%0 Dataset
%T 1km permafrost thickness map of Northeast China (2023-2024)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/4f607e2c-83e1-4445-a58d-e7e79ce81aa6
%W NCDC
%R 10.12072/ncdc.nieer.db7273.2026
%A LIU Guangyue
%A Zhao Lin
%K Permafrost;distribution of permafrost thickness;1km
%X This dataset provides a set of spatial distribution products of permafrost thickness based on geothermal gradient model and machine learning inversion, targeting the complexity of the spatial distribution of permafrost thickness in the Greater and Lesser Khingan Mountains in Northeast China. The research is based on limited deep hole (>20 m) ground temperature data, and uses a ground temperature gradient model to invert the deep ground temperature of shallow holes, calculate the depth of permafrost floor, and construct a basic training dataset containing 104 stations based on this. On this basis, precipitation (PRE), surface melting index (TDD), and terrain position index (TPI) are selected as key environmental prediction factors, and the Random Forest (RF) algorithm is applied to simulate and generate them. The results showed that the average thickness of permafrost in the study area was 47.71 ± 10 m, showing a significant spatial distribution pattern of "thick in the north and thin in the south, thick in the west and thin in the east, and thick in mountainous areas and plains". This dataset provides an important reference for estimating the thickness of permafrost in restricted areas of deep hole geothermal gradient measurement.