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，模拟多年冻土活动层厚度。模拟结果的R2为0.73。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。\n
获取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，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：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）。\n
构建“分类单元-E因子”对应关系表，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与相应模拟的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，得到校正后的活动层厚度分布数据。", "ds_quality": "
活动层厚度模拟结果与实测数据的R2为0.73。", "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": 5, "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": "2026-09-28 17:30:37", "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 R2 between the simulated results of active layer thickness and the measured data is 0.73.", "ds_ref_way": "", "ds_abstract": "
This dataset is the permafrost buried depth data of the Kamaran River Basin in the Tahe area on the east slope of the Daxinganling Mountains. Based on the surface temperature (GST) of the HOBO monitoring station in the study area, a \"surface temperature-environmental factor\" regression model is constructed to calculate the melting index. Calculate the E factor through the classification assignment method. The Stefan model is used to simulate the thickness of the active layer of permafrost. The R2 of the simulation results is 0.73. The data format is GeoTIFF, with a spatial resolution of approximately 30 m, and the projection is WGS_1984_Albers.", "ds_time_res": "3 years", "ds_acq_place": "Kamaran River Basin on the East Slope of Daxing'an Mountains", "ds_space_res": "30m", "ds_projection": "WGS_1984_Albers", "ds_process_way": "
The surface temperature (GST) at HOBO monitoring stations in the study area was eliminated and quality controlled, and the daily average surface temperature (GSTdaily) was calculated; all environmental factor grids were uniformly projected into WGS_1984_Albers, and re-sampled to 30m resolution.\r\n
Obtain environmental factor values at the HOBO site; extract slope direction based on DEM, and reclassify them into four categories: sunny slope, shaded slope, semi-shaded and semi-sunny slope, and flat slope. \r\n
Construction of the \"surface temperature-environmental factor\" regression model: Calculate the cumulative temperature with daily average temperature greater than 0℃, and obtain the accurate measured melting index (TDObserved, unit: ℃·day) at each station. Select key factors affecting surface thermal conditions, and extract Elevation, Topographic Moisture Index (TWI) and Normalized Vegetation Index (NDVI). A multiple linear regression model was constructed with TDObserved as the dependent variable and altitude, TWI and NDVI as independent variables. The regression model was applied to the entire basin, and grid data of environmental factors was used for calculations to generate preliminary TDD prediction data in the study area. Calculate the residuals between the measured values and the predicted values at each station, and use the inverse distance weighting method (IDW) to spatially interpolate the residuals to generate a continuous TDD residual surface. The preliminary prediction map is superimposed on the residual surface to obtain corrected final melt index distribution data. \r\n
Construction of \"ecology-terrain\" classification units and inversion of E-factor: A total of 36 types of ecology-terrain units were generated by combining surface coverage and slope direction. Based on the active layer thickness (ALTobserved) obtained from measured borehole and pit exploration and the melting index (TDD) of the corresponding points, the E factor (Eobserved) of each measured point is inversely calculated using Stefan deformation formula.\r\n
Build a \"classification unit-E factor\" correspondence table, assign the E factor value to the spatial classification layer, and generate spatially continuous E factor distribution data. \r\n
Calculation of active layer thickness (ALT): Calculate the active layer thickness (ALTcalculated, in m) in the study area based on Stefan equation. In order to further reduce the model deviation, the ALT simulation results were corrected twice, and the deviation between the measured point ALTobserved and the corresponding simulated ALTcalculated was calculated. The IDW method is also used to generate the ALT residual surface and superimposed with the preliminary calculation results to obtain the 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, "belong_to_nieer": false, "allow_update_data": false, "created_from": "fair", "ds_topic_tags": [ "多年冻土埋深", "大兴安岭", "卡马兰河流域", "中国东北" ], "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" }, { "true_name": "余江涛", "email": "yujiangtao23@163.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" }