本数据集提供了1982年至2015年北极陆地地区（北纬66°以北）的植被动态监测数据，包含7月和8月植被指数（NDVI），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。数据集基于多源遥感数据融合构建，整合了GIMMS NDVI3g v1（1982-2015，半月尺度）、MODIS植被产品（2000-2015，包括MOD13C2 NDVI），通过时序重建与空间融合算法生成长时间连续一致的植被参量序列。数据加工采用先进的植被动态提取技术：基于交叉定标与辐射归一化方法消除不同卫星传感器间的系统偏差，采用自适应滤波算法（如SG滤波、HANTS模型）有效剔除云污染、大气干扰及冰雪覆盖引起的异常波动。与FLUXNET通量站植被观测、地面调查数据对比，NDVI误差控制在±0.05以内，生长季长度检测精度达85%。", "ds_source": "
本数据集基于多源卫星遥感数据的系统集成与协同处理构建，具体数据来源及特征如下：GIMMS NDVI3g v1：基于AVHRR传感器的第三代全球植被指数数据集，提供1982-2015年逐半月NDVI数据，空间分辨率约8 km。该数据集经过辐射定标、轨道漂移校正和大气效应处理，是当前北极长期植被动态研究最广泛使用的数据基础。MODIS NDVI产品（MOD13C2）：基于Terra卫星的中分辨率成像光谱仪数据，提供2000-2015年逐月NDVI数据，空间分辨率0.05°（约5.6 km）。该产品采用合成算法最大程度减少云和气溶胶影响，具有较高的时序一致性和空间精度。FLUXNET2015北极通量站点数据：包含多个北极苔原、灌木和湿地生态系统的植被观测记录，用于NDVI产品的精度验证和物候参数校准。", "ds_process_way": "
本数据集通过系统化的多源遥感数据融合与植被动态重建流程构建，首先对GIMMS NDVI3g v1和MODIS MOD13C2 NDVI数据进行辐射校正、传感器定标与空间配准，通过BRDF校正消除观测几何效应，并统一重采样至10 km EASE-Grid 2.0网格系统。采用分段融合策略建立1982-2015年连续时序：1982-1999年以GIMMS数据为基础，利用MODIS数据构建交叉校准模型；2000-2015年融合双源数据，基于贝叶斯框架优化数据质量。通过自适应Savitzky-Golay滤波结合HANTS算法有效剔除云污染、大气干扰及冰雪噪声，并采用时间序列自回归模型进行缺失值填补。针对7-8月植被生长高峰期，应用最大值合成法生成月际NDVI产品，结合改进的混合像元分解模型与CAVM植被分类信息提取优化植被信号，最终形成空间连续、时序一致的北极植被指数数据集。", "ds_quality": "
本数据集通过多层次质量控制体系确保植被指数数据的可靠性与科学适用性。在时空一致性方面，多源数据融合有效解决了单传感器数据在北极高纬度地区因云覆盖、冰雪干扰和极夜现象导致的监测盲区，1982-2015年7-8月数据空间完整度达到97.3%，时序连续度超过98.8%。基于FLUXNET2015北极通量站点植被观测数据对比显示，7-8月NDVI与地面实测值的平均绝对误差为0.03，均方根误差控制在0.04以内，决定系数（R²）达0.86；与高分辨率Landsat NDVI产品（30米分辨率）在典型苔原-灌木生态过渡带的对比中，7-8月空间相关系数分别为0.89和0.87。", "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": 55853683, "ds_files_count": 70, "ds_format": "Geotiff", "ds_space_res": "10km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "7a098d73-2d3c-4a1a-b6a2-1d818a563c64.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 16:33:14", "last_updated": "2026-05-13 16:33:14", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7150.2026", "i18n": { "en": { "title": "Arctic Land Vegetation Dataset (1982-2015)", "ds_format": "Geotiff", "ds_source": "
This dataset is constructed through the systematic integration and collaborative processing of multi-source satellite remote sensing data. The specific data sources and their characteristics are as follows:GIMMS NDVI3g v1: This is a third-generation global vegetation index dataset based on the AVHRR sensor, providing biweekly NDVI data from 1982 to 2015 with a spatial resolution of approximately 8 km. The dataset has undergone radiometric calibration, orbital drift correction, and atmospheric effect processing, making it the most widely used data foundation for long-term vegetation dynamic studies in the Arctic.MODIS NDVI product (MOD13C2): Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra satellite, this product provides monthly NDVI data from 2000 to 2015 with a spatial resolution of 0.05° (approximately 5.6 km). The product employs a compositing algorithm to minimize the effects of clouds and aerosols, offering high temporal consistency and spatial accuracy.FLUXNET2015 Arctic flux site data: This includes vegetation observation records from multiple Arctic tundra, shrubland, and wetland ecosystems, and is used for the accuracy validation of NDVI products and the calibration of phenological parameters.", "ds_quality": "
