本数据集针对中国东北大小兴安岭地区多年冻土年平均地温（MAGST）空间分布的异质性，提供了一套高精度的地温空间模拟产品。数据集基于多源观测与机器学习框架构建，综合利用了区域内深孔（>20 m）与浅孔钻孔的15 m深度地温观测实测数据。研究选取降水（PRE）、地表融化指数（TDD）、地形位置指数（TPI）等环境因子作为关键预测变量，运用随机森林（Random Forest, RF）回归算法，模拟生成了研究区空间分辨率为1 km的年平均地温分布图。该数据有效重建并拓展了东北地区地温的空间格局信息。", "ds_source": "
基础样本：整合了区域内所有深孔及浅孔钻孔的15 m深度地温实测数据（涵盖104个站点）。\n
预测变量：降水（PRE）、地表融化指数（TDD）、地形位置指数（TPI）等。", "ds_process_way": "
数据整合：清洗并整合钻孔测温数据，作为训练集与验证集。\n
模型构建：采用随机森林回归算法建立地温与环境因子之间的非线性关系模型。\n
空间制图：将训练好的模型应用至全区域，生成MAGST空间分布图。\n
质量控制：通过交叉验证确保模型在站点尺度的拟合精度。", "ds_quality": "
本数据采用机器学习方法进行模型构建，计算混淆矩阵、总体准确率（Overall Accuracy）。结果显示，模型具有较高的一致性。", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "中国东北地区", "ds_acq_lon_east": 135.07999999999998, "ds_acq_lat_south": 38.730555555555554, "ds_acq_lon_west": 111.15, "ds_acq_lat_north": 53.55611111111111, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 2684053, "ds_files_count": 3, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "2年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "99a4cf27-caf4-4510-919e-fd4d106adbd9.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "221ebf56-1b0b-4574-972b-1fb6d3cf1be7", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-04-03 15:44:27", "last_updated": "2026-05-12 11:12:57", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7272.2026", "i18n": { "en": { "title": "1km permafrost temperature map of Northeast China (2023-2024)", "ds_format": "*.tif", "ds_source": "
Basic sample: Integrated 15 meter depth ground temperature measurement data from all deep and shallow boreholes in the region (covering 104 stations).\r\n
Predictive variables: Precipitation (PRE), Surface Melting Index (TDD), Topographic Location Index (TPI), etc.", "ds_quality": "
This data is modeled using machine learning methods to calculate confusion matrix and overall accuracy. The results show that the model has high consistency.", "ds_ref_way": "", "ds_abstract": "
This dataset provides a high-precision geothermal spatial simulation product for the heterogeneity of the annual average ground temperature (MAGST) spatial distribution of permafrost in the Greater and Lesser Khingan Mountains in Northeast China. The dataset is constructed based on multi-source observation and machine learning framework, and comprehensively utilizes the measured data of ground temperature at a depth of 15 meters from deep (>20 meters) and shallow boreholes in the region. The study selected environmental factors such as precipitation (PRE), surface melting index (TDD), and terrain position index (TPI) as key predictive variables, and used the Random Forest (RF) regression algorithm to simulate and generate an annual average ground temperature distribution map with a spatial resolution of 1 km in the study area. This data effectively reconstructed and expanded the spatial pattern information of ground temperature in Northeast China.", "ds_time_res": "", "ds_acq_place": "Northeast China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Data integration: Clean and integrate borehole temperature measurement data as training and validation sets.\r\n
Model construction: Using random forest regression algorithm to establish a nonlinear relationship model between ground temperature and environmental factors.\r\n
Spatial mapping: Apply the trained model to the entire region to generate a MAGST spatial distribution map.\r\n
Quality control: Ensure the fitting accuracy of the model at the site scale through cross validation.", "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, "ds_topic_tags": [ "多年冻土", "年平均地温分布", "1km" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国东北地区" ], "ds_time_tags": [ 2023, 2024 ], "ds_contributors": [ { "true_name": "刘广岳", "email": "liuguangyue@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "赵林", "email": "lzhao@nuist.edu.cn", "work_for": "南京信息工程大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "鲁莹莹", "email": "202312100015@nuist.edu.cn", "work_for": "南京信息工程大学", "country": "中国" } ], "ds_managers": [ { "true_name": "王翀", "email": "wangchong2022@nuist.edu.cn", "work_for": "南京信息工程大学", "country": "中国" } ], "category": "冻土" }