Microwave Backscatter Phenomenology of Corn Fields at L-Band Using a Full-Wave Electromagnetic Solver

Satellite and airborne radars currently monitor agricultural regions on Earth. Corn is a globally important crop that may benefit from radar observations for estimating soil moisture (SM) and other quantities. Estimates of SM could be used to enhance crop yield and aid in weather prediction. A scattering model is needed, however, to accurately estimate these quantities. Historically, corn is a difficult crop to model at microwave frequencies, and only approximate models for it exist. Novel models based on full-wave electromagnetic solvers can be more accurate by accounting for multiple scattering among plant constituents, other adjacent plants, and the underlying soil surface. Such a model is computationally expensive, but the increased availability of computing resources may make it more feasible. This article presents a model for corn at L-band based on finite element method (FEM) simulations in conjunction with Monte Carlo methods to estimate polarimetric backscattering coefficients. The FEM simulation uses periodic boundary conditions to limit its size. The physical representation of the corn plants comes from data-based 3-D plant models. The results of simulations are validated with synthetic aperture radar (SAR) data obtained during the SM active passive validation experiment of 2012 (SMAPVEX12) experimental campaign. The estimated backscattering coefficients of the SAR data are within ±2 dB for all polarization channels. Validation is performed for two days within the experimental campaign. Good agreement is observed between the simulated and measured values. This result indicates that the model can give novel insights into the scattering characteristics of corn. Future work remains to build an invertible model for estimating SM from backscatter measurements.