This dataset ensures the reliability and scientific applicability of vegetation index data through a multi-tiered quality control system. In terms of spatiotemporal consistency, multi-source data fusion effectively addresses monitoring blind spots caused by cloud cover, snow/ice interference, and polar night phenomena in single-sensor data in the high-latitude Arctic region. The spatial completeness of July–August data from 1982 to 2015 reaches 97.3%, and temporal continuity exceeds 98.8%. Comparisons based on FLUXNET2015 Arctic flux station vegetation observation data show that the mean absolute error between July–August NDVI and ground-measured values is 0.03, the root mean square error is controlled within 0.04, and the coefficient of determination (R²) reaches 0.86. In comparisons with high-resolution Landsat NDVI products (30-meter resolution) in typical tundra-shrub ecological transition zones, the spatial correlation coefficients for July and August are 0.89 and 0.87, respectively.", "ds_ref_way": "", "ds_abstract": "
This dataset provides vegetation dynamic monitoring data for Arctic land areas (north of 66°N) from 1982 to 2015, including vegetation index (NDVI) for July and August, with a spatial resolution of 10 km and projected using the Equal-Area Scalable Earth Grid (EASE-Grid 2.0). The dataset is constructed through the fusion of multi-source remote sensing data, integrating GIMMS NDVI3g v1 (1982–2015, biweekly scale) and MODIS vegetation products (2000–2015, including MOD13C2 NDVI). Long-term, continuous, and consistent vegetation parameter sequences are generated through time-series reconstruction and spatial fusion algorithms. Advanced vegetation dynamic extraction techniques are employed in data processing: cross-calibration and radiometric normalization methods are used to eliminate systematic biases between different satellite sensors, and adaptive filtering algorithms (such as SG filtering and HANTS models) effectively remove anomalies caused by cloud contamination, atmospheric interference, and snow/ice cover. Comparisons with FLUXNET flux station vegetation observations and ground survey data show that NDVI errors are controlled within ±0.05, and the detection accuracy for growing season length reaches 85%.", "ds_time_res": "", "ds_acq_place": "Arctic Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This dataset is constructed through a systematic multi-source remote sensing data fusion and vegetation dynamic reconstruction process. First, radiometric calibration, sensor calibration, and spatial registration are performed on GIMMS NDVI3g v1 and MODIS MOD13C2 NDVI data. BRDF correction is applied to eliminate observation geometric effects, and the data are uniformly resampled to the 10 km EASE-Grid 2.0 grid system. A segmented fusion strategy is employed to establish a continuous time series from 1982 to 2015: for the period 1982–1999, GIMMS data serve as the foundation, with MODIS data used to construct a cross-calibration model; for 2000–2015, the two data sources are fused, and data quality is optimized based on a Bayesian framework. Adaptive Savitzky-Golay filtering combined with the HANTS algorithm effectively removes cloud contamination, atmospheric interference, and snow/ice noise, while a time-series autoregressive model is used to fill in missing values. For the vegetation peak growth periods in July and August, the maximum value composite method is applied to generate monthly NDVI products. Combined with an improved mixed-pixel decomposition model and CAVM vegetation classification information, vegetation signals are extracted and optimized, ultimately forming a spatially continuous and temporally consistent Arctic vegetation index 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": [ "多源数据", "植被", "陆地蒸散发", "温度效应" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "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" }