Abstract
Abstract. 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 (GEPm) 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 estimateGEPm 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, NDVIred-edge; chlorophyll index, CIred-edge) were the best predictors of GEPm, explaining most of its variability during the observation period. The use of the reflectance approach did not lead to considerably improved results in estimating GEPm: the adjusted R2 (adjR2) of the model based on linear regression – including all the 5 years – was 0.74, while the adjR2 for the multiple regression model was 0.79. Incorporating mean midday photosynthetically active radiation (PARm) into the model resulted in a general decrease in the accuracy of estimates, highlighting the complexity of the GEPm 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 GEPm gap filling. Artificial gaps of three different lengths (1, 3 and 5 observation days) were introduced in the GEPm time series. The values of adjR2 for the three gap-filling scenarios showed that the accuracy of the gap filling slightly decreased with gap length. However, on average, the GEPm gaps were filled with an accuracy of 73% with the model fed with NDVIred-edge, and of 76% with the model using reflectance at 681, 720 and 781 nm and PARm data.
Highlights
Considering all of the observation years, the most robust estimates of GEPm were obtained when NDVIred-edge and CIred-edge were used to parameterize the model (Table 4). These results confirmed the findings of previous studies on both similar (Rossini et al, 2012) and different ecosystems (Gitelson et al, 2003b; Peng and Gitelson, 2012; Peng et al, 2011; Rossini et al, 2010), indicating that vegetation indices (VIs) based on the red-edge part of the spectrum are the most sensitive to the seasonal gross ecosystem production (GEP) dynamics due to their better linearity with chlorophyll content (Gitelson et al, 2003a; Sims and Gamon, 2002; Wu et al, 2008), and with green leaf area index – green LAI (Gitelson et al, 2003c; Viña et al, 2011)
The use of the reflectance approach instead of the VI approach did not lead to considerably improved results in estimating GEPm
This study investigated the potential of a commercially available system – based on a 16-band multispectral sensor – for monitoring mean midday gross ecosystem production (GEPm) in a dynamic subalpine grassland ecosystem of the Italian Alps
Summary
The eddy covariance (EC) technique is a widely and commonly applied method to estimate carbon dioxide exchange between vegetation and the atmosphere at the ecosystem scale (Baldocchi, 2003; Burba, 2013; Geider et al, 2001). This method is able to provide direct, near-continuous and high-temporal resolution measurements of net gas exchange, it has some limitations. Considering all of these aspects, it is clear that, EC measurements can be considered a solid basis for the ecosystem-scale CO2 flux measurements, complementary methods are needed to extend the estimates to landscape and regional scales
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