Estimation of crop variables using bistatic scatterometer data and artificial neural network trained by empirical models

Multi-temporal and multi-angular bistatic scatterometer measurements were carried out on two similar specially prepared kidney bean crop beds at two frequencies (6 GHz and 10 GHz) for like polarizations (HH- and VV-). The present study describes the estimation of crop variables and crop covered soil moisture of kidney bean crop using artificial neural network (ANN). The suitable configurations of bistatic scatterometer system were found at 10 GHz, 50° incidence angle for the estimation of kidney bean crop variables and 6 GHz, 20° incidence angle for the estimation of crop covered soil moisture at VV-polarization by linear regression analysis. Two artificial neural network models namely ANN-I and ANN-II were developed for the estimation of crop variables and crop covered soil moisture of kidney bean crop, respectively. The observed data set (scattering coefficients, crop variables and crop covered soil moisture) of first crop bed of kidney bean was used as a reference data set for developing empirical models. The training of the ANN-I model was done using 95 data set generated through empirical models consistent with the age of the kidney bean crop. The ANN-II was trained using the scattering coefficients and crop covered soil moisture of reference crop bed. The trained ANN-I and ANN-II models were tested by the observed data set of second kidney bean crop bed. The estimated values by ANN-I and ANN-II were found very close to the observed values of the crop variables and crop covered soil moisture of second kidney bean crop bed.