Solar-Induced chlorophyll Fluorescence (SIF) is a crucial parameter for Earth Observation, as it is strictly related to the vegetation health status. In particular, SIF can be easily monitored through optical remote sensing and offers unique information about vegetation functional state. In fact, SIF emission is one of the three main pathways exploited by vegetation to convert the Absorbed Photosynthetic Active Radiation (APAR) and it is related to pivotal parameters such as the Gross Primary Productivity (GPP), which is a key-role parameter in many environmental applications.
In this contribution, we present a novel approach based on an optimized multi-parameter retrieval algorithm to improve the understanding of the complex relationships between fluorescence and biophysical/biochemical variables. The proposed algorithm is specifically aimed to analyse the reflectance spectra acquired from the vegetation and consistently retrieve the Top Of canopy SIF spectrum, the SIF spectrum corrected for leaf/canopy reabsorption (i.e. at photosystem level), the quantum efficiency (SIFqe) and three canopy-related biophysical parameters (Leaf Area Index - LAI, Chlorophyll content - Cab and APAR) in few milliseconds. This algorithm mainly consists on a novel hybrid Phasor-Machine Learning based approach, which is exploited for the first time for quantitative retrievals in the context of remote sensing studies, and it is the first method capable to retrieve the full fluorescence spectrum at the photosystem level and the fluorescence quantum yield from experimental measurements acquired onsite.
More in detail, our approach exploits reflectance spectra, which are discrete-Fourier transformed on consecutive spectrally resolved complex planes. In each considered complex plane, the spectra which are characterized by the same biophysical and SIF parameters are projected in the same point. It is therefore possible to predict the unknown properties of a single reflectance spectrum by evaluating its projection position in each plane. In order to exploit, for each estimated parameter, the most suitable spectral windows and at the same time, avoiding possible superposition effects in some planes, the algorithm employs a supervised Machine Learning algorithm, trained with the atmosphere-canopy radiative transfer (RT) SCOPE model, which analyses the projection position of the reflectance spectrum in all the considered planes and estimates the investigated variables.
The algorithm has been validated by means of RT simulations, characterizing its retrieving accuracy by varying different parameters (spectral windows width, number of exploited phasor planes, size of the dataset, etc.) and by applying to the analysed spectra an increasing Poissonian noise, described in terms of signal to noise ratio (SNR). Considering the experimental conditions (SNR >= 500), the algorithm is able to independently estimate each biophysical parameter and SIF spectrum with a relative root mean square error (RRMSE) lower than 5%.
In order to investigate the seasonal and daily dynamics of SIF, LAI, Cab, SIFqe and APAR, the method has been also applied to field experimental data collected in the context of the AtmoFLEX and FLEXSense ESA campaigns. Field data were acquired in Grosseto (Italy) from two different crops (forage and Alfa Alfa) by means of the FLOX spectrometer accommodated on ground at top-of-canopy level and on high towers (~100 meters) in a deciduous forest of Downy Oak (France).
The retrieved annual dynamic for SIF spectra has been then compared with the results obtained by state of the art inversion-based methods, showing a good consistency between the two different approaches (RRMSE ~ 10%). Moreover, also the daily dynamics of the investigated variables behave accordingly to the results of the theoretical models.
The retrieval of SIF at the high tower has been investigated excluding the O2 spectral bands affected by the atmospheric reabsorption. The obtained results are promising and it has implications on tower-based measurements, where complex and computationally expensive atmospheric compensation techniques are needed to retrieve fluorescence from oxygen absorptions bands.
In summary, in this study we will show the results of the performance of this new algorithm and its ability in deriving accurate fluorescence spectrum corrected for reabsorption and fluorescence quantum yield from model simulation and real measurements collected over two crops and on a deciduous forest. Overall, the obtained results are of undoubted use and exploitation for the ESA Earth Explorer FLEX Mission, and this study demonstrates a promising potential to exploit ground and tower spectral measurements with advanced processing algorithms, for improving our understanding on the link between canopy structure and physiological functioning of plants and it can be straightforwardly employed to process reflectance spectra to open new perspectives in fluorescence retrieval at different scales.