The emission of solar-induced chlorophyll fluorescence (F) is a pivotal process to infer vegetation health and functioning that can be monitored by remote sensing. However, most of the current remote sensing methods retrieve only F at top-of-canopy level, therefore making the link with physiological processes occurring at photosystem level not trivial. In this study, we develop a novel machine learning Fourier (phasor)-based algorithm to retrieve F both at canopy level and after considering the reabsorption (i.e. photosystem level), consistently with relevant biophysical variables, exploiting the canopy apparent reflectance spectra (Rapp). In particular, Rapp is divided in consecutive spectral windows, where the Discrete Fourier Transform (DFT) is computed. Then, the DFT results in each window are exploited to estimate the fluorescence spectra and biophysical parameters, together with their uncertainties, by means of a supervised machine learning algorithm coupled to a statistical-based retrieval pipeline. The algorithm has been trained through synthetic Rapp spectra, obtained from simulations based on a Radiative Transfer (RT) model. As a proof of concept, the theoretical approach is then applied to experimental data, acquired both from crops and forests, at close and high soil-sensor distance respectively, to evaluate the retrieval accuracy of biophysical and F parameters. In particular, for the first time Rapp is used to extract the temporal evolution of F at canopy and photosystem levels and its quantum efficiency together with different biophysical variables, during the growing season of two agricultural crops. Furthermore, tower-based solar-induced fluorescence measurements in a deciduous forest are exploited to evaluate the performance of our algorithm when the atmospheric reabsorption and scattering are not negligible. The reliability of the proposed method is evaluated through a comparison with F spectra extracted from the state of the art SpecFit retrieval algorithm. This work promises a substantial advance toward a new accurate retrieval method for fluorescence signals and biophysical parameters at canopy and photosystem levels.

Scodellaro, R., Cesana, I., D'Alfonso, L., Bouzin, M., Collini, M., Chirico, G., et al. (2022). A novel hybrid machine learning phasor-based approach to retrieve a full set of solar-induced fluorescence metrics and biophysical parameters. REMOTE SENSING OF ENVIRONMENT, 280(October 2022) [10.1016/j.rse.2022.113196].

A novel hybrid machine learning phasor-based approach to retrieve a full set of solar-induced fluorescence metrics and biophysical parameters

Scodellaro R.
Primo
;
Cesana I.;D'Alfonso L.;Bouzin M.;Collini M.;Chirico G.;Colombo R.;Celesti M.;Cogliati S.
Co-ultimo
;
Sironi L.
Co-ultimo
2022

Abstract

The emission of solar-induced chlorophyll fluorescence (F) is a pivotal process to infer vegetation health and functioning that can be monitored by remote sensing. However, most of the current remote sensing methods retrieve only F at top-of-canopy level, therefore making the link with physiological processes occurring at photosystem level not trivial. In this study, we develop a novel machine learning Fourier (phasor)-based algorithm to retrieve F both at canopy level and after considering the reabsorption (i.e. photosystem level), consistently with relevant biophysical variables, exploiting the canopy apparent reflectance spectra (Rapp). In particular, Rapp is divided in consecutive spectral windows, where the Discrete Fourier Transform (DFT) is computed. Then, the DFT results in each window are exploited to estimate the fluorescence spectra and biophysical parameters, together with their uncertainties, by means of a supervised machine learning algorithm coupled to a statistical-based retrieval pipeline. The algorithm has been trained through synthetic Rapp spectra, obtained from simulations based on a Radiative Transfer (RT) model. As a proof of concept, the theoretical approach is then applied to experimental data, acquired both from crops and forests, at close and high soil-sensor distance respectively, to evaluate the retrieval accuracy of biophysical and F parameters. In particular, for the first time Rapp is used to extract the temporal evolution of F at canopy and photosystem levels and its quantum efficiency together with different biophysical variables, during the growing season of two agricultural crops. Furthermore, tower-based solar-induced fluorescence measurements in a deciduous forest are exploited to evaluate the performance of our algorithm when the atmospheric reabsorption and scattering are not negligible. The reliability of the proposed method is evaluated through a comparison with F spectra extracted from the state of the art SpecFit retrieval algorithm. This work promises a substantial advance toward a new accurate retrieval method for fluorescence signals and biophysical parameters at canopy and photosystem levels.
Si
Articolo in rivista - Articolo scientifico
Scientifica
Biophysical parameters; Field spectroscopy; FLEX mission; Fluorescence quantum yield; Solar-induced chlorophyll fluorescence;
English
Scodellaro, R., Cesana, I., D'Alfonso, L., Bouzin, M., Collini, M., Chirico, G., et al. (2022). A novel hybrid machine learning phasor-based approach to retrieve a full set of solar-induced fluorescence metrics and biophysical parameters. REMOTE SENSING OF ENVIRONMENT, 280(October 2022) [10.1016/j.rse.2022.113196].
Scodellaro, R; Cesana, I; D'Alfonso, L; Bouzin, M; Collini, M; Chirico, G; Colombo, R; Miglietta, F; Celesti, M; Schuettemeyer, D; Cogliati, S; Sironi, L
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/391230
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