Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems

Building the physics-driven mechanism model has always been the core scientific paradigm for parameter estimation in Earth surface systems, and developing the data-driven machine learning model is a crucial way for paradigm transformation in geoscience research. The coupling of mechanism and learning models can realize the combination of “rationalism” and “empiricism”, which is one of the most concerned research hotspots. In this paper, for remote sensing inversion and dynamic simulation, we deeply analyze the internal bottleneck and complementarity of mechanism and learning models and build a coupling paradigm framework with mechanism-learning cascading model, learning-embedded mechanism model, and mechanism-infused learning model. We systematically summarize ten specific coupling methods, including preprocessing and initialization, intermediate variable transfer, post-refinement processing, model substitution, model adjustment, model solution, input variable constraints, objective function constraints, model structure constraints, hybrid, etc., and analyze the main existing problems and future challenges. The research aims to provide a new perspective for in-depth understanding and application of the mechanism-learning coupling model and provide theoretical and technical support for improving the inversion and simulation capabilities of parameters in Earth surface systems and serving the development of Earth system science.