续提供全球每日雪深数据。GlobSnow 受欧空局资助，其雪深产品是一个协同数据集，它利用先进的数据同化方案，将卫星被动微波数据（来自SSM/I和AMSR-E等传感器）与地面气象站的雪深观测数据相结合(https://www.globsnow.info/)，但9月部分数据存在缺失。NHSD数据是国家青藏高原科学数据中心（https://poles.tpdc.ac.cn/） 提供的北半球长时间序列雪深数据产品。ERA5-Interm是欧洲是中期天气预报中心生产的全球陆面再分析数据集（https://apps.ecmwf.int/datasets/data/interim-full-daily/），为日值数据。MERRA-2是 NASA全球建模与同化办公室发布的再分析数据（https://disc.gsfc.nasa.gov/datasets/）。", "ds_process_way": "
通过重采样，将AMSR-E，AMSR2，NHSD和GlobSnow、ERA-Interim、MERRA2等数据集的空间分辨率统一为0.25°。采用随机森林算法，通过融合以上数据产品、环境因子，借助实测雪深数据进行模型训练和验证，生成一套时序更完整的积雪深度数据集。", "ds_quality": "
基于实测站点数据集，采用均方根误差（RMSE）、平均绝对误差（MAE）、决定系数（R2）和偏差四个指标评估积雪深度数据的精度。数据融合过程中，为确保时空一致性，将验证范围限定在北纬66°34以上地区，时间序列为1980-2019年。数据集验证采用实测站点数据进行验证。", "ds_acq_start_time": "1980-09-01 00:00:00", "ds_acq_end_time": "2019-05-31 00:00:00", "ds_acq_place": "北极", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 66.5, "ds_acq_lon_west": null, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 5600030692, "ds_files_count": 10657, "ds_format": "*.tif", "ds_space_res": "0.25°", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "cecc11cd-9b6d-4abb-9bca-6b078a366aec.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "53943799-d453-4bf2-a141-56c205c1355b", "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": 0, "publish_time": "2026-05-07 11:48:53", "last_updated": "2026-05-20 09:43:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7130.2026", "i18n": { "en": { "title": "Daily snow depth dataset on the Arctic (1980-2019)", "ds_format": "*.tif", "ds_source": "
AMSRE data are collected by the microwave scanning radiometer onboard NASA's Aqua satellite (Earth Observing System) and JAXA's GCOM-W1 satellite (https://nsidc.org/data/ae_dysno). NASA's AMSRE (AMSR-E) provides snow depth datasets from June 19, 2002, to October 3, 2011, while JAXA's AMSRE (AMSR2) has been continuously providing global daily snow depth data since July 2, 2012. Funded by the European Space Agency, the GlobSnow snow depth product is a collaborative dataset that utilizes an advanced data assimilation scheme to combine satellite passive microwave data (from sensors such as SSM/I and AMSR-E) with in-situ snow depth observations from meteorological stations (https://www.globsnow.info/), though partial data are missing for September. The NHSD dataset is a long-term Northern Hemisphere snow depth product provided by the National Tibetan Plateau Data Center (https://poles.tpdc.ac.cn/). ERA5-Interim is a global land surface reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (https://apps.ecmwf.int/datasets/data/interim-full-daily/) and provides daily data. MERRA-2 is a reanalysis dataset released by NASA's Global Modeling and Assimilation Office (https://disc.gsfc.nasa.gov/datasets/).", "ds_quality": "
We calculated the error metrics to evaluate the accuracy by comparing SWE dataset and datesets with observations. The root meansquare error (RMSE), mean absolute error (MAE), Pearson correlation coefficient (R) and bias were adopted to assess theaccuracy of the SD dataset. To ensure spatiotemporal consistency during the data fusion process, the validation scope was confined to regions north of 66°34'N. The time series was defined during 1980 to 2019. Moreover, to validate the accuracy in spatial of SD dataset, cross-validation was performed using observations.", "ds_ref_way": "", "ds_abstract": "
Snow Depth (SD) is a key parameter for characterizing snow thickness, playing a significant role in understanding regional water cycles, energy balance, and the impacts of climate change. To address the substantial uncertainties in existing remote sensing, reanalysis, and simulated snow depth products, as well as their insufficient accuracy in complex terrain regions, this project employs a random forest algorithm. It integrates snow depth products such as AMSR-E, AMSR2, NHSD, and GlobSnow, along with reanalysis datasets like ERA-Interim and MERRA2, and relevant environmental variables. Using ground-based observational snow depth data for model training and validation, a daily-scale SD product with a spatial resolution of 0.25° for the Arctic (north of 66°34'N) from 1980 to 2019 was generated through data fusion. Validation with measured snow depth data shows a correlation coefficient (R²) of 0.79, with a root mean square error (RMSE) of 8.5 cm and a mean absolute error (MAE) of 3.5 cm. This dataset provides crucial data support for hydrological modeling and data assimilation in land surface process models in the Arctic.", "ds_time_res": "", "ds_acq_place": "Arctic", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The spatial resolution of datasets including AMSR-E, AMSR2, NHSD, GlobSnow, ERA-Interim, and MERRA2 was resampled to 0.25°. Using the random forest algorithm, a more temporally complete snow depth dataset was generated by integrating the aforementioned data products and environmental factors, along with model training and validation based on in-situ snow depth observations.", "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", "长序列", "随机森林", "数据融合", "精度评估" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北纬66°34′以北" ], "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 ], "ds_contributors": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "ds_managers": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "category": "积雪" }