High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F760), and far-red apparent fluorescence yield (Fy$*$760). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy$$760 showed high correlations with the reference variables (R2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy$_$760 were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m2 m-2. The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R2 = 0.70) and Fy$*$760 (R2 = 0.68) when discarding data modeled with LAI $<$ 1 m2 m-2 and short-wave radiation Rin $<$ 250 W m-2. The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE$$ showed a stronger correlation with the fast SSA-decomposed component of Fy$_$760 (R2 = 0.42) than with original Fy$_*$760 (R2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.