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- W4290785215 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 ( R app ). In particular, R app 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 R app 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 R app 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. • Development of a new combined ML/phasor-based method to analyze hyperspectral data. • Fluorescence spectra retrieval at canopy, photosystem level and quantum efficiency. • Fluorescence and biophysical metrics estimated from apparent reflectance spectra. • Successful application of the proposed method to agricultural crops and forests." @default.
- W4290785215 created "2022-08-09" @default.
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- W4290785215 date "2022-10-01" @default.
- W4290785215 modified "2023-10-16" @default.
- W4290785215 title "A novel hybrid machine learning phasor-based approach to retrieve a full set of solar-induced fluorescence metrics and biophysical parameters" @default.
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- W4290785215 doi "https://doi.org/10.1016/j.rse.2022.113196" @default.
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