Quantitative estimation of sentinel-1A interferometric decorrelation using vegetation index - Pan, Jianping; Zhao, Ruiqi; Xu, Zhengxuan; Cai, Zhuoyan; Yuan, Yuxin (Frontiers in Earth Science, 2022)
Sentinel-1A data are widely used in interferometric synthetic aperture radar (InSAR) studies due to the free and open access policy. However, the short wavelength (C-band) of Sentinal-1A data leads to decorrelation in numerous applications, especially in vegetated areas. Phase blurring and reduced monitoring accuracy can occur owing to changes of the physical and chemical characteristics of vegetation during the satellite revisit period, which essentially makes the poor use of SAR data and increases the time and economic costs for researchers. Interferometric coherence is a commonly used index to measure the interference quality of two single-look complex (SLC) images, and its value can be used to characterize the decorrelation degree. The normalized difference vegetation index (NDVI) is obtained from optical images, and its value can be used to characterize the surface vegetation coverage. In order to solve the problem that Sentinel-1A decorrelation in vegetated area is difficult to estimate prior to SLC interference, this paper selects an vegetated area in Sichuan Province, China as the study area and establishes two two-order linear quantitative models between Landsat8-derived NDVI and Sentinel-1A interferometric coherence in co- and cross-polarization, then the two models are validated in an adjacent area. The results verify the reliability of the models and reveal the relationships between Sentinel-1A InSAR decorrelation and vegetation coverage in co- and cross- polarization, thus demonstrate that the NDVI can be applied to quantitatively estimate the InSAR decorrelation in vegetated area of Sentinel-1A data in both polarization modes prior to SLC interference.