Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where increasing impact of climate change coupled with inefficient management olive groves negatively affecting oil production and quality some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing grove final production. However, spatial resolution freely-available products not yet adequate estimate plant biophysical parameters complex agroecosystems such as groves, both trees grass cover contribute vegetation indices (VIs) signal at pixel scale. The aim study therefore test disentangling procedure partition VIs among components agroecosystem use monitoring processes during season. Specifically, five (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) recorded by Sentinel-2 10 m over Montalbano area (Tuscany, Central Italy), were tested proxy intercepted radiation. volume per was initially used linearly rescale into relevant value trees. models, describing relationship between rescaled observed fraction Photosynthetically Active Radiation (fPAR), fitted then validated against independent datasets. While calibration phase, greater robustness predicting fPAR obtained using NDVI (r = 0.96 RRMSE 9.86), validation results demonstrating that GEMI MCARI2/OSAVI provided highest performances (GEMI: r 0.89 21.71; MCARI2/OSAVI: 0.87 25.50), contrast MCARI2 lowest 0.67 36.78). These be related VIs’ intrinsic features (e.g., lower sensitivity atmosphere background effects), which make these indices, compared others, less sensitive saturation effects improving estimation vs. NDVI). On basis, evidenced need improve current methodologies reduce inter-row select appropriate estimation, especially affect remote sensed-derived an inadequate resolution.