Applications of Deep Learning for Correcting Tropospheric Phase Delays in InSAR: A Review

Tropospheric phase delay is a major error source that limits the accuracy of Interferometric Synthetic Aperture Radar (InSAR) deformation measurements, including those from the C- and X-band systems commonly examined in the studies reviewed here. Traditional correction methods, including external atmospheric models, spatiotemporal filtering, and phase–elevation empirical approaches, can partially mitigate atmospheric delays but often suffer from limited resolution, strong assumptions, or difficulties in separating atmospheric signals from complex surface deformation. Recent advances in deep learning provide new opportunities for InSAR tropospheric correction by learning nonlinear spatial and temporal relationships from interferometric observations and auxiliary information. This review provides a focused overview of deep learning-based approaches for correcting tropospheric phase delays in InSAR. Existing studies are organized according to their dominant modeling strategies, including convolutional neural network (CNN)-based spatial modeling, recurrent neural network (RNN)-based temporal modeling, generative adversarial network (GAN)-based generative learning, and other feedforward regression approaches. Key network-design strategies, training-label and dataset construction methods, and representative applications are discussed. Finally, current limitations and future research directions are discussed, emphasizing physical consistency, deformation preservation, cross-region generalization, and independent geodetic validation.