Analysis of Permafrost Distribution and Change in the Mid-East Qinghai–Tibetan Plateau during 2012–2021 Using the New TLZ Model

• Published in: Remote Sensing
• Main Authors: Zhijian Zhao, Hideyuki Tonooka
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: permafrost; zero-curtain; temperature at the top of the permafrost (TTOP); land surface temperature; subsurface temperature; active layer; Science; Q
• Online Access: https://doi.org/10.3390/rs14246350; https://doaj.org/article/e7a64d1cc0c1487383e2a4369f0880bb

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation–evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai–Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.