Mestrado MEDfOR - Mediterranean Forestry and Natural Resources Management - Instituto Superior de AgronomiaThe Eddy covariance method is a widespread method used for measuring carbon fluxes between the atmosphere and the ecosystem. It provides a high temporal resolution of measurements, but it is restricted to an area around the tower called footprint, and other methods are usually used in combination with eddy covariance data in order to estimate carbon fluxes for larger areas. Spectral vegetation indices derived from increasingly available satellite data can be combined with eddy covariance data to estimate carbon fluxes outside of the tower footprint. Following that approach, the present study attempted to model carbon fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between NEE or GPP and each vegetation index, (2) a linear relationship between GPP and the product of a vegetation index with PAR, and (3) a simplified LUE model assuming a constant LUE. We compared the performance of several vegetation indices from two sources (Landsat and SPOT-Vegetation) as predictors of NEE and GPP, based on three accuracy metrics (R², RMSE and AIC). Two types of aggregation of flux data were explored, midday average fluxes and daily average fluxes. The Vapor Pressure Deficit was used to separate the growing season in two phases, a greening phase 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 was the best predictor of GPP and NEE during the greening 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 type 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to illustrate the mapping of GPP and NEE for the study areaN/
