Multiresolution Cross-Attention–Transformer for Spatiotemporal Prediction of Permafrost Deformation in InSAR Time Series

The Qinghai-Tibet Plateau (QTP) has the largest area of permafrost terrain in the mid- and low-latitude regions of the world. Under the combined influence of global change and human activities, the permafrost on the QTP is undergoing widespread degradation, which will not only have significant impacts on the global environment and carbon cycle, but also result in permafrost-related geohazards. Surface deformation resulting from the freeze-thaw process is an important characteristic of permafrost, and predicting it is of great significance for understanding the development of permafrost. However, intrinsic complexity and multifactorial environmental controls limit the accuracy and generalizability of existing prediction models. In this paper, based on the deformation results from the InSAR technique, an integrated algorithm for permafrost deformation prediction is proposed. The algorithm combines wavelet multi-resolution analysis and a Multi-Resolution Cross-Attention (MRCA)-Transformer model, in which both periodic and non-periodic deformation features, together with environmental variables such as soil moisture and temperature, are incorporated through a multi-head cross-attention mechanism for spatiotemporal prediction. The proposed model is trained and evaluated using the InSAR time series deformation derived from 2017-2024 Sentinel-1A data over the QTP permafrost region. The model achieves an average root-mean-square error (RMSE) of 4.97 mm, an average mean absolute error (MAE) of 4.22 mm, with standard deviations of 3.21 mm and 2.89 mm, respectively. The square of the correlation coefficient and the coefficient of determination are both greater than 0.99 across all time steps. Moreover, the results show that the model demonstrates effective performance across a variety of terrains and observational conditions without additional training. This study provides significant scientific insights and practical value for the monitoring of permafrost evolution, environmental assessment, and early warning of geohazards.