From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

TL;DR

This review discusses the evolution of remote sensing inversion methods from physics-based models to foundation models, highlighting recent advances, challenges, and future directions in AI-driven quantitative remote sensing.

Contribution

It systematically compares different inversion paradigms, emphasizing recent foundation model developments and proposing future research directions for improved remote sensing inversion.

Findings

Foundation models enable better multi-modal integration.

Recent advances improve cross-task adaptation.

Challenges remain in interpretability and domain generalization.

Abstract

Quantitative remote sensing inversion aims to estimate continuous surface variables-such as biomass, vegetation indices, and evapotranspiration-from satellite observations, supporting applications in ecosystem monitoring, carbon accounting, and land management. With the evolution of remote sensing systems and artificial intelligence, traditional physics-based paradigms are giving way to data-driven and foundation model (FM)-based approaches. This paper systematically reviews the methodological evolution of inversion techniques, from physical models (e.g., PROSPECT, SCOPE, DART) to machine learning methods (e.g., deep learning, multimodal fusion), and further to foundation models (e.g., SatMAE, GFM, mmEarth). We compare the modeling assumptions, application scenarios, and limitations of each paradigm, with emphasis on recent FM advances in self-supervised pretraining, multi-modal integration, and cross-task adaptation. We also highlight persistent challenges in physical interpretability, domain generalization, limited supervision, and uncertainty quantification. Finally, we envision the development of next-generation foundation models for remote sensing inversion, emphasizing unified modeling capacity, cross-domain generalization, and physical interpretability.