多年冻土是全球变化背景下最敏感的地圈要素之一，其热状况变化直接影响地表能量与水分平衡、生态系统过程及碳循环反馈。准确刻画多年冻土的空间分布、年平均地温（Mean Annual Ground Temperature, MAGT）及活动层厚度（Active Layer Thickness, ALT）的长期演变，对于理解高纬度地区对气候变暖的响应机制和预测未来环境风险具有重要意义。本数据集基于GTN-P（Global Terrestrial Network for Permafrost）和CALM（Circumpolar Active Layer Monitoring）提供的多年冻土监测数据，选取MAGT与ALT作为核心指标，结合地形、气候、土壤和植被等多源环境因子，利用多种机器学习算法构建预测模型，并通过交叉验证筛选出最佳模型进行空间制图。数据集覆盖北纬45°以北的泛北极地区，时间跨度为1980–2019年，按5年为一个平均周期（1980–1984、1985–1989、1990–1994、1995–1999、2000–2004、2005–2009、2010–2014、2015–2019）进行重建，空间分辨率为1 km。", "ds_source": "
本数据集基于GTN-P（Global Terrestrial Network for Permafrost）和CALM（Circumpolar Active Layer Monitoring）提供的多年冻土和活动层厚度监测数据，同时整合多源环境因子用于建模。地形因子来自美国地质调查局（USGS）提供的空间分辨率为1 km × 1 km 的SRTM DEM数据，并利用SAGA GIS软件提取了11项与地形相关的指标，包括：海拔（H）、坡度（Slope）、平面曲率（PlanC）、剖面曲率（ProC）、地形湿度指数（TWI）、汇水面积（TCA）、相对坡位（RSP）、坡长指数（LS）、地形收敛指数（CI）、谷深（VD）和封闭洼地（CD）。气候因子数据来自WorldClim v2.1全球气候数据库，植被因子采用NASA MOD13A3产品提供的归一化植被指数（NDVI）。土壤因子则包括不同土层深度的砂粒（Sand）、粉粒（Silt）和粘粒（Clay）含量，数据来源于SoilGrids全球土壤数据库。", "ds_process_way": "
本数据集以GTN-P和CALM的多年冻土和活动层厚度监测数据为基础，提取并匹配了相应的环境因子信息，涵盖地形（海拔、坡度、坡向）、气候（气温、降水、冻融指数）、土壤属性（砂粒、粉粒和粘粒含量）以及植被（NDVI）等多类指标。在模型构建与评估过程中，选取多种常见机器学习分类算法并采用10折交叉验证进行性能对比，结果表明随机森林模型在预测精度和稳定性方面表现最佳，因此被确定为最终建模方案。进一步基于模型的特征重要性排序和特征组合测试，筛选出最优特征组合，并利用全部监测站点数据进行训练构建最终模型。最终数据产品覆盖1980–2019年，按5年为一个时间段（1980–1984、1985–1989、1990–1994、1995–1999、2000–2004、2005–2009、2010–2014、2015–2019）进行重建，每一期均生成对应的GeoTIFF格式1 km分辨率栅格文件。", "ds_quality": "
模型精度评估结果表明，MAGT的预测RMSE为1.23 ℃，R²为0.85；ALT的RMSE为55.6 cm，R²为0.84，能够较好地反映多年冻土热态的时空差异及演变趋势。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2016-12-31 00:00:00", "ds_acq_place": "北纬45度以北", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 45.0, "ds_acq_lon_west": 0.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 643380190, "ds_files_count": 4, "ds_format": "*.tif", "ds_space_res": "1000 m", "ds_time_res": "5年", "ds_coordinate": "WGS84", "ds_projection": "Stereographic_North_Pole", "ds_thumbnail": "4ba7d43e-51a9-4f61-9895-b10739f62bc2.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": 0, "publish_time": "2026-05-07 14:38:18", "last_updated": "2026-05-13 16:05:44", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7133.2026", "i18n": { "en": { "title": "Distribution of Permafrost in the Arctic Circle, Annual Average Ground Temperature, and Changes in Active Layer Thickness (1980-2019)", "ds_format": "*.tif", "ds_source": "
This dataset is based on permafrost and active layer thickness monitoring records provided by the GTN-P (Global Terrestrial Network for Permafrost) and CALM (Circumpolar Active Layer Monitoring) programs, supplemented with multiple environmental predictors for model development. Topographic factors were derived from the Shuttle Radar Topography Mission (SRTM) DEM provided by the United States Geological Survey (USGS) at a spatial resolution of 1 km × 1 km. Using the SAGA GIS software, 11 terrain-related indices were extracted, including elevation (H), slope (Slope), plan curvature (PlanC), profile curvature (ProC), topographic wetness index (TWI), total catchment area (TCA), relative slope position (RSP), slope length factor (LS), convergence index (CI), valley depth (VD), and closed depressions (CD). Climatic predictors were obtained from the WorldClim v2.1 global climate database, while vegetation information was derived from the NASA MOD13A3 product, providing the normalized difference vegetation index (NDVI). Soil properties, including sand, silt, and clay content at multiple depth intervals, were taken from the SoilGrids global soil database.", "ds_quality": "
