%0 Dataset
%T Monthly Soil Surface Temperature Dataset around the Arctic (2003-2023)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/0fb47ccf-054c-4876-9476-34357dee2bb4
%W NCDC
%R 10.12072/ncdc.arctic-change.db7158.2026
%A Zhu Wenquan
%A Guo Hongxiang
%K Soil surface temperature;circumpolar region;multiple environmental factors;monthly modeling;MODIS land surface temperature
%X Soil surface temperature (SST) is an important variable for energy exchange between the earth and atmosphere, and a key indicator for evaluating changes in permafrost conditions. Remote sensing technology can invert large-scale, long-term land surface temperature data (LST). However, there are biases between land surface temperature and soil surface temperature due to factors such as vegetation and snow cover. This study is based on MODIS LST data, combined with five environmental factors including vegetation, snow cover, soil composition, terrain, and solar radiation. By establishing a random forest model between these data and soil surface temperature on a monthly basis, a monthly average SST dataset with a spatial resolution of 1 km around the Arctic (north of 45 ° N) from 2003 to 2023 was produced. Using station observation data for accuracy verification, it was found that the root mean square error (RMSE) range of the SST dataset is 1.14-2.09 ° C, with an R ² of 0.7 or above, which is superior to the SST dataset produced by existing seasonal multiple linear regression modeling methods. The accuracy advantage is significant in low vegetation areas (such as sparse tundra, grassland, wetlands, etc.) during the cold season (September to April of the following year) and high vegetation areas (such as forests) during the warm season (May to August). This dataset can provide fundamental support for the mapping of permafrost distribution and estimation of active layer thickness.