Physics-Guided Machine Learning-Based Forward-Modeling of Radar Observables: A Case Study on Sentinel-1 Observations of Corn-Fields

Abstract

Artificial neural networks have the potential to model the interaction of radar signals with vegetation but often do not follow the physical rules. This article aims to develop a new physics-guided machine learning approach that combines neural networks and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. We propose a data-driven framework to model synthetic aperture radar observables by incorporating physical knowledge in two ways: through the network architecture and the loss function. A key aspect of our approach is its ability to integrate knowledge encoded in physics-based models. The results show that by using scientific knowledge to guide the construction and learning of the neural network, we can provide a framework with better generalizability and stability.