2010年北极环极永久冻土区的地上与地下生物量碳密度图数据涵盖树木、苔原、草地和耕地等植被类型，并提供了不确定性图层。适用于全球碳循环模型、温室气体清单和生态系统服务评估。土壤有机碳（SOC）储量的空间显式估算，基于 harmonized regional soil classification maps 和 pedon 数据，使用 pedo-transfer 函数和外推方法填补数据缺失，生成按土壤类型（Gelisols、Histosols 等）分层的SOC储量。数据集支持GIS制图和陆地生态系统模型应用，适用于全球碳循环和气候变化研究。", "ds_source": "
数据来自以下文献：Spawn, S.A., Sullivan, C.C., Lark, T.J. et al. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci Data 7, 112 (2020). https://doi.org/10.1038/s41597-020-0444-4 Hugelius, G., Bockheim, J. G., Camill, P., Elberling, B., Grosse, G., Harden, J. W., Johnson, K., Jorgenson, T., Koven, C. D., Kuhry, P., Michaelson, G., Mishra, U., Palmtag, J., Ping, C.-L., O'Donnell, J., Schirrmeister, L., Schuur, E. A. G., Sheng, Y., Smith, L. C., Strauss, J., and Yu, Z.: A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region, Earth Syst. Sci. Data, 5, 393–402, https://doi.org/10.5194/essd-5-393-2013, 2013.\n
Hugelius, G., Tarnocai, C., Broll, G., Canadell, J. G., Kuhry, P., and Swanson, D. K.: The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions, Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, 2013.", "ds_process_way": "
采用“叠加与分配”方法，结合树覆盖百分比、土地覆盖图和规则决策树，将各植被类型的生物量碳密度图进行整合。地下生物量通过根冠比模型估算。不确定性通过误差传播方法量化。\n
（1）使用 ArcGIS 10.0 进行空间数据整合与多边形属性计算；\n
（2）采用 pedo-transfer 函数（如BD与OC%的回归模型）填补缺失数据；\n
（3）对未采样深度的数据使用外推或默认值（如C层SOC密度）；\n
（4）将 pedon 数据按土壤类型和区域（北美/欧亚）分类，计算平均SOC储量；\n
（5）生成矢量多边形和栅格格式（TIFF、NetCDF）数据产品。", "ds_quality": "
数据质量通过 pedon 数据完整性、插值方法和外推策略控制。使用统计检验（t检验）验证土壤类型间SOC储量的独立性。数据存在空间覆盖不均（格陵兰数据缺乏）。所有经过插值或外推的 pedon 均标记，供用户筛选。", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2010-12-31 00:00:00", "ds_acq_place": "北极,环极地区,阿拉斯加,加拿大,俄罗斯", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 66.55, "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": "login-access", "ds_total_size": 1772505, "ds_files_count": 4, "ds_format": "*.csv,*.shp,*.tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "b6301224-85a9-4542-a8ba-9d9107491368.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": [ "170.99" ], "quality_level": 0, "publish_time": "2026-05-07 15:06:29", "last_updated": "2026-05-07 15:26:22", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7139.2026", "i18n": { "en": { "title": "A data set for estimating biomass carbon and organic carbon storage in soils of the northern circumpolar permafrost region in the year 2010", "ds_format": "*.csv,*.shp,*.tif", "ds_source": "
The GIMMS NDVI 3g product is from the AVHRR instrument positioned on the NOAA polar‐orbiting meteorological satellite (https://ecocast.arc.nasa.gov/data/pub/gimms). This product spans the period from 1982 to 2015 with a temporal resolution of 15 days and a spatial resolution of 8 km. It has been widely used globally, eliminating the effects of factors such as volcanic eruptions, solar zenith angle, and sensor sensitivity changes over time.
The MODIS NDVI data (MOD13Q1) originate from the NASA MODIS Land Product suite, developed using a unified algorithm for MODIS Vegetation Index products. The data set, with a temporal resolution of 16 days and a spatial resolution of 250 m, was downloaded from the MODIS Web (https://modis.gsfc.nasa.gov/). The product's time frame spans from February 28, 2000 to December 31, 2020.
The DEM data used in this study are provided by the Shuttle Radar Topography Mission (SRTM) operated by the National Geospatial‐Intelligence Agency and the National Aeronautics and Space Administration. The available options for spatial resolution is 90 m, with the latter being accessible at http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp.", "ds_quality": "
Data quality is controlled through pedon completeness, gap-filling methods, and extrapolation strategies. Statistical tests (t-test) validate class independence of SOC storage. Uncertainties include uneven spatial coverage (e.g., Greenland). All gap-filled or extrapolated pedons are flagged for user discretion.", "ds_ref_way": "", "ds_abstract": "
The time-series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.Using the MOD13Q1 and GIMMS NDVI 3g vegetation index datasets, combined with digital elevation models and other data sources, a random forest downscaling model was employed to generate a 250 m spatial resolution NDVI data product for the Tibetan Plateau from 1982 to 2020, which was subsequently validated and evaluated.Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7. This data can provide essential support for research on the grassland ecosystem of the Tibetan Plateau.", "ds_time_res": "", "ds_acq_place": "Arctic, circumpolar, Alaska, Canada, Russia", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The \"overlay and allocate\" method was used, integrating landcover-specific biomass maps with percent tree cover and landcover maps via a rule-based decision tree. Belowground biomass was estimated using root-to-shoot ratios. Uncertainty was propagated using error propagation techniques.\r\n
(1) Spatial integration and polygon attribute calculations using ArcGIS 10.0; \r\n
(2) Gap-filling with pedo-transfer functions (e.g., regression between BD and OC%); \r\n
(3) Extrapolation or default values for unsampled depths (e.g., C-horizon SOC density); \r\n
(4) Classification of pedons by soil type and region (North America/Eurasia) for mean SOC storage; \r\n
(5) Generation of vector polygon and raster formats (TIFF, NetCDF).", "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": [ "生物量碳", "地上生物量", "地下生物量", "土壤有机碳", "永久冻土", "北极", "碳循环" ], "ds_subject_tags": [ "地球科学其他学科" ], "ds_class_tags": [], "ds_locus_tags": [ "北极", "环极地区", "阿拉斯加", "加拿大", "俄罗斯" ], "ds_time_tags": [ 2010 ], "ds_contributors": [ { "true_name": "朱仁斌", "email": "zhurb@ustc.edu.cn", "work_for": "中国科学技术大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "朱仁斌", "email": "zhurb@ustc.edu.cn", "work_for": "中国科学技术大学", "country": "中国" } ], "ds_managers": [ { "true_name": "付东升", "email": "joyfds@mail.ustc.edu.cn", "work_for": "中国科学技术大学", "country": "中国" } ], "category": "水文" }