Sun-Induced Fluorescence (SIF) estimates from remote sensing data are becoming more and more popular, from leaf level to global scale. This signal is a promising tool for the remote estimation of plants functioning, and its accurate quantification from space in the red and far red regions is the main aim of the Florescence Explorer (FLEX) mission, candidate for European Space Agency eighth Earth Explorer (ESA, EE-8). Although it is inherently related to plant photosynthesis efficiency, remotely sensed SIF signal is also highly influenced by environmental variables and vegetation biophysical and biochemical parameters. According to the recent Global Sensitivity Analysis (GSA) of the state-of-the-art Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model, the maximum rate of carboxylation (Vcmax) drives only a relatively small portion of the SIF signal. In turn, variables such as broadband incoming shortwave radiation (Rin), Leaf Chlorophyll Content (LCC) and Leaf Area Index (LAI) are major drivers of SIF intensity. Their variation in the spatial and temporal domains, if not properly accounted for, can lead to biased interpretation of the remotely sensed SIF signal in relation to the physiological behaviour of the vegetation. In this contribution we evaluate the potential of normalizing SIF by different quantities related to the incident photosynthetically active radiation (PAR), the photosynthetically active radiation absorbed by the canopy (APAR) and traditional vegetation indices linked to canopy greenness, for minimizing the influence of Rin, LAI and LCC on the SIF signal. The analysis is carried out using a simulated dataset generated with the SCOPE model, setting all variables fixed except for Rin, LAI and LCC. For these three variables, a broad range of input values is tested . The effect of the normalization methods is evaluated individually for several SIF wavelengths in the red and far-red emission regions. The indices related to APAR perform best among all investigated ones, and are followed by the vegetation indices linked to canopy greenness and finally by metrics related to the incoming radiation. The use of APAR performs well in normalizing the effect of LCC variations on both red and far-red SIF, while LAI influence is still affecting the red SIF even after the normalization. The metrics proposed for SIF normalization are then tested using an experimental dataset collected in August 2015 in an experimental farm in Northern Italy, over two monospecific soybean vegetation patches (a wild variety and a mutant one). Along with high spectral resolution top of canopy measurements, the main canopy biochemical and structural characteristics of the two soybean varieties were also measured. In-situ results confirm those obtained from the analysis of canopy fluorescence emission spectra simulated with the SCOPE model. The analysis is carried out using a simulated dataset generated with the SCOPE model, setting all variables fixed except for Rin, LAI and LCC. For these three variables, a broad range of input values is tested . The effect of the normalization methods is evaluated individually for several SIF wavelengths in the red and far-red emission regions. The indices related to APAR perform best among all investigated ones, and are followed by the vegetation indices linked to canopy greenness and finally by metrics related to the incoming radiation. The use of APAR performs well in normalizing the effect of LCC variations on both red and far-red SIF, while LAI influence is still affecting the red SIF even after the normalization. The metrics proposed for SIF normalization are then tested using an experimental dataset collected in August 2015 in an experimental farm in Northern Italy, over two monospecific soybean vegetation patches (a wild variety and a mutant one). Along with high spectral resolution top of canopy measurements, the main canopy biochemical and structural characteristics of the two soybean varieties were also measured. In-situ results confirm those obtained from the analysis of canopy fluorescence emission spectra simulated with the SCOPE model.

Celesti, M., Rossini, M., Panigada, C., Cogliati, S., Delle Vedove, G., Peressotti, A., et al. (2016). Towards an unbiased link between Sun-Induced chlorophyll Fluorescence and photosynthetic capacity: minimization of the effects of the main biophysical and environmental variables on the fluorescence signal. Intervento presentato a: ESA Living Planet Symposium 2016, Prague, Czech Republic.