The model evaluation results show that the predicted MAGT achieved an RMSE of 1.23 ℃ with an R² of 0.85, while the ALT predictions yielded an RMSE of 55.6 cm with an R² of 0.84, indicating that the dataset reliably captures the spatiotemporal variability and evolutionary trends of permafrost thermal conditions.", "ds_ref_way": "", "ds_abstract": "
Permafrost is one of the most sensitive components of the geosphere under global change, and its thermal dynamics directly influence surface energy and water balance, ecosystem processes, and carbon cycle feedbacks. Accurately characterizing the spatial distribution of permafrost, mean annual ground temperature (MAGT), and active layer thickness (ALT), as well as their long-term evolution, is essential for understanding the response of high-latitude regions to climate warming and for predicting future environmental risks. This dataset is derived from permafrost monitoring records provided by the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) program, using MAGT and ALT as core indicators. Multiple environmental factors, including topography, climate, soil, and vegetation, were integrated, and several machine learning algorithms were tested to construct predictive models, with the best-performing model selected through cross-validation for spatial mapping. The dataset covers the pan-Arctic region north of 45°N during 1980–2019, reconstructed at 5-year intervals (1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, 2015–2019) with a spatial resolution of 1 km.", "ds_time_res": "", "ds_acq_place": "North of 45°N", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This dataset is based on permafrost and active layer thickness monitoring records from GTN-P (Global Terrestrial Network for Permafrost) and CALM (Circumpolar Active Layer Monitoring), with corresponding environmental factors extracted and matched. The predictors include topographic variables (elevation, slope, aspect), climatic factors (air temperature, precipitation, freezing and thawing indices), soil properties (sand, silt, and clay content), and vegetation information (NDVI). During model development and evaluation, several common machine learning classification algorithms were tested, and 10-fold cross-validation was applied for performance comparison. The results showed that the random forest model achieved the best accuracy and stability and was therefore selected as the final approach. Based on the feature importance ranking and feature combination testing, the optimal set of predictors was identified, and the final model was trained using all monitoring sites. The resulting data products cover the period 1980–2019, reconstructed at 5-year intervals (1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, 2015–2019). Each time slice is provided as a GeoTIFF raster file at a spatial resolution of 1 km.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 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, "belong_to_nieer": false, "ds_topic_tags": [ "多年冻土", "活动层厚度", "年平均地温", "机器学习" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "环北极", "北纬45度以北" ], "ds_time_tags": [ 1980, 1984, 1985, 1989, 1990, 1994, 1999, 2000, 2004, 2005, 2009, 2010, 2014, 2015, 2019 ], "ds_contributors": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "ds_managers": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "category": "极地" }