Monitoring of carbon dioxide fluxes in a subalpine grassland ecosystem of the Italian Alps using a multispectral sensor
The study investigates the potential of a commercially available proximal sensing system – based on a 16-band multispectral sensor – for monitoring mean midday gross ecosystem production (GEP m) in a subalpine grassland of the Italian Alps equipped with an eddy covariance flux tower. Reflectance observations were collected for 5 consecutive years, characterized by different climatic conditions, together with turbulent carbon dioxide fluxes and their meteorological drivers. Different models based on linear regression (vegetation indices approach) and on multiple regression (reflectance approach) were tested to estimateGEP m from optical data. The overall performance of this relatively low-cost system was positive. Chlorophyll-related indices including the red-edge part of the spectrum in their formulation (red-edge normalized difference vegetation index, NDVI red-edge; chlorophyll index, CI red-edge) were the best predictors of GEP m, explaining most of its variability during the observation period. The use of the reflectance approach did not lead to considerably improved results in estimating GEP m: the adjusted R2 (adj R2) of the model based on linear regression – including all the 5 years – was 0.74, while the adj R2 for the multiple regression model was 0.79. Incorporating mean midday photosynthetically active radiation (PAR m) into the model resulted in a general decrease in the accuracy of estimates, highlighting the complexity of the GEP m response to incident radiation. In fact, significantly higher photosynthesis rates were observed under diffuse as regards direct radiation conditions. The models which were observed to perform best were then used to test the potential of optical data for GEP m gap filling. Artificial gaps of three different lengths (1, 3 and 5 observation days) were introduced in the GEP m time series. The values of adj R2 for the three gap-filling scenarios showed that the accuracy of the gap filling slightly decreased with gap length. However, on average, the GEP m gaps were filled with an accuracy of 73% with the model fed with NDVI red-edge, and of 76% with the model using reflectance at 681, 720 and 781 nm and PAR m data.