Analysing the potential of polarimetric decomposition parameters of Sentinel–1 dual-pol SAR data for estimation of rice crop biophysical parameters

Abstract

The potential of dual-polarization Sentinel–1 polarimetric decomposition parameters i.e., entropy, anisotropy and alpha angle, to monitor the biophysical parameters of rice crop namely, fresh biomass, dry biomass, vegetation water content (VWC) and plant height is investigated in this study. Multi-temporal Sentinel–1A dataset during critical growth stages of rice was considered for regression analysis between the polarimetric decomposition parameters and the biophysical parameters using linear and logarithmic models. The best correlations are obtained at early vegetation stages of the crop i.e. between tillering to booting stages. The maximum R2 value obtained is 0.6 for linear regression of entropy and VWC followed by other biophysical parameters. The correlation of polarimetric decomposition parameters with biophysical parameters is not as high as that of VH backscatter coefficient. Multiple regression using various Sentinel–1 parameters yields a better correlation than regression using individual parameters. The maximum R2 value of 0.765 is obtained for the multiple linear regression for VWC. Multiple regression using the backscatter coefficients and polarimetric decomposition parameters together gives a better correlation than that using only the backscatter coefficients. This indicate that the polarimetric decomposition parameters are sensitive to biophysical parameters and can be used as additional parameters along with the backscatter parameters for rice crop monitoring.

Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development

“…The scientific community has subsequently developed several versions of the RVI, including the Dual-pol Radar Vegetation Index (DpRVI- [2]), Compact-pol Radar Vegetation Index (CpRVI- [18]), and Polarimetric Radar Vegetation Index (PRVI- [19]). Besides these, scientists analyzed various SAR deliverables including H-alfa plane delivered from dual-pol polarimetric decomposition [20,21], or recently introduced three vegetation descriptors: the co-pol purity parameter (m cp ), the pseudo-scattering angle (θ cp ), and the pseudoscattering entropy (H cp ) [22]. The method combines the backscattering intensity and information of polarization decomposition to construct a normalized index q, which is used to generate these three vegetation indices.…”

“…Additionally, Zhao et al (2022) [3] proposed the Deep-Crop model, which combines optical and SAR time series to extract phenology, incorporating spatial-aware features. Dave et al (2023) [21] analyzed the potential of polarimetric decomposition parameters of Sentinel-1 dual-pol SAR data for the estimation of rice crop biophysical parameters. In their study they used multi-temporal Sentinel-1A images to calculate various indices (σ 0 VV , σ 0 V H , Entropy, Anisotropy, and Alpha) to investigate their correlation level with rice crop biophysical parameters.…”