An accurate estimation of thaw depth is critical to understanding permafrost changes due to climate warming on the Qinghai-Tibetan Plateau (QTP). However, previous studies mainly focused on the interannual changes of active layer thickness (ALT) across the QTP, and little is known about the changes in the seasonal thaw depth. Machine learning (ML) is a critical tool to accurately estimate the ALT of permafrost, but a direct comparison of ML with deep learning (DL) in ALT projection regarding the model performance is still lacking. Here, ML, namely randomforest (RF), and DL algorithms like convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks were compared to estimate the interannual changes of ALT and seasonal thaw depth on the QTP. Meteorological series, in-situ collected ALT observations, and geospatial information were used as predictors. The results show that both ML and DL methods are capable of estimating ALT and seasonal thaw depth in permafrost areas. The CNN and LSTM models developed using longer lagging times exhibit better performance in thaw depth prediction while the RF models are either mediocre or sometimes even worse as the lagging time increases. The results showthat the ALT from 2003 to 2011 on the QTP exhibits an increasing trend, especially in the northern region. In addition, 68.8%, 88.7%, 52.5%, and 47.5% of the permafrost regions on the QTP have deepened seasonal thaw depth in spring, summer, autumn, and winter, respectively. The correlation between air temperature and permafrost thaw depth ranges from 0.65 to 1 with the time lag ranging from 1 to 32 days. This study shows that ML and DL can be effectively used in retrieving ALT and seasonal thaw depth of permafrost, and could present an efficient way to figure out the interannual and seasonal variations of permafrost conditions under climate warming.

This paper presents the results of 39 years of observations conducted at the Chabyda station to monitor the thermal state of permafrost landscapes under current climatic warming. The analysis of long-term records from weather stations in the region has revealed one of the highest increasing trends in mean annual air temperature in northern Russia. The partitioning of the energy balance in different landscape units within the study area has been analyzed. Quantitative relationships in the long-term variability of ground thermal parameters, such as the ground temperature at the bottom of the active layer and seasonal thaw depth, have been established. The ground temperature dynamics within the depth of zero annual amplitude indicates that both warm and cold permafrost are thermally stable. The short-term variability of the snow accumulation regime is the main factor controlling the thermal state of the ground in permafrost landscapes. The depth of seasonal thaw is characterized by low interannual variability and exhibits little response to climate warming, with no statistically significant increasing or decreasing trend. The results of the ground thermal monitoring can be extended to similar landscapes in the region, providing a reliable basis for predicting heat transfer in natural, undisturbed landscapes.