Robust machine learning techniques for rice crop variables estimation using multiangular bistatic scattering coefficients

The present study is designed to explore the potential of bistatic scattering coefficients (σ °) and machine learning algorithms for the estimation of rice crop variables using ground-based multiangular, multitemporal, and dual-polarized bistatic scatterometer data. The bistatic scatterometer measurements are carried out at eight different growth stages of the rice crop in the angular range of incidence angle 20 deg to 70 deg for HH- and VV-polarization at 10-GHz frequency in the specular direction with an azimuthal angle (φ  =  0). Several field measurements are taken for the measurement of rice crop variables, such as vegetation water content, leaf area index, and plant height at its various growth stages. Machine learning algorithms—such as fuzzy inference system (FIS), support vector machine for regression (SVR), and generalized linear model (GLM)—are used to estimate the rice crop variables using bistatic scatterometer data. The linear regression analysis is carried out for the evaluation of the multiangular, multitemporal, and dual-polarized datasets for the selection of optimum incidence angle and polarization for accurate estimation of rice crop variables. The highest value of the coefficient of determination (R2) is found at 30-deg incidence angle for VV-polarization. The sensitivity of copolarized ratio of σ °   with the rice crop variable is also evaluated using linear regression analysis for the estimation of rice crop variables. The highest value of R^2 is found to be at 35-deg incidence angle between the copolarized ratio of σ °   and rice crop variables. The performance of SVR model is found superior in comparison to the FIS and GLM at VV-polarization and the copolarized ratio of σ °   for the estimation of rice crop variables. However, the copolarized ratio of σ °   is found superior to VV-polarized bistatic scatterometer data for the estimation of rice crop variables.