AI驱动的遥感反演从物理模型迈向基础模型，提升生态监测精度。
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion
- 从物理模型到基础模型，用AI实现多源遥感数据融合反演
- 基础模型在自监督预训练与跨任务适应上表现优异
- 适合遥感、环境监测、碳核算等领域研究人员参考
定量遥感反演旨在从卫星观测中估计生物量、植被指数和蒸散等连续地表变量，支撑生态系统监测、碳核算与土地管理。随着遥感系统与人工智能的发展，传统基于物理的范式正被数据驱动和基础模型（FM）方法取代。本文系统回顾了反演技术的方法演进，涵盖物理模型（如PROSPECT、SCOPE、DART）、机器学习方法（如深度学习、多模态融合）以及基础模型（如SatMAE、GFM、mmEarth）。对比分析了各范式的建模假设、应用场景与局限性，重点聚焦近期基础模型在自监督预训练、多模态融合与跨任务适应方面的进展。同时指出物理可解释性、领域泛化能力、标注数据有限及不确定性量化等持续挑战。展望下一代遥感反演基础模型的发展方向，强调统一建模能力、跨域泛化与物理可解释性。
原文摘要 · Abstract (English)
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.
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