THE MONITORING AND SPATIAL-TEMPORAL EVOLUTION CHARACTERISTIC ANALYSIS FOR LAND SUBSIDENCE IN BEIJING

This study developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1° spatial resolution by covering the period from 2001 to 2020. The Phy-DL FMF was extensively evaluated against ground-truth AERONET data and tested on a global scale against conventional satellite-based FMF products to demonstrate its superiority in accuracy.

To overcome the problem that layover scatterers with no amplitude stability and spatial coherence are lead to reliability insufficient and accuracy reduction in monitoring urban land subsidence, we applied the Fast Fourier Transform to convert Persistent Scatterers to frequency domain during the PS-InSAR identification process. The method could identify and separate single and layover scatterers, reduced the effect of layover scatterers, improved the accuracy of urban land subsidence monitoring.