Predicting Ecosystem-scale CO2 Fluxes and Vegetation Biophysical Parameters of a Subalpine Grassland with Continuous Canopy Hyperspectral Reflectance Measurements.

Abstract:

This study investigates the potential of the ASD-WhiteRef system for monitoring CO₂ fluxes and vegetation biophysical parameters (such as e.g. fraction of absorbed photosynthetically active radiation - fapar, canopy total chlorophyll content - TotChl). The ASD-WhiteRef is an automated system designed for continuous and unattended acquisition of radiometric data using an ASD FieldSpec Pro spectroradiometer. The ASD-WhiteRef system was installed in May 2013 at the EC tower (at a height of 6 m, with a field of view of 25°) of the FLUXNET Monte Bondone site (IT-MBo), which is a representative of a typical extensively-managed, low-productive meadow of the Italian Alps.

Vegetation hyperspectral reflectance and EC observations were collected on a continuous basis for three growing seasons covering periods of extreme weather conditions (both hot/dry and rainy periods), while fapar and TotChl were determined periodically (at around weekly intervals) during two growing seasons at different vegetation development stages by means of line quantum sensors and UV-VIS spectroscopy method, respectively.

In order to characterize the interannual dynamics in grassland CO₂ fluxes three approaches were used: i) linear regression between CO₂ fluxes and spectral vegetation indices – VI (model 1); ii) linear regression between CO₂ fluxes and a product of VI and PAR (model 2), iii) partial least squares regression (PLSR) using simultaneously the full set of ASD-WhiteRef reflectance spectra (2151 bands, 350-2500 nm) to predict CO₂ fluxes. In addition, model i) and model iii) were tested also for predicting fapar and TotChl variability. The range of presented VIs contained both VIs derived from the Sentinel-2 bands simulation and VIs calculated using all two-band combinations of wavelengths available from the ASD-WhiteRef hyperspectral dataset.

The findings of the study highlight the potential of vegetation spectroscopy to monitor temporal variations in key drivers of photosynthesis process in grassland ecosystem and further upscaling of the observations.