本数据基于机械/人工钻探与探坑获取的地温测量数据，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温度图。数据格式为GeoTIFF，空间分辨率约30 m，投影为WGS_1984_Albers。", "ds_source": "
野外采样数据：基于大兴安岭西坡根河流域现场机械/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温度图。", "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分布规律，无显著异常值。", "ds_acq_start_time": "2023-08-01 00:00:00", "ds_acq_end_time": "2025-10-31 00:00:00", "ds_acq_place": "大兴安岭西坡额尔古纳地区根河流域", "ds_acq_lon_east": 122.70277777777778, "ds_acq_lat_south": 49.92916666666667, "ds_acq_lon_west": 119.28333333333333, "ds_acq_lat_north": 51.282777777777774, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 4664332, "ds_files_count": 3, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "3年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "95a2157f-b136-4be4-bbed-d5403c07a85c.jpg", "ds_thumb_from": 0, "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": "2026-04-01 09:46:58", "last_updated": "2026-05-11 19:06:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7248.2026", "i18n": { "en": { "title": "Temperature distribution map of 30m permafrost in the Genhe River Basin of the Erguna area on the western slope of the Greater Khingan Range (2023-2025)", "ds_format": "*.tif", "ds_source": "
Field sampling data: Ground temperature measurement data obtained from on-site mechanical/manual drilling and exploration pits in the Genhe River Basin on the western 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 regional distribution patterns and has no significant outliers.", "ds_ref_way": "", "ds_abstract": "
This data is based on ground temperature measurement data obtained from mechanical/manual drilling and exploration pits. Through methods such as ground temperature gradient model, environmental similarity model, and random forest, a permafrost temperature map at the research area scale was obtained. The data format is GeoTIFF, with a spatial resolution of approximately 30m and a projection of WGS1984_ Albers.", "ds_time_res": "", "ds_acq_place": "Genhe River Basin in the Erguna area on the western slope of the Greater Khingan Range", "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 temperature 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", "大兴安岭", "根河流域", "冻土温度" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "大兴安岭西坡额尔古纳地区根河流域" ], "ds_time_tags": [ 2023, 2024, 2025 ], "ds_contributors": [ { "true_name": "胡国杰", "email": "huguojie123@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": "邹德富", "email": "defuzou@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": "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": "肖瑶", "email": "xiaoyao@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": "杜二计", "email": "duerji@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": "赵拥华", "email": "zhaoyonghua@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "肖瑶", "email": "xiaoyao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "肖瑶", "email": "xiaoyao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冻土" }