Detecting and monitoring the dynamics of the disease symptoms caused by Xylella fastidiosa at a large scale is essential to curtailing its expansion and mitigating its impacts
With this in mind, researchers developed a model integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with field observations and 3D radiative transfer modeling (3D-RTM).
Results demonstrated that Sentinel-2 data enabled detecting changes associated with temporal variations of Xf-induced symptoms at the orchard level. More specifically, among the Sentinel-2 Vegetation Indices (VIs) used, the Atmospherically Resistant Vegetation Index (ARVI) and the Optimized Soil-Adjusted Vegetation Index (OSAVI) showed higher-quality performance for the estimation of the disease severity and incidence. However, the olive crop’s discontinuous canopy confounded the VIs measured using the satellite data.
The 3D-RTM and field observations reduced this drawback, enabling the retrieval of biophysical vegetation parameters over time and explaining the temporal variations in both the tree canopy and the background. With the support of the 3-D RTM model, ARVI and OSAVI indices proved to be useful tools in monitoring orchard-level changes in the disease severity and incidence levels.
Overall, these results suggest that Sentinel-2 time-series can provide useful spatiotemporal indicators to monitor the damage caused by Xylella fastidiosa infections across large areas.
Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling
https://doi.org/10.1016/j.rse.2019.111480 | via Science Direct