Estimating and mapping evapotranspiration in olive groves of semi-arid Tunisia using empirical formulas and satellite remote sensing

Abstract

Accurate estimation of evapotranspiration (ET) is needed to optimize irrigation in semi-arid environments. The present work aims to estimate evapotranspiration using empirical formulas and remote sensing in a commercial olive orchard near Menzel Mhiri in east central Tunisia. This study compares evapotranspiration with the Mapping Evapotranspiration at high Resolution with Internal Calibration (METRIC) model with estimates from five empirical equations (Penman-Monteith; Blaney-Criddle; Hargreaves temperature; Hargreaves radiation; and Priestley-Taylor). Reference evapotranspiration estimated using these five empirical equations was based on meteorological station measurements in olives at the pilot study site. Three years of ET estimates based on Landsat-8 satellite images and the METRIC method covering 2017, 2018, and 2019 were downloaded using Google EEFlux. The results obtained by remote sensing showed good ability to characterize seasonal variation in reference (ET 0 ) and actual crop (ETc) evapotranspiration. Reference evapotranspiration values for olive trees estimated by the METRIC model during the three experimental years accurately represent those estimated with the Penman-Monteith formula. Also, there was very good agreement between results obtained with METRIC and those calculated using the Blaney-Criddle method. The monthly average of ET 0 and ETc values determined during the three experimental years (2017, 2018, and 2019) showed that the METRIC model underestimates irrigation needs by about 15% compared to the Penman-Monteith (PM) approach.

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

“…Despite a slight overestimation of ET, the study emphasizes the model's potential, although with considerations for specific orchard settings. In a comparative analysis of empirical ET calculation methods, Bchir et al [23] validated METRIC estimates against five different methods, demonstrating strong correlations (R² values: 0.85 to 0.99). Minor underestimations were verified when incorporating Landasat-8 satellite imagery, supporting the reliability of these methods for olive orchards.…”

“…Providing non-destructive methodologies with high spatial, radiometric and temporal resolutions, RS allows the characterization and monitoring of spatiotemporal variability for multidimensional purposes [9][10][11][12][13]. Over the past few decades, RS technology has played a crucial role in the development of new agricultural applications, focusing primarily on monitoring vegetation cover [14][15][16], assessing crop vigor conditions [17][18][19], estimating nutrient and water status [20][21][22], determining crop evapotranspiration (ET c ) [23][24][25], identifying and managing invasive plants [26][27][28], detecting and monitoring pest/diseases [29][30][31] and forecasting crop yields [32][33][34]. The effectiveness of RS applications in agriculture depends on several fundamental factors, including the choice of sensing platform, which can be a satellite, aircraft, unmanned aerial vehicle (UAV) or terrestrial platform; the segment of the electromagnetic spectrum used; the number and range of spectral bands; spatial, temporal and radiometric resolutions; and the energy source (passive or active sensors) [9].…”