地表反照率反馈效应（SAF）是驱动极地增暖的关键机制之一。利用多源遥感与再分析资料，构建了覆盖 1980–2014 年北极陆地地区的 SAF 数据集。数据源包括 GLASS 第二代反照率产品（0.05°/8 天）、GIMMS3g NDVI（约 8 km/15 天）、ERA5-Land 再分析产品（9 km，涵盖气温、积雪、土壤水分与类型）以及北极环极植被图（提供土地覆盖和土壤基质化学特征）。所有数据经重投影与插值统一至 10 km EASE-Grid 格网，确保时空一致性。基于经典 SAF 框架，通过地表反照率相对变化与近地气温变化率之比计算月尺度 SAF 强度。精度评估结果显示：GLASS 反照率与地面及 MODIS 数据相关系数普遍高于 0.85，均方根误差小于 0.05；NDVI 数据在多源比对中保持良好时序稳定性；ERA5-Land 气温与积雪数据相较观测资料偏差低于 5%。整体表明该数据集具备较高的时空一致性与可靠性。", "ds_source": "
利用多源遥感与再分析数据，构建了覆盖 1982–2014 年北极陆地地区的地表反照率反馈效应数据集。主要数据源包括：（1）GLASS 第二代地表反照率产品，空间分辨率 0.05°，时间分辨率 8 天，提供长期连续的短波宽带黑天顶反照率信息；（2）GIMMS3g NDVI 数据集，空间分辨率约 8 km，时间分辨率 15 天，经最大值合成处理获得逐月植被指数；（3）ERA5-Land 再分析产品，提供 2 米气温、积雪覆盖率、土壤含水量及土壤类型等关键陆面参数，空间分辨率提升至 9 km；（4）北极环极植被图（CAVM），用于获取土地覆盖类型和土壤基质化学特征。", "ds_process_way": "
为了保证多源数据的一致性与可比性，本研究在数据处理环节开展了统一化预处理。首先，将 GLASS 反照率、GIMMS3g NDVI、ERA5-Land 陆面参数及 CAVM 植被数据进行投影转换，并重采样至 25 km 的 EASE-Grid 格网，这是极区研究中常用的等面积格网，可避免高纬度区域面积畸变。反照率数据由 0.05° 分辨率经最近邻插值后，采用块均值方法聚合到 25 km 格网；NDVI 数据则通过最大值合成（MVC）生成逐月时间序列，以减弱云和大气影响。ERA5-Land 的气温、积雪覆盖率、土壤水分和土壤类型等数据，通过双线性插值统一到相同空间分辨率，并与其他数据对齐。随后，基于时间序列差分法计算逐月反照率变化与气温变化，用于估算 SAF 强度。最后，结合最优参数地理探测器（OPGD）模型，对不同因子（积雪、植被、土地覆盖、土壤类型与水分）的空间作用力和交互效应进行量化，形成完整的 SAF 时空格局数据集。", "ds_quality": "
在数据集构建过程中高度重视质量控制与精度评估。首先，GLASS 反照率产品在生成过程中已融合 MODIS 与 AVHRR 数据，并通过多源滤波和填补算法降低了云和缺测带来的不确定性。与地面观测及 MODIS 产品对比验证表明，其相关系数普遍在 0.80 以上，均方根误差控制在 0.05 以内，能够较好反映高纬度地区的季节性变化。其次，GIMMS3g NDVI 数据经过传感器间校正与轨道漂移修正，具有较好的长期稳定性和可比性，经与其他遥感植被指数（如 MODIS NDVI）交叉比对，时序一致性显著。ERA5-Land 的气温、积雪覆盖率与土壤水分数据在空间分辨率（9 km）与再分析模式物理方案上均较 ERA-Interim 有显著改进，其与北极气象站点对比的平均偏差小于 5%。此外，所有数据在处理过程中均经过统一的投影与重采样，确保了多源数据间的空间一致性。综合来看，构建的数据集在时间连续性、空间精度与多源一致性方面均表现良好，可为地表反照率反馈效应研究提供可靠的数据支撑。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2014-12-31 00:00:00", "ds_acq_place": "环北极陆地", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 30.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 2314267640, "ds_files_count": 2097, "ds_format": ".tif", "ds_space_res": "10000", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "f865a3ee-bcf5-4151-b6c8-443f0a5a90d7.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 16:23:33", "last_updated": "2026-05-13 16:23:33", "protected": false, "protected_to": "2028-03-13 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7146.2026", "i18n": { "en": { "title": "Arctic Land Surface Albedo Feedback Dataset (1980–2014)", "ds_format": ".tif", "ds_source": "
A surface albedo feedback effect dataset covering the Arctic land region from 1982 to 2014 was constructed using multi-source remote sensing and reanalysis data. The main data sources include: (1) GLASS second-generation surface albedo product, with a spatial resolution of 0.05 ° and a temporal resolution of 8 days, providing long-term continuous shortwave broadband black zenith albedo information; (2) The GIMMS3g NDVI dataset has a spatial resolution of approximately 8 km and a temporal resolution of 15 days. Monthly vegetation indices are obtained through maximum value synthesis processing; (3) ERA5 Land reanalysis product provides key land surface parameters such as 2-meter temperature, snow cover, soil moisture content, and soil type, with a spatial resolution increased to 9 km; (4) Arctic Circumpolar Vegetation Map (CAVM) is used to obtain land cover types and soil matrix chemical characteristics.", "ds_quality": "
We attach great importance to quality control and accuracy evaluation during the dataset construction process. Firstly, the GLASS albedo product has integrated MODIS and AVHRR data during the generation process, and reduced the uncertainty caused by cloud and missing measurements through multi-source filtering and imputation algorithms. Comparison and verification with ground observations and MODIS products show that the correlation coefficient is generally above 0.80, and the root mean square error is controlled within 0.05, which can better reflect the seasonal changes in high latitude regions. Secondly, the