Combining AI Techniques with Physical Models: Forest Height Inversion from TanDEM-X InSAR Data Using a Hybrid Modeling Approach

Abstract

In the realm of artificial intelligence, specifically utilizing methodologies such as machine learning and deep learning, a conspicuous display of substantial potential across various parameter estimation problems has been demonstrated. However, such AI techniques are often employed without the incorporation of domain-specific knowledge or expertise, raising concerns about the explainability and robustness of the implemented methodologies. In contrast, physical models (PMs) offer a significantly enhanced level of deterministic robustness. However, it is imperative to recognize that these models can exhibit performance limitations owing to their inherent simplicity and/or strictness. Moreover, the accuracy of their inversion process is circumscribed by the assumptions and simplifications that underlie them, particularly those applied to the vertical reflectivity function, which are prerequisites for achieving a well-balanced inversion problem. As a result, it becomes imperative to advocate for hybrid modeling approach by the integration of AI techniques with physical models, especially in the context of forest height estimation derived from TanDEM-X coherence measurements. Accurate estimation of forest height is crucial for understanding forest structure and biomass, which in turn plays a pivotal role in climate change mitigation and ecosystem management. In this study, we propose a novel hybrid modeling approach that combines machine learning techniques and physical models to invert forest height from TanDEM-X InSAR (Interferometric Synthetic Aperture Radar) data. This approach might be relevant for the Biomass mission for understating the forest and its structures.