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

Javier Alvaro Quille-Mamani, German Huayna, Edwin Pino-Vargas (), Samuel Chucuya-Mamani, Bertha Vera-Barrios, Lia Ramos-Fernandez, Jorge Espinoza-Molina and Fredy Cabrera-Olivera
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Javier Alvaro Quille-Mamani: Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
German Huayna: Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
Edwin Pino-Vargas: Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
Samuel Chucuya-Mamani: Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
Bertha Vera-Barrios: Faculty of Mining Engineering, National University of Moquegua, Moquegua 180101, Peru
Lia Ramos-Fernandez: Departament of Water Resources, Universidad Nacional Agraria La Molina, Lima 15024, Peru
Jorge Espinoza-Molina: Department of Architecture, Jorge Basadre National University, Tacna 2300, Peru
Fredy Cabrera-Olivera: Department of Geological Engineering-Geotechnics, Jorge Basadre National University, Tacna 2300, Peru

Agriculture, 2024, vol. 14, issue 5, 1-17

Abstract: 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.

Keywords: temperature vegetation dryness index; normalized difference vegetation index; soil adjusted vegetation index; Enhanced Vegetation Index 2; Google Earth engine; semi-arid regions (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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