Quantitative estimation of sentinel-1A interferometric decorrelation using vegetation index

Sentinel-1A data are widely used in interferometric synthetic aperture radar (InSAR) studies due to the free and open access policy. However, short wavelength (C-band) of Sentinal-1A leads decorrelation numerous applications, especially vegetated areas. Phase blurring reduced monitoring accuracy can occur owing changes physical chemical characteristics vegetation during satellite revisit period, which essentially makes poor use SAR increases time economic costs for researchers. Interferometric coherence is a commonly index measure interference quality two single-look complex (SLC) images, its value be characterize degree. The normalized difference (NDVI) obtained from optical surface coverage. In order solve problem that area difficult estimate prior interference, this paper selects Sichuan Province, China as study establishes two-order linear quantitative models between Landsat8-derived co- cross-polarization: When NDVI at extremely high low levels, close zero, while show different relationships cross-polarization terms middle level. global error basically obeys normal distribution with mean −0.037 −0.045, standard deviation 0.205 0.201 VV VH channels. then validated validation areas, results confirm reliability reveal InSAR coverage cross-polarization, thus demonstrating applied quantitatively both polarization modes SLC interference.