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 map of permafrost thickness 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.
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.
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.
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.
Model validation: The five fold cross validation method is used to evaluate the prediction accuracy of the random forest model and ensure its reliability.
Spatial consistency check: Use ArcGIS to visually check and logically analyze the generated raster data, ensuring that the spatial distribution of permafrost thickness conforms to regional distribution patterns and has no significant outliers.
| # | number | name | type |
| 1 | 2022FY100700 | Survey of Permafrost Conditions and Freeze-Thaw Damage in the High-Latitude Regions of Northeast China | Basic Resource Survey Project |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.NIEER.DB7249.2026
10.12072/ncdc.nieer.db7249.2026
