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 ([Formula: see text]), DPDD [Formula: see text]), IDPDD ([Formula: see text]), and VDDPI [Formula: see text] 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 rain-fed 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 σvh° and σvv° bands throughout the period, and high R2 with σvh° (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 σvh°, σvv°, 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.
Keywords: Paddy; RF; SVIDP; SVM; Sentinel-1 SAR.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.