Crop classification by using dual-pol SAR vegetation indices derived from Sentinel-1 SAR-C data

In the following study, an attempt is made for crop classification of rainy season through analyzing time-series Sentinel-1 SAR data of May 2020 to September 2020. The SVIDP index derived from dual-pol (VV and VH) bands consisting of NRPB (sigma(0)vh(ij) - sigma(0)vv(ij)/sigma(0)vh(ij )+ sigma(0)vv(ij)), DPDD (sigma(0)vh(ij) + sigma(0)vv(ij))/root 2), IDPDD (sigma(0)vv((max) )- sigma(0)vv(ij)) + sigma(0)vh(ij)/root 2), and VDDPI (sigma(0)vh(ij) + sigma(0)vv(ij)/sigma(0)vv(ij)) ratios are utilized for discriminating inter-vegetative boundaries of crop pixels. This study was conducted near Karnal city region, Karnal district, Haryana, India. The Sentinel-1 data has the capability to penetrate thick cloud cover and provide high revisit frequency data for rainfed crops. Obtained classification achieved higher accuracy in both RF (93.77%) and SVM (93.50%) classifiers. Obtained linear regression statistics of mean raster imagery reveals that IDPDD index is much sensitive to other crop which has highest standard deviations in sigma(vh)degrees and sigma(vv)degrees bands throughout the period, and high R-2 with sigma(vh)degrees (0.70), VV (0.58), NRPB (0.693), and DPDD (0.697) indices. In contrast to this, IDPDD index has least correlation (< 0.289) with sigma(vh)degrees , sigma(vv)degrees, EVI 2, NRPB, and DPDD indices for water body which has smooth surface and lowest SAR backscattering with minimum standard deviations in the same period.