Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

Abstract

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

Empirical vs. light-use efficiency modelling for estimating carbon fluxes in a mid-succession ecosystem developed on abandoned karst grassland

“…Among the five VI explored for estimating GPP and NEE in the present study, EVI was found to be the best VI proxy for both GPP and NEE. This seems adequate since the ecosystem addressed is not a pure grassland as it was the case in a previous study where NDVI was the best proxy for GPP [ 11 ]. The heterogeneous nature of the ecosystem in this study, made of a mix of tree patches and grassland, partly explains why EVI performed better than NDVI.…”

“…The mean annual temperature is 10.5˚C, and the average annual precipitation is about 1370 mm. These are statistics of 30 years (1971-2000) climate data from four different meteorological stations of the area [11,19]. The study area (Fig 1) covers a diverse ecosystem where both grass, shrubs and tree patches coexist.…”

“…In fact, R eco is one of the main sources of uncertainty in the estimation of NEE [16]. To overcome this limitation, some studies attempted to estimate NEE and GPP directly with simple empirical regression models especially in grasslands and croplands [10,11]. For evergreen systems where the seasonal decrease in photosynthetic activity can occur without substantial declines in canopy greenness reflected by VI, this approach is inadequate [17].…”

“…For the estimation of GPP, remote sensing can be used through the application of different models [ 7 , 8 ], such as VI-based empirical models [ 9 – 11 ] and light-use efficiency (LUE)-based models [ 5 , 12 – 14 ]. VI-based empirical models rely mostly on regression whereas LUE models are rooted in the LUE theory which states that GPP is related to the absorbed photosynthetically active radiation (APAR) and the LUE or photosynthetic efficiency by unit of photosynthetically active radiation (PAR).…”

Flame-Made La2O3-Based Nanocomposite CO2 Sensors as Perspective Part of GHG Monitoring System

“…Verification activities can be carried out on multiple spatial and temporal scales and include data from standard computational inventory methods, instrumental methods, remote sensing and modeling. All information must be processed by a hybrid data center system [20][21][22][23][24][25][26][27][28].…”