本数据集提供了1982年至2015年北极陆地植被显著变绿区域的7月和8月植被显著变绿区域的蒸散发（ET）数据，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。数据集基于多源蒸散发产品融合构建，融合了GLEAM、SynthesizedET和TerraClimate三种主流蒸散发产品，并通过集合平均方法提升数据稳健性与时空覆盖度。植被显著变绿区域的界定基于多源遥感与再分析NDVI数据，通过长期趋势分析与显著性检验，提取1982-2015年间7月和8月持续显著变绿（p < 0.05） 的像元，形成动态变化的“显著变绿区域掩膜”。在此基础上，将多源蒸散发产品在该掩膜范围内进行融合与裁剪，生成植被变绿区域专属的蒸散发格点数据集。数据质量控制包括：多源ET产品的一致性检验与偏差校正，基于FLUXNET通量站点观测的独立验证，以及变绿掩膜的不确定性传递评估。", "ds_source": "
本数据集基于多源蒸散发产品与植被动态数据的系统集成构建，具体数据来源如下：GLEAM v3.5a：基于多源卫星观测（SSM/I、MODIS等）与再分析数据驱动的物理模型蒸散发产品，提供1982-2015年逐月全球陆地蒸散发数据，空间分辨率0.25°；SynthesizedET：融合FLUXNET通量观测、遥感数据与机器学习方法生成的蒸散发产品，具有较高的站点尺度精度，空间分辨率0.5°；TerraClimate：基于气候水量平衡模型与高分辨率气候强迫数据生成的月尺度蒸散发数据集，空间分辨率约4 km（0.04°）。GIMMS NDVI3g v1：提供1982-2015年逐半月植被指数数据（空间分辨率约8 km），用于检测长期植被变化趋势；FLUXNET2015北极通量站点数据：用于蒸散发产品的精度验证，涵盖典型苔原、灌木与湿地生态系统。", "ds_process_way": "
本数据集通过系统化数据处理流程构建，首先对GLEAM、SynthesizedET和TerraClimate三套蒸散发产品进行标准化预处理，包括异常值剔除、缺失值填补及空间重采样至10 km EASE-Grid 2.0网格系统。基于GIMMS与MODIS NDVI数据的长期时序分析，采用Theil-Sen趋势估计结合Mann-Kendall显著性检验（p < 0.05）识别1982-2015年7-8月持续显著变绿的植被区域，并利用CAVM土地覆盖数据排除非植被像元。在确定的变绿区域空间掩膜内，通过多源蒸散发产品的集合平均与偏差校正实现数据融合，采用时空一致性约束优化融合结果，最终生成覆盖北极植被显著变绿区域的逐月蒸散发格点数据集。", "ds_quality": "
本数据集通过多层次质量控制体系确保蒸散发数据的可靠性与科学适用性。在空间完整性方面，多源数据融合有效弥补了单一产品在北极复杂地形和植被过渡带的表现局限。站点尺度验证：基于FLUXNET2015北极通量站点独立观测数据对比显示，月尺度蒸散发的均方根误差（RMSE）为7.2 mm/month，平均偏差（MBE）控制在±5.5 mm/month以内，决定系数（R²）达0.81；与FLUXNET2015数据在典型苔原-灌木过渡带的对比中，生长季（7-8月）空间相关系数达0.86，非生长季维持在0.78以上。", "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": 38449454, "ds_files_count": 70, "ds_format": "Geotiff", "ds_space_res": "10km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "561b09b3-00d0-4f14-b438-fbb9db817eec.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", "170.4510" ], "quality_level": 3, "publish_time": "2026-05-13 16:54:18", "last_updated": "2026-05-13 16:54:18", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7151.2026", "i18n": { "en": { "title": "Evapotranspiration dataset of Arctic land vegetation significantly greening areas (1982-2015)", "ds_format": "Geotiff", "ds_source": "
This dataset is constructed through the systematic integration of multi-source evapotranspiration products and vegetation dynamic data. The specific data sources are as follows:GLEAM v3.5a: A physical model-based evapotranspiration product driven by multi-source satellite observations (e.g., SSM/I, MODIS) and reanalysis data, providing monthly global terrestrial evapotranspiration data from 1982 to 2015 with a spatial resolution of 0.25°.SynthesizedET: An evapotranspiration product generated by integrating FLUXNET flux observations, remote sensing data, and machine learning methods, offering high accuracy at the site scale with a spatial resolution of 0.5°.TerraClimate: A monthly evapotranspiration dataset generated based on a climate water balance model and high-resolution climate forcing data, with a spatial resolution of approximately 4 km (0.04°).GIMMS NDVI3g v1: Provides biweekly vegetation index data from 1982 to 2015 (spatial resolution of approximately 8 km), used for detecting long-term vegetation change trends.FLUXNET2015 Arctic flux site data: Used for accuracy validation of evapotranspiration products, covering typical tundra, shrubland, and wetland ecosystems.", "ds_quality": "
This dataset ensures the reliability and scientific applicability of evapotranspiration data through a multi-level quality control system. In terms of spatial completeness, multi-source data fusion effectively compensates for the performance limitations of individual products in the complex terrain and vegetation transition zones of the Arctic. Site-scale validation: Comparisons with independent observation data from FLUXNET2015 Arctic flux stations show that the root mean square error (RMSE) for monthly evapotranspiration is 7.2 mm/month, the mean bias error (MBE) is controlled within ±5.5 mm/month, and the coefficient of determination (R²) reaches 0.81. In comparisons with FLUXNET2015 data for typical tundra-shrub transition zones, the spatial correlation coefficient during the growing season (July–August) reaches 0.86 and remains above 0.78 during the non-growing season.", "ds_ref_way": "", "ds_abstract": "
This dataset provides evapotranspiration (ET) data for the vegetation significantly greening areas in July and August over Arctic land 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 dataset is constructed by integrating multiple evapotranspiration products, combining three mainstream ET products—GLEAM, SynthesizedET, and TerraClimate—with robustness and spatiotemporal coverage enhanced through ensemble averaging. The delineation of significantly greening vegetation areas is based on multi-source remote sensing and reanalysis NDVI data. Through long-term trend analysis and significance testing, pixels that exhibited sustained significant greening (p < 0.05) during July and August from 1982 to 2015 were extracted to form a dynamically changing \"significantly greening area mask.\" Building on this, multi-source evapotranspiration products were fused and cropped within this mask to generate a gridded evapotranspiration dataset specific to vegetation greening areas. Data quality control includes consistency verification and bias correction of multi-source ET products, independent validation based on FLUXNET flux tower observations, and uncertainty propagation assessment of the greening mask.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This dataset is constructed through a systematic data processing workflow. Initially, standardized preprocessing is applied to the three evapotranspiration products—GLEAM, SynthesizedET, and TerraClimate—including outlier removal, missing value imputation, and spatial resampling to a 10 km EASE-Grid 2.0 grid system. Based on long-term time-series analysis of GIMMS and MODIS NDVI data, Theil-Sen trend estimation combined with Mann-Kendall significance testing (p < 0.05) is employed to identify vegetation areas that exhibited sustained significant greening during July–August from 1982 to 2015. Non-vegetation pixels are excluded using CAVM land cover data. Within the delineated greening area spatial mask, data fusion is achieved through ensemble averaging and bias correction of the multi-source evapotranspiration products, with spatiotemporal consistency constraints applied to optimize the fusion results. Finally, a monthly gridded evapotranspiration dataset covering Arctic vegetation areas with significant greening is generated.", "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", "陆地蒸散发" ], "ds_subject_tags": [ "大气科学", "自然地理学" ], "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" } ], "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" }