Yuxuan Zhang

Interferometric phase optimization is a critical step in multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR). The advent of multi-polarimetric SAR satellites has boosted the polarimetric interferometric phase optimization techniques, where the polarimetric information can be used to enhance the interferometric phase quality. Polarimetric interferometric phase optimization approaches mainly include polarimetric coherence optimization (PCO) and polarimetric phase linking (PPL) algorithms. PCO algorithms that with good performance like Exhaustive Search Polarimetric Optimization (ESPO) are with high computational cost, especially for quad-polarimetric data cases. PPL algorithms mainly focus on distributed scatterers (DSs) and do not adopt the adaptive optimization for PS pixels. In addition, their DSs selection merely depends on the number of statistically homogeneous pixels (SHPs) through setting a threshold whose determination approach is usually not clearly defined. Moreover, the interferometric phase estimation results of PPL are affected by heterogeneous pixels. To overcome these limitations, by employing the total power polarization optimization and non-local PL, an adaptive polarimetric interferometric phase optimization algorithm for both PS and DS pixels is developed. The proposed algorithm has been evaluated using the simulated data, quad-polarimetric UAVSAR data, and dual-polarimetric Sentinel-1 data. The results demonstrate that in comparison with the previous methods, the proposed algorithm achieves superior interferometric phase quality, along with improved effectiveness and efficiency.

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.