Scodellaro, R. and Cesana, I. and D’Alfonso, L. and Bouzin, M. (2023) i-φ-MaLe: A novel hybrid machine learning phasor-based approach to retrieve a full-set of solar-induced fluorescence metrics and biophysical parameters. Il nuovo cimento C, 46 (5). pp. 1-4. ISSN 1826-9885
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Abstract
Solar-induced fluorescence (F) is crucial to monitor vegetation health, as it provides information about photosynthetic processes. Our new method, i-φ-MaLe, simultaneously estimates F spectra, Leaf Area Index (LAI), Chlorophyll Content (Cab), Absorbed Photosynthetic Active Radiation (APAR) and F Quan- tum Yield (Fqe) from canopy reflectance spectra by coupling the phasor approach with Machine Learning (ML) techniques. We validated i-φ-MaLe on simulations and spectra acquired for increasing spectrometer-canopy distances, up to 100 m (where O2 bands are affected by atmospheric oxygen absorption). The reliability of i-φ- MaLe in such complex experimental scenarios paves the way to new perspectives concerning the real time monitoring of vegetation stress status on high scales.
| Item Type: | Article |
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| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 08 Mar 2024 13:54 |
| Last Modified: | 08 Mar 2024 13:54 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/22724 |
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