本数据集提供了1982年至2015年覆盖北极陆地地区（北纬66°以北）的逐月积雪覆盖度（Snow Cover Fraction, SCF）产品，空间分辨率为10 km，采用等积可扩展地球网格（EASE-Grid 2.0）投影。数据基于多源卫星遥感资料融合生成，包括AVHRR、MODIS及被动微波遥感数据，并结合ERA5-Land再分析数据进行时序一致性与空间完整性的优化。数据集采用改进的非线性光谱混合分析模型与多传感器协同反演算法，有效解决高纬度地区云覆盖、冰雪混杂及极夜条件下光学遥感数据缺失的问题。在质量控制方面，通过交叉验证与地面站点观测对比，积雪覆盖度反演精度在不同地表类型下均方根误差（RMSE）控制在8-12%，平均绝对误差（MAE）低于6%，尤其在苔原与寒漠区域表现稳健。数据格式为GeoTIFF，包含月度积雪覆盖度（0-100%）。本数据集适用于北极陆表过程研究、气候模式验证、水文模型驱动及冰冻圈变化监测等领域，为理解北极季节性积雪动态及其对气候反馈提供可靠的长时序空间数据支持。", "ds_source": "
本数据集融合了多源卫星遥感与再分析数据，具体包括：AVHRR 可见光近红外数据，用于提取早期光学积雪信息；MODIS双星（Terra/Aqua）月合成积雪产品，提供高精度NDSI反演结果；SSM/I与SSMIS被动微波亮温数据，通过梯度比值算法补充云下积雪监测；以及ERA5-Land再分析数据中的地表温度与雪水当量变量，用于多源协同校准与缺失值重建。所有数据经时空匹配与投影转换统一至10 km EASE-Grid 2.0网格系统，形成连续一致的月尺度积雪覆盖度序列。", "ds_process_way": "
本数据集通过系统化的多源数据融合与时空重建流程生成。首先对各源数据实施预处理：AVHRR数据完成辐射定标、大气校正与云掩蔽；MODIS积雪产品进行双星融合与NDSI动态阈值提取；被动微波数据通过改进型梯度比值算法反演积雪范围，并区分干湿雪状态；ERA5-Land变量则用于构建雪线温度约束条件。在此基础上，采用基于贝叶斯概率框架的多源协同反演算法，以光学数据为主、微波数据为辅，通过时空加权插值填补云覆盖缺失，并利用再分析数据校正季节性过渡区域的雪盖误判。最终通过自适应滤波平滑与站点观测验证（如俄罗斯雪深观测网络、北美雪图分析系统），输出1982–2015年逐月10 km分辨率北极陆地积雪覆盖度格点数据集。", "ds_quality": "
本数据集通过多层次质量控制体系保障数据的可靠性与适用性。在时空一致性方面，多源数据融合有效解决了单一传感器在北极地区因云覆盖、极夜和复杂地表造成的监测盲区，1982-2015年月尺度空间完整度达95%以上，时序连续性较单一数据源提升约30%。精度验证采用三阶段策略：与全球积雪观测站点（地面实测数据对比显示，积雪覆盖度反演平均绝对误差为7.2%，均方根误差为9.8%；与更高分辨率遥感产品（如Sentinel-2 20m雪盖产品）空间一致性检验中，夏季苔原区吻合度达89%，冬季林雪过渡带为82%。", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2015-12-31 00:00:00", "ds_acq_place": "北极陆地", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 66.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 2918208865, "ds_files_count": 410, "ds_format": "Geotiff", "ds_space_res": "10km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "45fa9378-1722-4b37-ab3f-679cca38db13.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": [ "170.15" ], "quality_level": 3, "publish_time": "2026-05-13 17:07:46", "last_updated": "2026-05-13 17:07:46", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7148.2026", "i18n": { "en": { "title": "Arctic Land Snow Cover Dataset (1982-2015)", "ds_format": "Geotiff", "ds_source": "
This dataset integrates multi-source satellite remote sensing and reanalysis data, including: AVHRR GAC visible and near-infrared data for extracting early-stage optical snow cover information; MODIS dual-satellite (Terra/Aqua) monthly composite snow products, providing high-precision NDSI inversion results; SSM/I and SSMIS passive microwave brightness temperature data, which supplement snow cover monitoring under cloud cover through gradient ratio algorithms; as well as surface temperature and snow water equivalent variables from ERA5-Land reanalysis data, used for multi-source collaborative calibration and missing value reconstruction. All data have been spatially and temporally matched and uniformly projected onto a 10 km EASE-Grid 2.0 system, forming a continuous and consistent monthly snow cover fraction time series.", "ds_quality": "
This dataset ensures reliability and applicability through a multi-tiered quality control system. In terms of spatiotemporal consistency, multi-source data fusion effectively addresses monitoring blind spots in the Arctic region caused by cloud cover, polar night, and complex terrain for single sensors, achieving a monthly spatial completeness of over 95% from 1982 to 2015 and improving temporal continuity by approximately 30% compared to single-source data. A three-stage strategy is employed for accuracy validation: comparisons with global snow observation stations (ground-based measurements show an average absolute error of 7.2% and a root mean square error of 9.8% for snow cover fraction inversion; spatial consistency tests with higher-resolution remote sensing products (such as the Sentinel-2 20m snow cover product) indicate an agreement of 89% in summer tundra areas and 82% in winter forest-snow transition zones.", "ds_ref_way": "", "ds_abstract": "
