i-$\phi$-male: A novel hybrid machine learning phasor-based approach to retrieve a full-set of solar-induced fluorescence metrics and biophysical parameters.

Solar-induced Fluorescence (F) is a pivotal parameter to monitor vegetation health as it provides crucial information on photosynthetic processes. Actually, only the atmospheric O$_2$-bands of high-resolution apparent reflectance spectra ($R_$app$$) are exploited to retrieve F. We propose a new method, i-$\phi$-male, based on the phasor approach coupled to supervised machine learning (S-ML) techniques. The 650-800nm $R_app$ spectra are Discrete Fourier transformed on consecutive spectral windows, where the S-ML algorithm, trained with a radiative transfer (RT) model, estimates: F, Leaf Area Index, Chlorophyll Content, Absorbed Photosynthetic Active Radiation, F Quantum Yield and F at photosystem level. We validated i-$\phi$-male on RT simulations (error $<$ 3% $)$ and provided analysis of field measurements acquired for increasing spectrometer-canopy distances, up to 100m (where $O_2$ bands are affected by atmospheric oxygen absorption). The simultaneous retrieval of biophysical variables and F from spectra acquired at multiple distances from the canopy paves the way to new important perspectives related to the real time monitoring of vegetation stress level on high scales.