本数据集为大兴安岭东坡塔河地区卡马兰河流域多年冻土埋深数据，基于研究区内14个HOBO监测站点的地表温度（GST），构建“地表温度-环境因子”回归模型，计算融化指数。通过分类赋值法，计算E因子。采用Stefan模型，模拟典型区多年冻土活动层厚度。模拟结果的R2达到0.64，RMSE为0.41 m。数据格式为GeoTIFF，空间分辨率约30 m，投影为WGS_1984_Albers。", "ds_source": "
地表温度数据：研究区内14个HOBO监测站点的地表温度（GST），时间序列为2024年1月1日-12月31日。\n
DEM数据：航天飞机雷达地形测绘使命（SRTM）30 m数据。\n
植被类型数据：地球大数据科学工程数据共享服务系统的全球30 m地表覆盖精细分类产品。", "ds_process_way": "
对研究区内14个HOBO监测站点的地表温度（GST），进行异常值剔除与质量控制，计算逐日平均地表温度（GSTdaily）；将所有环境因子栅格统一投影为WGS_1984_Albers，重采样至30 m分辨率。利用ArcGIS的多值提取至点工具，获取14个HOBO站点处的环境因子数值；基于DEM提取坡向，重分类为阳坡、阴坡、半阴半阳坡及平坡4类。\n
“地表温度-环境因子”回归模型构建：统计日均温大于0℃的累积温度，获取各站点精确的实测融化指数（TDDobserved，单位：℃·day）。选取影响地表热状况的关键因子。基于 DEM 提取海拔（Elevation）、地形湿度指数（TWI）和归一化植被指数（NDVI）。以TDDobserved为因变量，海拔、TWI和NDVI为自变量，构建多元线性回归模型。将回归模型应用至全流域，利用环境因子栅格数据进行运算，生成研究区TDD初步预测数据。计算各站点实测值与预测值的残差。采用反距离权重法（IDW）对残差进行空间插值，生成连续的TDD残差表面。将初步预测图与残差表面叠加，获取经校正的最终融化指数分布数据。\n
“生态-地形”分类单元构建与E因子反演：在ArcGIS中进行栅格叠加分析，将地表覆盖与坡向组合，生成共计36类生态-地形单元。基于实测钻孔及坑探获取的活动层厚度（ALTobserved）及对应点位的融化指数（TDD），利用Stefan变形公式反推各实测点的E因子（Eobserved）。构建“分类单元-E因子”对应关系表，通过属性连接（Join），将E因子值赋予空间分类图层，生成空间连续的E因子分布数据。\n
活动层厚度（ALT）计算：基于Stefan方程逐像元计算研究区活动层厚度（ALTcalculated，单位为m）。为进一步降低模型偏差，对ALT模拟结果进行二次校正，计算实测点ALTobserved与模拟值ALTcalculated的偏差。同样利用IDW方法生成ALT残差表面，并将其与初步计算结果叠加，得到最终校正后的活动层厚度分布数据。", "ds_quality": "
“地表温度-环境因子”回归模型，回归方程决定系数（R2）为0.67。活动层厚度最终模拟结果与实测数据相比，决定系数（R2）达到0.64，均方根误差（RMSE）为0.41 m，满足制图精度要求。", "ds_acq_start_time": "2023-08-01 00:00:00", "ds_acq_end_time": "2025-10-31 00:00:00", "ds_acq_place": "大兴安岭东坡卡马兰河流域", "ds_acq_lon_east": 123.69111111111111, "ds_acq_lat_south": 51.75694444444444, "ds_acq_lon_west": 122.48611111111111, "ds_acq_lat_north": 52.46944444444445, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 22826699, "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": "79343ac7-0150-4af9-9d63-06046776924e.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 18:21:21", "last_updated": "2026-05-11 18:43:49", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7244.2026", "i18n": { "en": { "title": "30m Permafrost Depth Map of Kamalan River Basin in Dongpo Tahe Area of Daxing'anling Mountains (2023-2025)", "ds_format": "*.tif", "ds_source": "
Surface temperature data: The surface temperature (GST) of 14 HOBO monitoring stations in the study area, with a time series from January 1, 2024 to December 31, 2024.\r\n
DEM data: 30m data from the Space Shuttle Radar Topography Mission (SRTM).\r\n
Vegetation type data: Global 30 meter land cover fine classification product of the Earth Big Data Science Engineering Data Sharing Service System.", "ds_quality": "
The regression model of \"surface temperature environmental factors\" has a coefficient of determination (R2) of 0.67. Compared with the measured data, the final simulation result of the thickness of the active layer has a determination coefficient (R2) of 0.64 and a root mean square error (RMSE) of 0.41 m, which meets the requirements of mapping accuracy.", "ds_ref_way": "", "ds_abstract": "
This dataset is the burial depth data of permafrost in the Kamalan River Basin in the Dongpo Tahe area of the Greater Khingan Range. Based on the surface temperature (GST) of 14 HOBO monitoring stations in the study area, a regression model of \"surface temperature environmental factors\" was constructed to calculate the melting index. Calculate the E factor using the classification assignment method. Using the Stefan model, simulate the thickness of the active layer of permafrost in typical areas. The simulation result has an R2 of 0.64 and an RMSE of 0.41 m. The data format is GeoTIFF, with a spatial resolution of approximately 30 meters and a projection of WGS1984_ Albers.", "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": "
