Satellite-observed solar-induced chlorophyll fluorescence (SIF) provides a direct radiative link between solar radiation, photosystem de-excitation and vegetation photosynthetic activity. As multiple satellite missions now deliver global SIF products, machine learning (ML) has become a key tool for: (i) flexible nonlinear SIF retrieval, (ii) spatial reconstruction and downscaling of SIF fields, (iii) full-spectrum SIF reconstruction beyond narrow absorption windows, and (iv) data-driven analysis of the SIF–gross primary production (GPP) relationship. In addition, ML methods are increasingly used for: (v) uncertainty quantification (UQ) along the SIF information chain, and (vi) emulation (i.e., surrogate modelling) of radiative transfer models (RTMs) to accelerate computationally demanding SIF workflows. This review provides a conceptual and methodological survey of recent ML applications across the satellite SIF processing chain, summarises emerging products and methods, and highlights open challenges in uncertainty treatment, spectral reconstruction, and hybrid RTM–ML approaches. Particular emphasis is placed on the upcoming ESA FLEX mission, planned for launch in 2026, which will deliver multi-band SIF observations optimised for photosynthesis monitoring. While FLEX Level-2 (L2) operational processing will be based on physically grounded retrieval algorithms developed within ESA projects, ML is expected to play an important role in scientific exploitation and in the development of higher-level products (L3/L4), supporting high-resolution, uncertainty-aware SIF and GPP products and helping to bridge scales from leaf to ecosystem.
Verrelst et al. (Mon,) studied this question.