本数据基于机械/人工钻探与探坑获取的地下冰储量实测样本数据，采用随机森林回归模型，以实测体积含水量(VWC)为因变量，多环境因子为自变量，进行空间预测建模。模型预测结果输出为不同深度层（2m以上、2-5m、5m以下）的栅格数据，最终合成并制作研究区域尺度的多年冻土地下冰储量图。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/人工钻和探坑获取的地下冰含量测量数据；环境因子数据来源于气候、土壤、地形、植被类型等空间数据集。\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工具对环境因子数据进行处理，采用随机森林回归模型进行3层地下冰体积含冰量空间预测，并结合多年冻土厚度数据进行统计。", "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，确保多年冻土地下冰储量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": 59013485, "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": "6340cb28-e2b3-4bed-98e3-19346bbe02c5.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 10:47:05", "last_updated": "2026-05-11 19:18:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7250.2026", "i18n": { "en": { "title": "Distribution Map of 30m Permafrost Underground Ice Reserves in the Genhe River Basin of the Erguna Region on the Western Slope of the Greater Khingan Range (2023-2025)", "ds_format": "*.tif", "ds_source": "
Field sampling data: based on underground ice content 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; The environmental factor data comes from spatial datasets such as climate, soil, terrain, vegetation types, etc.\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 underground ice storage conforms to regional distribution patterns and has no significant outliers.", "ds_ref_way": "", "ds_abstract": "
This data is based on actual measured sample data of underground ice reserves obtained through mechanical/manual drilling and exploration pits. A random forest regression model is used to conduct spatial prediction modeling with measured volumetric water content (VWC) as the dependent variable and multiple environmental factors as independent variables. The model prediction results are output as grid data for different depth layers (2m above, 2-5m, 5m below), and finally synthesized and produced a map of permafrost underground ice storage at the research area scale. 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": "
Using Python and ArcGIS tools to process environmental factor data, a random forest regression model was used to predict the spatial ice content of 3-layer underground ice volume, and statistical analysis was conducted in conjunction with permafrost thickness data.", "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", "地下冰储量" ], "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" }