Linking optical remote sensing with carbon fluxes and biophysical parameters is critical to exploit spatial and temporal extensive information useful for validating model simulations at different scales. Proximal sensing is fundamental to quantify and understand the seasonal dynamics of ecosystems and to upscale the observations carried out at the ground level. In this study, we present the results from an ongoing research project at the FLUXNET eddy covariance site of Monte Bondone (Italy). The site is located at 1550 m a.s.l. on a mountain plateau in the Italian Alps (Viote del Monte Bondone). The area is managed as an extensively-managed meadow, cut once a year, and dominated by Nardus stricta and Festuca nigrescens. The climate of this area is sub-continental (warm and wet summer), with precipitation peaks in spring and autumn. A new hyperspectral system (WhiteRef Box, developed by Fondazione Edmund Mach in collaboration with the Institute of Biometeorology, CNR, Italy) based on the ASD FieldSpec spectrometer (spectral range 350-2500 nm, resolution ~3 nm at 700 nm) was designed to acquire continuous radiometric measurements. The system was installed on the eddy covariance tower at a height of 6 m, with a field of view of 25°. To obtain reflectance values, white panel radiance spectra and canopy radiance spectra were collected every 5 minutes between 10:00 a.m. and 1:00 p.m. (solar time) during the growing season of 2013. In addition, measurements of biophysical parameters such as above-ground biomass, fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Plant Area Index, Canopy Chlorophyll Content, Canopy Water Content and Green Herbage Ratio were performed at weekly intervals within the spectrometer footprint (~5 m2). In this work, we present some preliminary results regarding the potential of spectral vegetation indices - based on VNIR and SWIR spectral bands- for capturing seasonal trends of CO2 fluxes as well as vegetation biophysical parameters dynamics. Spectral vegetation indices sensitive to chlorophyll content (such as Meris Terrestrial ChIorophyll Index, Vogelmann Indices) showed a good linear correlation with fAPAR, daily Gross Primary Production and chlorophyll content (R2> 0.8 for all the three variables). The SWIR-based Vegetation Indices (e.g. Normalised Difference Infrared Index, Moisture Stress Index) confirmed their ability to estimate Canopy Water Content. Most of the analyzed indices showed to be linearly related with Green Herbage Ratio (explaining more than 80% of variance). The Near Infrared Difference Index (Vescovo et al., 2012) confirmed his potential in predicting canopy structural parameters such as Plant Area Index and biomass (R2> 0.90).

Vescovo, L.; Gianelle, D.; Marcolla, B.; Zaldei, A.; Sakowska, K. (2013). A new tower-based hyperspectral system for the estimation of CO2 fluxes and biophysical parameters in a subalpine grassland ecosystem. In: AGU Fall meeting, San Francosco, CA, 9-13 December 2013. url: http://adsabs.harvard.edu/abs/2013AGUFM.B43H..02V handle: http://hdl.handle.net/10449/24017

A new tower-based hyperspectral system for the estimation of CO2 fluxes and biophysical parameters in a subalpine grassland ecosystem

Vescovo, Loris;Gianelle, Damiano;Marcolla, Barbara;Sakowska, Karolina
2013-01-01

Abstract

Linking optical remote sensing with carbon fluxes and biophysical parameters is critical to exploit spatial and temporal extensive information useful for validating model simulations at different scales. Proximal sensing is fundamental to quantify and understand the seasonal dynamics of ecosystems and to upscale the observations carried out at the ground level. In this study, we present the results from an ongoing research project at the FLUXNET eddy covariance site of Monte Bondone (Italy). The site is located at 1550 m a.s.l. on a mountain plateau in the Italian Alps (Viote del Monte Bondone). The area is managed as an extensively-managed meadow, cut once a year, and dominated by Nardus stricta and Festuca nigrescens. The climate of this area is sub-continental (warm and wet summer), with precipitation peaks in spring and autumn. A new hyperspectral system (WhiteRef Box, developed by Fondazione Edmund Mach in collaboration with the Institute of Biometeorology, CNR, Italy) based on the ASD FieldSpec spectrometer (spectral range 350-2500 nm, resolution ~3 nm at 700 nm) was designed to acquire continuous radiometric measurements. The system was installed on the eddy covariance tower at a height of 6 m, with a field of view of 25°. To obtain reflectance values, white panel radiance spectra and canopy radiance spectra were collected every 5 minutes between 10:00 a.m. and 1:00 p.m. (solar time) during the growing season of 2013. In addition, measurements of biophysical parameters such as above-ground biomass, fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Plant Area Index, Canopy Chlorophyll Content, Canopy Water Content and Green Herbage Ratio were performed at weekly intervals within the spectrometer footprint (~5 m2). In this work, we present some preliminary results regarding the potential of spectral vegetation indices - based on VNIR and SWIR spectral bands- for capturing seasonal trends of CO2 fluxes as well as vegetation biophysical parameters dynamics. Spectral vegetation indices sensitive to chlorophyll content (such as Meris Terrestrial ChIorophyll Index, Vogelmann Indices) showed a good linear correlation with fAPAR, daily Gross Primary Production and chlorophyll content (R2> 0.8 for all the three variables). The SWIR-based Vegetation Indices (e.g. Normalised Difference Infrared Index, Moisture Stress Index) confirmed their ability to estimate Canopy Water Content. Most of the analyzed indices showed to be linearly related with Green Herbage Ratio (explaining more than 80% of variance). The Near Infrared Difference Index (Vescovo et al., 2012) confirmed his potential in predicting canopy structural parameters such as Plant Area Index and biomass (R2> 0.90).
Biosphere/atmosphere interactions
Instruments and techniques
Carbon cycling
2013
Vescovo, L.; Gianelle, D.; Marcolla, B.; Zaldei, A.; Sakowska, K. (2013). A new tower-based hyperspectral system for the estimation of CO2 fluxes and biophysical parameters in a subalpine grassland ecosystem. In: AGU Fall meeting, San Francosco, CA, 9-13 December 2013. url: http://adsabs.harvard.edu/abs/2013AGUFM.B43H..02V handle: http://hdl.handle.net/10449/24017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/24017
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