本数据集为东北大兴安岭东坡塔河地区卡马兰河流域多年冻土厚度空间栅格数据，栅格单元数值表征多年冻土厚度（单位：m）。数据集以研究区现场机械 / 人工钻探与探坑获取的实测地温数据为核心观测基础，融合 Google Earth Engine（GEE）平台及权威数据源获取的气候、土壤、地形等多源环境空间数据集作为模型预测变量。通过 Python 与 ArcGIS 完成环境因子的格式转换、WGS84 坐标系空间配准、目标分辨率重采样及归一化等预处理流程，基于地形（高程、坡度、坡向、地形起伏度、地形湿度指数、地形位置指数等）、土壤（土壤质地、土地覆盖、基岩埋深）、气象（气温、降水、冻融指数）三类核心因子，综合采用地温梯度模型、环境相似性模型与随机森林算法，反演生成研究区尺度多年冻土厚度空间分布产品。为保障数据可靠性，采用五折交叉验证评估随机森林模型预测精度，并通过 ArcGIS 开展栅格数据可视化校验与空间逻辑分析，确保多年冻土厚度空间分布符合区域冻土分布规律，无显著异常值。本数据集可为寒区水文过程模拟、生态环境评估与工程建设规划提供关键基础数据支撑。", "ds_source": "
野外采样数据：基于大兴安岭东坡卡马兰河流域现场机械/人工钻探与探坑获取的地温测量数据。\n
环境数据：来源于Google Earth Engine (GEE)平台及权威网站下载的气候、土壤、地形等多源空间数据集，作为模型预测变量。", "ds_process_way": "
利用Python和ArcGIS对所有环境因子数据进行预处理，包括格式转换、空间配准（统一至WGS84坐标系）、重采样（至目标分辨率）与归一化。基于地形因子（高程、坡度、坡向、地形起伏度、地形湿度指数、地形位置指数等）、土壤因子（土壤质地、土地覆盖、基岩埋深）、气象因子（气温、降水、冻融指数）数据，通过地温梯度模型、环境相似性模型、随机森林的方法，得到研究区域尺度的多年冻土厚度图。", "ds_quality": "
模型验证：采用五折交叉验证方法评估随机森林模型的预测精度，确保模型可靠。\n
空间一致性检查：使用ArcGIS对生成的栅格数据进行可视化检查与逻辑分析，确保多年冻土温度空间分布符合区域冻土分布规律，无显著异常值。", "ds_acq_start_time": "2023-07-01 00:00:00", "ds_acq_end_time": "2024-09-30 00:00:00", "ds_acq_place": "大兴安岭东坡卡马兰河流域", "ds_acq_lon_east": 123.5, "ds_acq_lat_south": 51.86611111111111, "ds_acq_lon_west": 122.61944444444444, "ds_acq_lat_north": 52.35888888888889, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 22486979, "ds_files_count": 3, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "2年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "09610a8a-e49d-484b-b1e5-bf33429ed642.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": "2026-03-31 18:21:18", "last_updated": "2026-05-11 18:15:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7243.2026", "i18n": { "en": { "title": "30m Permafrost Thickness Map of Kamalan River Basin in Dongpo Tahe Area of Daxing'anling Mountains (2023-2024)", "ds_format": "*.tif", "ds_source": "
Field sampling data: Ground temperature measurement data obtained from on-site mechanical/manual drilling and exploration pits in the Kamaran River Basin on the east slope of the Greater Khingan Range.\r\n
Environmental data: sourced from multi-source spatial datasets such as climate, soil, and terrain downloaded from the Google Earth Engine (GEE) platform and authoritative websites, used as model predictive variables.", "ds_quality": "
Model validation: The five fold cross validation method is used to evaluate the prediction accuracy of the random forest model and ensure its reliability.\r\n
Spatial consistency check: Use ArcGIS to visually check and logically analyze the generated raster data, ensuring that the spatial distribution of permafrost temperature conforms to the regional permafrost distribution pattern and has no significant outliers.", "ds_ref_way": "", "ds_abstract": "
This dataset is a spatial raster data of permafrost thickness in the Kamalan River Basin of the Dongpo Tahe area in the Greater Khingan Range of Northeast China. The grid unit values represent the permafrost thickness (unit: m). The dataset is based on the measured ground temperature data obtained from on-site mechanical/manual drilling and exploration pits in the research area, and integrates multi-source environmental spatial datasets such as climate, soil, and terrain obtained from the Google Earth Engine (GEE) platform and authoritative data sources as model prediction variables. By using Python and ArcGIS to complete the preprocessing process of environmental factor format conversion, WGS84 coordinate system spatial registration, target resolution resampling, and normalization, based on three core factors: terrain (elevation, slope, aspect, terrain undulation, terrain humidity index, terrain position index, etc.), soil (soil texture, land cover, bedrock burial depth), and meteorology (temperature, precipitation, freeze-thaw index), the geothermal gradient model, environmental similarity model, and random forest algorithm are comprehensively used to invert and generate the spatial distribution products of permafrost thickness at the scale of the study area. To ensure data reliability, a five fold cross validation was used to evaluate the prediction accuracy of the random forest model, and ArcGIS was used for raster data visualization verification and spatial logic analysis to ensure that the spatial distribution of permafrost thickness conforms to the regional permafrost distribution pattern and has no significant outliers. This dataset can provide key basic data support for simulating hydrological processes, ecological environment assessment, and engineering construction planning in cold regions.", "ds_time_res": "", "ds_acq_place": "Kamaran River Basin on the East Slope of Daxing'an Mountains", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Preprocess all environmental factor data using Python and ArcGIS, including format conversion, spatial registration (unified to WGS84 coordinate system), resampling (to target resolution), and normalization. Based on terrain factors (elevation, slope, aspect, terrain undulation, terrain humidity index, terrain position index, etc.), soil factors (soil texture, land cover, bedrock burial depth), and meteorological factors (temperature, precipitation, freeze-thaw index) data, the study area scale permafrost thickness map is obtained through methods such as geothermal gradient model, environmental similarity model, and random forest.", "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", "厚度", "卡马兰河流域" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "大兴安岭东坡卡马兰河流域" ], "ds_time_tags": [ 2023, 2024 ], "ds_contributors": [ { "true_name": "臧淑英", "email": "zsy6311@163.com", "work_for": "哈尔滨师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "何俭翔", "email": "hejianxiang1998@126.com", "work_for": "哈尔滨师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "孙丽", "email": "sunli_wabb@163.com", "work_for": "哈尔滨师范大学", "country": "中国" } ], "category": "冻土" }