本数据集针对中国东北大小兴安岭地区多年冻土厚度空间分布的复杂性，提供了一套基于地温梯度模型与机器学习反演的多年冻土厚度空间分布产品。研究建立在有限的深孔（>20 m）地温数据基础之上，通过地温梯度模型反演浅孔的深层地温，计算得出多年冻土底板深度，并以此构建了包含104个站点的基础训练数据集。在此基础上，选取降水（PRE）、地表融化指数（TDD）和地形位置指数（TPI）等作为关键环境预测因子，应用随机森林（Random Forest, RF）算法模拟生成。结果显示，研究区多年冻土平均厚度为47.71±10 m，在空间上呈现显著的“北厚南薄、西厚东薄、山区厚平原薄”的分布格局。该数据集为深孔地温梯度测量受限区域的多年冻土厚度估算提供了重要参考。", "ds_source": "
观测数据：包含104个站点的地温观测数据（深孔>20m及浅孔数据）。\n
环境因子：降水（PRE）、地表融化指数（TDD）、地形位置指数（TPI）等。", "ds_process_way": "
地温梯度建模：利用深孔地温数据建立地温梯度模型。\n
数据反演：反演浅孔站点的深部地温，计算得到多年冻土底板深度。\n
空间制图：利用随机森林（Random Forest）算法，结合环境因子对整个区域的多年冻土厚度进行空间制图。\n
精度评价：随机森林模型的分类准确度（Classification Accuracy）为0.74；模拟结果显示的平均冻土厚度标准差约为±10 m。", "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": 2725748, "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": "4f607e2c-83e1-4445-a58d-e7e79ce81aa6.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 15:53:49", "last_updated": "2026-05-12 11:13:25", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7273.2026", "i18n": { "en": { "title": "1km permafrost thickness map of Northeast China (2023-2024)", "ds_format": "*.tif", "ds_source": "
Observation data: including ground temperature observation data from 104 stations (deep hole>20m and shallow hole data).\r\n
Environmental factors: precipitation (PRE), surface melting index (TDD), terrain 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 set of spatial distribution products of permafrost thickness based on geothermal gradient model and machine learning inversion, targeting the complexity of the spatial distribution of permafrost thickness in the Greater and Lesser Khingan Mountains in Northeast China. The research is based on limited deep hole (>20 m) ground temperature data, and uses a ground temperature gradient model to invert the deep ground temperature of shallow holes, calculate the depth of permafrost floor, and construct a basic training dataset containing 104 stations based on this. On this basis, precipitation (PRE), surface melting index (TDD), and terrain position index (TPI) are selected as key environmental prediction factors, and the Random Forest (RF) algorithm is applied to simulate and generate them. The results showed that the average thickness of permafrost in the study area was 47.71 ± 10 m, showing a significant spatial distribution pattern of \"thick in the north and thin in the south, thick in the west and thin in the east, and thick in mountainous areas and plains\". This dataset provides an important reference for estimating the thickness of permafrost in restricted areas of deep hole geothermal gradient measurement.", "ds_time_res": "", "ds_acq_place": "Northeast China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Ground temperature gradient modeling: Establish a ground temperature gradient model using deep hole ground temperature data.\r\n
Data inversion: Invert the deep ground temperature of shallow hole stations and calculate the depth of permafrost floor for many years.\r\n
Spatial mapping: Using the Random Forest algorithm, combined with environmental factors, to spatially map the thickness of permafrost in the entire region.\r\n
Accuracy evaluation: The classification accuracy of the random forest model is 0.74; The simulation results show that the standard deviation of the average frozen soil thickness is about ± 10 m.", "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", "冻土厚度分布", "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" }