Retrieval of Multiple Variables from Hyperspectral Data: A PRISMA-Aligned Systematic Review of Classical Physics-Based Machine Learning and Hybrid Algorithms in Vegetation and Raw Materials Application Domains

Highlights What are the main findings? Machine learning and deep learning approaches dominate vegetation applications, while classical physics-based methods remain prevalent in raw materials, with hybrid models achieving the highest overall performance across domains. Across vegetation, mineral and methane mapping, insufficient standardization of accuracy metrics and incomplete methodological reporting represent the main sources of bias rather than algorithmic limitations. What are the implications of the main findings? Hybrid physics-informed machine learning approaches emerge as the most promising pathway to ensure robustness, transferability and operational readiness for upcoming hyperspectral missions. The results provide evidence-based guidance for algorithm selection and development aligned with user requirements of current and future hyperspectral missions, such as PRISMA, CHIME, SBG and IRIDE.Highlights What are the main findings? Machine learning and deep learning approaches dominate vegetation applications, while classical physics-based methods remain prevalent in raw materials, with hybrid models achieving the highest overall performance across domains.