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 (σ0vhij-σ0vvij/σ0vhij+σ0vvij\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }^{0}{vh}_{ij}- {\sigma }^{0}{vv}_{ij}/{\sigma }^{0}{vh}_{ij}+ {\sigma }^{0}{vv}_{ij}$$\end{document}), DPDD (σ0vhij+σ0vvij)/√2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(\sigma }^{0}{vh}_{ij}+ {\sigma }^{0}{vv}_{ij})/ \surd 2$$\end{document}), IDPDD (σ0vv(max)-σ0vvij)+σ0vhij/√2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma}^{0}{vv}_{(max)}- {\sigma }^{0}{vv}_{ij})+{\sigma }^{0}{vh}_{ij}/ \surd 2$$\end{document}), and VDDPI (σ0vhij+σ0vvij/σ0vvij)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\sigma }^{0}{vh}_{ij}+{\sigma }^{0}{vv}_{ij}/ {\sigma }^{0}{vv}_{ij})$$\end{document} 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.