This dataset provides monthly snow cover fraction (SCF) products covering Arctic land areas (north of 66°N) from 1982 to 2015, with a spatial resolution of 10 km and projected using the Equal-Area Scalable Earth Grid (EASE-Grid 2.0). The data are generated through the fusion of multi-source satellite remote sensing datasets, including AVHRR, MODIS, and passive microwave remote sensing data, and are optimized for temporal consistency and spatial completeness using ERA5-Land reanalysis data. The dataset employs an improved nonlinear spectral mixture analysis model and a multi-sensor collaborative inversion algorithm to effectively address challenges such as cloud cover, snow-ice mixture confusion, and the lack of optical remote sensing data under polar night conditions in high-latitude regions. In terms of quality control, cross-validation and comparison with ground-based station observations show that the snow cover fraction inversion accuracy across different land cover types maintains a root mean square error (RMSE) of 8–12% and a mean absolute error (MAE) below 6%, with particularly robust performance in tundra and cold desert regions. The data are provided in GeoTIFF format and include monthly snow cover fraction (0–100%), a quality control identifier layer, and auxiliary environmental variables (such as surface temperature and snow albedo). This dataset is suitable for research on Arctic land surface processes, climate model validation, hydrological model forcing, and cryosphere change monitoring, providing reliable long-term spatial data support for understanding Arctic seasonal snow dynamics and their climate feedbacks.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This dataset is generated through a systematic multi-source data fusion and spatiotemporal reconstruction process. First, preprocessing is applied to each source dataset: AVHRR data undergo radiometric calibration, atmospheric correction, and cloud masking; MODIS snow products are subjected to dual-satellite fusion and dynamic NDSI threshold extraction; passive microwave data are processed using an improved gradient ratio algorithm to invert snow cover extent and distinguish between dry and wet snow conditions; and ERA5-Land variables are utilized to construct snowline temperature constraints. Building on this, a multi-source collaborative inversion algorithm based on a Bayesian probability framework is employed, prioritizing optical data with microwave data as supplementary. Spatiotemporally weighted interpolation is applied to fill gaps caused by cloud cover, and reanalysis data are used to correct snow cover misclassification in seasonal transition zones. Finally, adaptive filtering smoothing and validation against station observations (such as the Russian Snow Survey Network and the North American Snow Analysis System) are conducted to produce a gridded dataset of monthly Arctic land snow cover fraction at 10 km resolution for the period 1982–2015.", "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": [ "北极", "多源数据", "陆地", "积雪覆盖" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极陆地" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "俞琳飞", "email": "yulf.20b@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" }, { "true_name": "冷国勇", "email": "lenggy@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "俞琳飞", "email": "yulf.20b@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "俞琳飞", "email": "yulf.20b@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "极地" }