Optimization of dual-polarized bistatic specular scatterometer for studying microwave scattering response and vegetation growth parameters retrieval of paddy crop using a machine learning algorithm

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 R2 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.

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

Bistatic scatterometer measurements were performed on the rice crop-bed in the angular range of 20° to 60° for specular direction (ϕ=0) at X-, C- and L-bands for HH-, VV-, and HV-polarizations. The...

Bistatic specular scattering measurements for the estimation of rice crop growth variables using fuzzy inference system at X-, C-, and L-bands

Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X- and C-band

The present study describes the ground based bistatic scatterometer measurements of ladyfinger crop at its various growth stages in the specular direction with the azimuthal angle (
 $$ \phi = 0 $$
 ) for the angular incidence angle ranging from 20° to 60° at the interval of 10° at HH and VV polarization. An outdoor ladyfinger crop bed of an area 4 × 4 m2 was specially prepared for the ground based bistatic scatterometer measurements. The crop growth variables like vegetation water content, leaf area index, fresh biomass, and plant height were also measured at the time of each bistatic scatterometer measurement. The specular bistatic scattering coefficients were found to be decreasing with the crop growth variables up to the maturity stage and then after it increased slightly. The linear regression analysis was carried out between specular bistatic scattering coefficient and crop growth variables at all the incidence angles for HH and VV polarization to select the optimum angle of incidence and polarization for the estimation of crop growth variables. The potential of subtractive clustering based adaptive neuro-fuzzy inference system was applied for the estimation of crop growth variables. The estimated values for different crop growth variables were found almost close to the observed values.

Ground Based Bistatic Scatterometer Measurement for the Estimation of Growth Variables of Ladyfinger Crop at X-Band

Compared with the backscattering configuration, the bistatic scattering echoes can provide multidimensional information on land surface. Based on the Michigan Microwave Canopy Scattering (MIMICS) model, a first-order microwave bistatic scattering model for vegetations is developed in this paper. The dominant scattering mechanism for wheat and soybean in the L and C bands is analyzed by simulating the bistatic scattering echoes in multiple viewpoints, which can help us understand the interaction between incident wave and vegetation parameters. The influence of crop height, leaf size and moisture of vegetations and down layer soil on the scattering echoes is fully investigated. The simulations show that the bistatic scattering echoes are more sensitive to the vegetation parameters than that in backscattering configuration. There exist optimal scattering angles, in specular direction and in direction perpendicular to the incident plane, to improve the retrieval accuracy of vegetation parameters and moisture of soil surface. Moreover, the simulations demonstrate that bistatic scattering echoes in high frequency (C band) are a good choice to retrieve the vegetation parameters, and the echoes in low frequency (L band) are preferred to retrieve the soil parameters. This research can be used to provide reference for crop monitoring and future bistatic system design.

https://www.jpier.org/pierb/pierb67/09.16011502.pdf

Zhen-Sen Wu

Papers

Bistatic Scattering Characteristics of Wheat and Soybean by Radiative Transfer Model in L Band and C Band