RELATING INTERFEROMETRIC SIGNATURE OF REPEAT PASS ERS-1 SAR SIGNALS TO DYNAMIC LAND COVER CHANGES

Abstract

Radar interferometry opens a very wide field of new applications especially to spaceborne Synthetic Aperture Radar (SAR) data. While repeat-pass SAR interferometry provides the possibility of producing topographic maps and geocoded as well as radiometrically calibrated radar images, interferometric coherence due to non-topographic phase change is a powerful tool to analyse the changes taking place in land covers. The degradation of coherence between two images is due to many effects and is not well understood. This degradation is originated either due to changing characteristics of the scatterers between the two images or due to the changes in permittivity driven by moisture migration. It is important to understand the process that determines the degree of coherence between images in repeat-pass SAR interferometry over different surface types. Considering the importance of this aspect, ERS-1 SAR repeat-pass data has been used to derive interferometric correlations between two images and analysis of these correlations with temporal changes, especially in surface soil moisture conditions, provides valuable thematic information. It is quite interesting to note that the changes in phase (represented by interferometric correlation) and intensity of backscatter are not uniform. While change in permittivity driven by moisture migration from bare soil conditions brings in changes in both phase and intensity, volume scattering as a function of canopy geometry, coupled with changes in permitivity from vegetated surfaces is more sensitive to phase than change in backscatter intensity especially between the two passes of 9 days. This reveals the sensitivity of phase information towards extremely dynamic land cover change in terms of soil moisture variations. A quantitative analysis on the interferometric correlation in combination with the backscatter intensity and the backscatter intensity change between the two passes reveals the crucial information which may be used for landuse/land cover classifications and monitoring and assessment of agricultural droughts in tropical regions.