A Physics-Based ML Approach for Corn Plant Height Estimation with Simulated Sar Data

We present a physics-based machine learning (ML) approach for estimating corn plant height from simulated synthetic aperture radar (SAR) data. The proposed study intends to demonstrate the physical awareness of data-driven approaches such as ML. In this regard, a multi-layer perceptron (MLP) artificial neural network (ANN), designed for corn plant height estimation, was trained with simulated C- and L-band SAR data generated using a state of the art electromagnetic model for microwave backscattering from terrain covered with vegetation. Here we show how the most significant connections between the nodes composing the network and the most relevant input variables can be detected, demonstrating the physical meaning behind the mapping criteria of the network itself.