GRASS COVER, TREE DENSITY, AND LEAF DEVELOPMENT OF MEDITERRANEAN ORCHARDS FROM HIGH RESOLUTION DATA

Abstract

Abstract. The study focused on Mediterranean orchards and aimed to explore different remote sensing data (Sentinel 2 data (2016–2023), 1 Pleiades image (2022) and the extraction of Google-satellite-hybrid images (GSH,2017)) to compute key variables affecting water requirements such as tree age and density per plot, leaf development, the inter-row management. Surveys were conducted on 22 farms where accurate information on agricultural practices was collected. The results have shown that a thresholding on the NDVI Sentinel 2 in the summer period allowed the identification of young orchards with an accuracy of 98%. The analysis of temporal profiles of FAPAR allowed the identification of key phenological stages such as flowering and fruit set. Supervised classification was employed to separate grassed and non-grassed plots using three spectral bands of Sentinel 2. Classifications performed from GSH images gave more accurate results (81% well classified) compared with Sentinel 2 (79%) and Pleiades (57%) when identifying grassed plots. The methods presented in this study propose methods easily accessible based on free-to-download data, making them applicable in diverse orchard contexts.