Remote Sensing for Sustainable Pistachio Cultivation and Improved Quality Traits Evaluation through Thermal and Non-Thermal UAV Vegetation Indices

Abstract

Pistachio (Pistacia vera L.) has earned recognition as a significant crop due to its unique nutrient composition and its adaptability to the growing threat of climate change. Consequently, the utilization of remote sensing techniques for non-invasive pistachio monitoring has become critically important. This research was conducted in two pistachio orchards located in Spain, aiming to assess the effectiveness of vegetation indices (VIs) in estimating nut yield and quality under various irrigation conditions. To this end, high-resolution multispectral and thermal imagery were gathered using a Micasense ALTUM sensor carried by a DJI Inspire 2 drone in order to calculate the NDRE (normalized difference red edge index), GNDVI (green normalized difference vegetation index), NDVI (normalized difference vegetation index), and CWSI (crop water stress index). Each orchard underwent two flights at distinct growth stages, totaling four flights. In June, NDRE-carbohydrates (r = 0.78) and CWSI-oleic (r = 0.77) showed the highest correlations, while in September, CWSI-carbohydrates (r = 0.62) and NDVI-iron (r = 0.54) Despite NDVI’s limitations due to saturation effects, all VIs had significant yield and quality correlations, with GNDVI proving most effective in both flights. CWSI correlated considerably on both dates in terms of several quality parameters (carbohydrate percentage, magnesium, iron, and fatty acids, namely palmitoyl, stearic, oleic, and linoleic), surpassing non-thermal indices. Finally, it is important to consider the impact of environmental factors, such as the location of the sun, when interpreting the CWSI, as it modifies the temperature distribution pattern within the canopy. This study supports the viability of remote sensing and vegetation indices as potential tools for enhancing the management of pistachio orchards.