Towards an unbiased link between Sun-Induced chlorophyll Fluorescence and photosynthetic capacity: minimization of the effects of the main biophysical and environmental variables on the fluorescence signal

CELESTI, MARCO
Primo
;
ROSSINI, MICOL
Secondo
;
PANIGADA, CINZIA;COGLIATI, SERGIO;COLOMBO, ROBERTO
Ultimo
2016

Abstract

Sun-Induced Fluorescence (SIF) estimates from remote sensing data are becoming more and more popular, from leaf level to global scale. This signal is a promising tool for the remote estimation of plants functioning, and its accurate quantification from space in the red and far red regions is the main aim of the Florescence Explorer (FLEX) mission, candidate for European Space Agency eighth Earth Explorer (ESA, EE-8). Although it is inherently related to plant photosynthesis efficiency, remotely sensed SIF signal is also highly influenced by environmental variables and vegetation biophysical and biochemical parameters. According to the recent Global Sensitivity Analysis (GSA) of the state-of-the-art Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model, the maximum rate of carboxylation (Vcmax) drives only a relatively small portion of the SIF signal. In turn, variables such as broadband incoming shortwave radiation (Rin), Leaf Chlorophyll Content (LCC) and Leaf Area Index (LAI) are major drivers of SIF intensity. Their variation in the spatial and temporal domains, if not properly accounted for, can lead to biased interpretation of the remotely sensed SIF signal in relation to the physiological behaviour of the vegetation. In this contribution we evaluate the potential of normalizing SIF by different quantities related to the incident photosynthetically active radiation (PAR), the photosynthetically active radiation absorbed by the canopy (APAR) and traditional vegetation indices linked to canopy greenness, for minimizing the influence of Rin, LAI and LCC on the SIF signal. The analysis is carried out using a simulated dataset generated with the SCOPE model, setting all variables fixed except for Rin, LAI and LCC. For these three variables, a broad range of input values is tested . The effect of the normalization methods is evaluated individually for several SIF wavelengths in the red and far-red emission regions. The indices related to APAR perform best among all investigated ones, and are followed by the vegetation indices linked to canopy greenness and finally by metrics related to the incoming radiation. The use of APAR performs well in normalizing the effect of LCC variations on both red and far-red SIF, while LAI influence is still affecting the red SIF even after the normalization. The metrics proposed for SIF normalization are then tested using an experimental dataset collected in August 2015 in an experimental farm in Northern Italy, over two monospecific soybean vegetation patches (a wild variety and a mutant one). Along with high spectral resolution top of canopy measurements, the main canopy biochemical and structural characteristics of the two soybean varieties were also measured. In-situ results confirm those obtained from the analysis of canopy fluorescence emission spectra simulated with the SCOPE model. The analysis is carried out using a simulated dataset generated with the SCOPE model, setting all variables fixed except for Rin, LAI and LCC. For these three variables, a broad range of input values is tested . The effect of the normalization methods is evaluated individually for several SIF wavelengths in the red and far-red emission regions. The indices related to APAR perform best among all investigated ones, and are followed by the vegetation indices linked to canopy greenness and finally by metrics related to the incoming radiation. The use of APAR performs well in normalizing the effect of LCC variations on both red and far-red SIF, while LAI influence is still affecting the red SIF even after the normalization. The metrics proposed for SIF normalization are then tested using an experimental dataset collected in August 2015 in an experimental farm in Northern Italy, over two monospecific soybean vegetation patches (a wild variety and a mutant one). Along with high spectral resolution top of canopy measurements, the main canopy biochemical and structural characteristics of the two soybean varieties were also measured. In-situ results confirm those obtained from the analysis of canopy fluorescence emission spectra simulated with the SCOPE model.
No
abstract + poster
ESA Programme: Future Missions (Sentinels, EE and Meteorological Programmes), Special Session, Bio-geophysical products, Sun-induced chlorophyll fluorescence
English
ESA Living Planet Symposium 2016
Celesti, M., Rossini, M., Panigada, C., Cogliati, S., Delle Vedove, G., Peressotti, A., et al. (2016). Towards an unbiased link between Sun-Induced chlorophyll Fluorescence and photosynthetic capacity: minimization of the effects of the main biophysical and environmental variables on the fluorescence signal. Intervento presentato a: ESA Living Planet Symposium 2016, Prague, Czech Republic.
Celesti, M; Rossini, M; Panigada, C; Cogliati, S; Delle Vedove, G; Peressotti, A; Sakowska, K; Miglietta, F; Colombo, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/131923
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