本数据集提供了1982年至2015年覆盖北极陆地地区的月尺度地表反照率数据。该数据集基于多源卫星反照率产品的集合平均方法构建，以提升时空覆盖度与数据稳健性。所融合的主流反照率产品包括：MCD43A3（MODIS）：500米/8天分辨率，基于BRDF模型反演；GLASS AVHRR：0.05°/8天，通过多源融合与时空滤波优化；CLARA-A3（AVHRR-based）：0.25°/逐月，适用于高纬度地区长期分析。所有输入数据经重投影与双线性插值统一至10 km EASE-Grid 空间格网，并采用算术平均法生成集合月值产品，以降低单一传感器偏差并填补缺值。为支持反照率分析与验证，本数据集同步提供了经空间对齐的ERA5-Land再分析变量，以及北极环极植被图（CAVM） 的土地覆盖与土壤属性信息。数据集精度通过FLUXnet通量站点的计算得到的观测地表反照率进行验证。将站点观测的向上与向下短波辐射通量计算为反照率，并与对应像元的集合平均产品进行对比。验证结果表明：站点与像元反照率的相关系数（R）普遍高于 0.80，均方根误差（RMSE）控制在 0.06 以内。", "ds_source": "
本研究构建的北极地表反照率数据集基于三套主流卫星反照率产品融合而成，包括 MODIS MCD43A3（V006）黑空与白空反照率产品、GLASS AVHRR 地表反照率产品以及 CLARA-A3 地表反照率产品；同时整合了 ERA5-Land 再分析数据提供的气温、积雪覆盖率与土壤水分等环境变量，以及北极环极植被图（CAVM）中的土地覆盖与土壤属性信息；数据验证依托 FLUXnet2015 通量站点观测的短波辐射数据，涵盖 US-Brw、FI-Sod、RU-Che 等典型北极站点。", "ds_process_way": "
北极地表反照率数据集通过系统化的数据处理流程生成：首先将三套源反照率产品（MODIS MCD43A3、GLASS AVHRR 和 CLARA-A3）通过双线性重采样方法统一重投影至一致的10 km EASE-Grid 2.0坐标系，并采用时间插值方法生成连续的月度合成数据。随后通过像元级算术平均融合这些对齐的数据集以生成集合反照率产品，并基于时间一致性检验和跨产品偏差阈值进行异常值剔除。ERA5-Land和CAVM的辅助变量经空间匹配至同一网格，验证环节则通过FLUXnet2015站点观测数据与对应像元计算反照率值的直接比对实现。", "ds_quality": "
本数据集通过多源卫星产品融合与系统质量控制，具备良好的数据完整性与可靠性。1982–2015年月尺度覆盖完整度达98%以上，所有数据统一重采样至10 km EASE-Grid 2.0网格，时空一致性处理有效抑制了传感器差异与时序突变。基于FLUXnet2015北极站点观测的验证显示，反照率数据与实测值的相关系数平均为0.85，均方根误差控制在0.06以内，平均偏差接近零。通过多产品集合平均与时空滤波，降低了云污染、冰雪反照率饱和及尺度效应带来的不确定性。", "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": 3110451953, "ds_files_count": 410, "ds_format": ".tif", "ds_space_res": "100000", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "697a26d3-0dc2-4769-821b-0f4adb0dcfff.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": 3, "publish_time": "2026-05-13 16:28:11", "last_updated": "2026-05-13 16:28:11", "protected": false, "protected_to": "2028-03-13 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7147.2026", "i18n": { "en": { "title": "Arctic Land Surface Albedo Dataset (1982–2015)", "ds_format": ".tif", "ds_source": "
The Arctic surface albedo dataset constructed in this study is based on the fusion of three mainstream satellite albedo products, including MODIS MCD43A3 (V006) black and white sky albedo product, GLASS AVHRR surface albedo product, and CLARA-A3 surface albedo product; Simultaneously integrating environmental variables such as temperature, snow cover, and soil moisture provided by ERA5 Land reanalysis data, as well as land cover and soil attribute information from the Arctic Circumpolar Vegetation Map (CAVM); The data validation relies on the shortwave radiation data observed by FLUXNet2015 flux stations, covering typical Arctic stations such as US Brw, FI Sad, RU Che, etc.", "ds_quality": "
This dataset has good data integrity and reliability through multi-source satellite product fusion and system quality control. From 1982 to 2015, the coverage integrity of the scale reached over 98%, and all data were uniformly resampled to a 10 km EASE Grid 2.0 grid. The spatiotemporal consistency processing effectively suppressed sensor differences and temporal mutations. Based on the verification of FLUXNet2015 Arctic station observations, the average correlation coefficient between albedo data and measured values is 0.85, the root mean square error is controlled within 0.06, and the average deviation is close to zero. By using multi product set averaging and spatiotemporal filtering, the uncertainty caused by cloud pollution, ice and snow albedo saturation, and scale effects has been reduced.", "ds_ref_way": "", "ds_abstract": "
This dataset provides monthly scale surface albedo data covering the Arctic land region from 1982 to 2015. This dataset is constructed based on the ensemble averaging method of multi-source satellite albedo products to improve spatiotemporal coverage and data robustness. The mainstream albedo products integrated include: MCD43A3 (MODIS): resolution of 500 meters/8 days, based on BRDF model inversion; GLASS AVHRR: 0.05 °/8 days, optimized through multi-source fusion and spatiotemporal filtering; CLARA-A3 (AVHRR based): 0.25 °/month, suitable for long-term analysis in high latitude regions. All input data are re projected and bilinear interpolated to a 10 km EASE Grid spatial grid, and the arithmetic mean method is used to generate a set of monthly value products to reduce single sensor bias and fill missing values. To support albedo analysis and validation, this dataset synchronously provides spatially aligned ERA5 Land reanalysis variables, as well as land cover and soil attribute information from the Arctic Circumpolar Vegetation Map (CAVM). The accuracy of the dataset was validated by calculating the observed surface albedo using FLUXNet flux stations. Calculate the upward and downward shortwave radiation flux observed at the station as albedo and compare it with the ensemble average product of the corresponding pixels. The verification results indicate that the correlation coefficient (R) between the site and pixel albedo is generally higher than 0.80, and the root mean square error (RMSE) is controlled within 0.06.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The Arctic surface albedo dataset is generated through a systematic data processing process: first, three sets of source albedo products (MODIS MCD43A3, GLASS AVHRR, and CLARA-A3) are uniformly re projected to a consistent 10 km EASE-Grid 2.0 coordinate system using bilinear resampling method, and continuous monthly composite data is generated using time interpolation method. Subsequently, these aligned datasets are fused using pixel level arithmetic mean to generate a set albedo product, and outlier removal is performed based on time consistency testing and cross product deviation threshold. The auxiliary variables of ERA5 Land and CAVM are spatially matched to the same grid, and the verification process is achieved by directly comparing the observed data of FLUXNet2015 station with the calculated albedo values of corresponding pixels.", "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": [ "长时序", "地表反照率", "GLASS", "ERA5" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极" ], "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" }