Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru

Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDI NDVI and TVDI SAVI ), 24% severe drought (TVDI NDVI ) and 25% (TVDI SAVI ) of the total area, compared to TVDI EVI2 , which showed 37% drought and 16% severe drought. It is concluded that TVDI NDVI and TVDI SAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.