Perform outlier removal and quality control on the surface temperature (GST) of 14 HOBO monitoring stations in the study area, and calculate the daily average surface temperature (GSTdaily); Project all environmental factor grids uniformly as WGS1984-Albers and resample to a resolution of 30 meters. Using ArcGIS' multi value extraction tool to obtain environmental factor values at 14 HOBO sites; Based on DEM extraction of slope orientation, it is reclassified into four categories: sunny slope, shady slope, semi shady and semi sunny slope, and flat slope.\r\n
Construction of regression model for \"surface temperature environmental factors\": Accumulated temperature with daily average temperature greater than 0 ℃ is counted to obtain accurate measured melting index (TDDobservated, unit: ℃ · day) for each station. Select key factors that affect surface thermal conditions. Extract elevation, terrain moisture index (TWI), and normalized vegetation index (NDVI) based on DEM. Construct a multiple linear regression model with TDDobservated as the dependent variable and altitude, TWI, and NDVI as independent variables. Apply the regression model to the entire watershed, perform calculations using environmental factor raster data, and generate preliminary TDD prediction data for the study area. Calculate the residual between the measured and predicted values at each site. Using the inverse distance weighting method (IDW) for spatial interpolation of residuals to generate a continuous TDD residual surface. Overlay the preliminary prediction map with the residual surface to obtain the corrected final melting index distribution data.\r\n
Construction of ecological terrain classification units and E-factor inversion: Grid overlay analysis was performed in ArcGIS to combine surface cover and slope orientation, generating a total of 36 ecological terrain units. Based on the active layer thickness (ALTobserved) obtained from actual drilling and pit exploration, as well as the corresponding melting index (TDD) at each point, the E factor (Eoobserved) of each measured point is calculated using Stefan's deformation formula. Construct a correspondence table for \"classification units - E factors\", assign E factor values to spatial classification layers through attribute joins, and generate spatially continuous E factor distribution data.\r\n
Calculation of Active Layer Thickness (ALT): Calculate the ALT of the study area pixel by pixel based on Stefan's equation. To further reduce model bias, perform secondary correction on the ALT simulation results and calculate the deviation between the measured point ALTobserved and the simulated value ALTcalculated. Similarly, the IDW method is used to generate the ALT residual surface, which is then overlaid with the preliminary calculation results to obtain the final corrected active layer thickness distribution 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", "埋深", "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, 2025 ], "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": "郭殿繁", "email": "dfguo@hrbnu.edu.cn", "work_for": "哈尔滨师范大学", "country": "中国" }, { "true_name": "陈梦瑶", "email": "cmy_543@163.con", "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" }