Estimating the GPP of olive trees with variable canopy cover by the use of Sentinel-2 MSI images

The estimation of gross primary production (GPP) is a key step towards the simulation of crop yield which can be performed by feeding the light use efficiency approach with remotely sensed estimates of absorbed photosynthetic active radiation (fAPAR). The application of this approach, however, is problematic for bi-layer ecosystems, such as olive groves, which have tree canopy cover (CC) variable in space and time due, for example, to pruning or leaf growing. The current paper investigates on this issue concerning a small olive grove in Florence (Central Italy), which, during the 2021 growing season, was monitored by standard meteorological and soil water content measurements; additionally, observations of fAPAR were taken in four plots differently affected by spring pruning. These ground observations allowed to calculate daily GPP references for the four plots, which were used to assess corresponding estimates obtained from interpolated weather data and Sentinel-2 MSI NDVI imagery. In particular, the fAPAR and GPP estimates were obtained both considering the NDVI values of the entire plots and separating the contribution of olive tree canopies from that of grasses. The results of the experiment indicated that the GPP of the most intensively pruned plots could be estimated correctly only by taking into consideration the temporally variable fAPAR contribution of olive tree canopies.