Abstract
Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km2), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 105 km2 and 6.81 × 105 km2, respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.
Highlights
Permafrost accounts for about one quarter of the Northern Hemisphere and is an important component of the cryosphere [1]
The permafrost distribution obtained by the surface frost number (SFN) model and the top of permafrost (TTOP) model agreed excellently with each other with kappa coefficient (Ka) of 0.989 and overall accuracy (OA) of 99.5%
The extent of the permafrost region inferred from the SFN model and TTOP model were 6.88 × 105 km2 and 6.81 × 105 km2, respectively
Summary
Permafrost accounts for about one quarter of the Northern Hemisphere and is an important component of the cryosphere [1]. Permafrost could experience significant degradation due to global warming at the regional and global scales [5,6]. The permafrost degradation could lead to the melting of ground ice and substantial emissions of greenhouse gases (CO2, CH4 and N2O). Such a change will, in turn, have serious impacts on ecosystem [7,8], local hydrological system [9,10], and engineering projects [11,12]. Due to the obvious spatial difference of microclimatic factors, understanding the permafrost distribution at the regional scale is important for investigating the global permafrost distribution [16]
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