本数据集致力于填补北极植被变绿对区域增温反馈效应量化研究中的空白，数据核心基于对1982年至2015年间北极地区陆地表面和大气变量的遥感产品和再分析数据的综合处理。主要输入包括：植被指数（NDVI）（采用GIMMS3g、PKU GIMMS等多个产品以确保鲁棒性）、近地表气温（TEM）（采用CRU TS、UDEL、CHGN的多产品平均值，以降低单一数据源不确定性）、地表反照率、蒸散发和水汽（主要采用ERA5、MERRA-2等再分析产品）。所有数据均统一重采样至10 km EASE-Grid 网格。数据的精度评估采用了与 FLUXNET 2015 北极地区通量站观测数据进行对比验证的方法，通过计算相关系数（CC）和相对偏差（RB）来确保输入数据的可靠性。其中，多产品平均气温被证实具有最高的准确性（最低相对偏差-10.36%）。", "ds_source": "
植被指数（NDVI）（采用GIMMS3g、PKU GIMMS等多个产品以确保鲁棒性）、近地表气温（TEM）（采用CRU TS、UDEL、CHGN的多产品平均值，以降低单一数据源不确定性）、地表反照率、蒸散发和水汽（主要采用ERA5、MERRA-2等再分析产品）。", "ds_process_way": "
本数据集的数据加工和模型方法旨在建立一个统一的、知识驱动的数据分析框架，以精确解构北极植被变绿对气温的反馈效应。数据首先经历了严格的时空统一化，将1982年至2015年间的多源遥感产品和再分析数据（包括多套NDVI、气温、反照率、蒸散发和水汽）统一重采样至 10 km EASE-Grid 网格的月度分辨率，并通过计算多产品平均值来增强关键输入数据的稳健性。在去趋势的基础上，构建经验统计分析模型，在格点尺度上计算植被温度反馈效应。", "ds_quality": "
本数据集为确保植被变绿温度反馈效应计算结果的科学可靠性，采用了多重严格的精度控制与评估机制。首先，在数据准备阶段，通过对关键变量（如NDVI和气温）进行多产品集成，即计算多种遥感和再分析数据集的平均值作为输入，有效降低了单一数据源的系统性偏差和不确定性，增强了数据的鲁棒性。其次，核心精度评估依赖于与外部独立观测的对比验证：研究使用FLUXNET 2015网络中北极地区的11个通量站的实测月度观测数据作为基准，对所有格网化的气温、潜热通量和短波辐射等产品进行了时空匹配和验证。精度评估主要采用相关系数（CC）来衡量时间变化上的一致性，并使用相对偏差（RB）来量化系统性偏差。评估结果显示，经过多产品平均处理的气温数据集表现出最高的准确性，其相对偏差仅为-10.36%，证实了其作为模型输入数据的可靠性。", "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": 4452754, "ds_files_count": 17, "ds_format": "Geotiff", "ds_space_res": "10km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "712afedd-b1f0-4e2c-98ae-f45fc0b04247.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": "09314967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2026-05-13 17:23:17", "last_updated": "2026-05-13 17:23:17", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7154.2026", "i18n": { "en": { "title": "Dataset of the Temperature Effect of Arctic Land Vegetation Greening", "ds_format": "Geotiff", "ds_source": "
Normalized Difference Vegetation Index (NDVI) (utilizing multiple products such as GIMMS3g and PKU GIMMS to ensure robustness), near-surface Air Temperature (TEM) (using the multi-product average of CRU TS, UDEL, and CHGN to mitigate single-source uncertainty), surface albedo, evapotranspiration, and water vapor (primarily sourced from ERA5, MERRA-2, and other reanalysis products).", "ds_quality": "
To ensure the scientific reliability of the calculated temperature feedback effects from vegetation greening, this dataset employed multiple, rigorous quality control and assessment mechanisms. Firstly, during the data preparation stage, multi-product integration was performed on key variables (such as NDVI and air temperature). By calculating the average values of multiple remote sensing and reanalysis datasets as input, this process effectively reduced the systematic biases and uncertainties inherent in single-source data, thereby enhancing the data's robustness. Secondly, the core accuracy assessment relied on validation against independent external observations: the study used actual monthly measured data from 11 flux stations within the FLUXNET 2015 network across the Arctic as a benchmark. All gridded products for air temperature, latent heat flux, and shortwave radiation were subjected to spatio-temporal matching and validation against these observations. Accuracy was primarily assessed using the Correlation Coefficient (CC) to measure consistency in temporal variation, and the Relative Bias (RB) to quantify systematic deviation. The assessment results confirmed that the multi-product averaged air temperature dataset exhibited the highest accuracy, with a relative bias of only -10.36%, thus verifying its reliability as input data for the model.", "ds_ref_way": "", "ds_abstract": "
This dataset is dedicated to filling the research gap in the quantitative assessment of the temperature feedback effect of Arctic vegetation greening on regional warming. The data core is based on the comprehensive processing of remote sensing products and reanalysis data for land surface and atmospheric variables over the Arctic region spanning from 1982 to 2015. The main inputs include: the Normalized Difference Vegetation Index (NDVI) (utilizing multiple products such as GIMMS3g and PKU GIMMS to ensure robustness), near-surface Air Temperature (TEM) (using the multi-product average of CRU TS, UDEL, and CHGN to mitigate single-source uncertainty), surface albedo, evapotranspiration, and water vapor (primarily sourced from ERA5, MERRA-2, and other reanalysis products). All data were uniformly resampled to a 10 km EASE-Grid spatial resolution. The data accuracy assessment employed a comparative validation method against FLUXNET 2015 Arctic flux station observations, using the Correlation Coefficient (CC) and Relative Bias (RB) to ensure the reliability of the input data. Specifically, the multi-product average air temperature was confirmed to possess the highest accuracy, exhibiting the lowest relative bias of -10.36%.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The data processing and modeling methodology of this dataset aim to establish a unified, knowledge-driven analytical framework for precisely disentangling the temperature feedback effect of Arctic vegetation greening. The data first underwent strict spatio-temporal standardization, where multi-source remote sensing and reanalysis products from 1982 to 2015 (including multiple sets of NDVI, air temperature, albedo, evapotranspiration, and water vapor) were uniformly resampled to a monthly resolution on a 10 km EASE-Grid. Robustness of key input data was further enhanced by calculating the multi-product average. The core modeling employs the Partial Least Squares Path Model (PLS-PM), a statistical method that uses prior knowledge to construct a causal path diagram. PLS-PM allows the study to simultaneously evaluate the direct effect and indirect feedback effects on air temperature, as NDVI changes influence it via three mediating variables—albedo, evapotranspiration, and water vapor—thereby achieving the quantitative decomposition of the total temperature effect.", "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": [ "多源数据", "植被", "陆地蒸散发", "温度效应" ], "ds_subject_tags": [ "大气科学" ], "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" }