GIMMS3g NDVI data has been calibrated between sensors and corrected for orbital drift, demonstrating good long-term stability and comparability. Cross comparison with other remote sensing vegetation indices such as MODIS NDVI shows significant temporal consistency. The temperature, snow cover, and soil moisture data of ERA5 Land have significantly improved in spatial resolution (9 km) and reanalysis model physical scheme compared to ERA Interim, with an average deviation of less than 5% compared to Arctic meteorological stations. In addition, all data undergoes unified projection and resampling during the processing, ensuring spatial consistency between multi-source data. Overall, the constructed dataset performs well in terms of temporal continuity, spatial accuracy, and multi-source consistency, providing reliable data support for the study of surface albedo feedback effects.", "ds_ref_way": "", "ds_abstract": "
The surface albedo feedback effect (SAF) is one of the key mechanisms driving polar warming. A SAF dataset covering the Arctic land region from 1980 to 2014 was constructed using multi-source remote sensing and reanalysis data. The data sources include GLASS second-generation albedo product (0.05 °/8 days), GIMMS3g NDVI (approximately 8 km/15 days), ERA5 Land reanalysis product (9 km, covering temperature, snow cover, soil moisture and types), and Arctic Circumpolar Vegetation Map (providing land cover and soil matrix chemical characteristics). All data were re projected and interpolated to a 10 km EASE Grid grid to ensure spatiotemporal consistency. Based on the classical SAF framework, the monthly scale SAF intensity is calculated by the ratio of the relative change in surface albedo to the rate of change in near earth temperature. The accuracy evaluation results show that the correlation coefficient between GLASS albedo and ground and MODIS data is generally higher than 0.85, with a root mean square error of less than 0.05; NDVI data maintains good temporal stability in multi-source comparison; ERA5 Land temperature and snow data have a deviation of less than 5% compared to observational data. Overall, it indicates that the dataset has high spatiotemporal consistency and reliability.", "ds_time_res": "", "ds_acq_place": "Circumpolar Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
In order to ensure the consistency and comparability of multi-source data, this study carried out unified preprocessing in the data processing stage. Firstly, the GLASS albedo, GIMMS3g NDVI, ERA5 Land land surface parameters, and CAVM vegetation data were projected and converted, and resampled to a 25 km EASE Grid grid. This is a commonly used equal area grid in polar research, which can avoid area distortion in high latitude regions. The albedo data is aggregated into a 25 km grid using the block mean method after nearest neighbor interpolation at a resolution of 0.05 °; NDVI data is generated monthly time series through Maximum Value Synthesis (MVC) to mitigate the impact of clouds and atmosphere. ERA5 Land's temperature, snow cover, soil moisture, and soil type data are unified to the same spatial resolution through bilinear interpolation and aligned with other data. Subsequently, based on the time series difference method, monthly changes in albedo and temperature were calculated to estimate SAF intensity. Finally, by combining the Optimal Parameter Geographic Detector (OPGD) model, the spatial forces and interaction effects of different factors (snow cover, vegetation, land cover, soil type, and moisture) were quantified to form a complete SAF spatiotemporal pattern dataset.", "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", "温度效应", "GLASS", "ERA5" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极" ], "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 ], "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" }