Remote sensing-based techniques for water management in small-scale farms in arid climate

Abstract

Accurate estimation of actual evapotranspiration (ETa) is a critical component in improving agricultural water management and water use efficiency. Remote sensing (RS) techniques provide a promising inexpensive tool for reliable crop water consumption estimations compared to conventional field measurements. Having agricultural land fragmentation and mixed cropping systems in the Nile River Delta, traditional methods of estimating ETa are seemingly challenging. The present study aims to improve agricultural water management at the meso scale using RS-based techniques. Four RS-based methods were employed to estimate ETa in mixed cropping farms at the Nile River Delta. The adopted methods include: (i) the Surface Energy Balance Algorithm for Land (SEBAL), (ii) the Simplified Surface Energy Balance algorithm (SSEB), (iii) Earth Engine Evapotranspiration Flux (EEFLUX) product, and (iv) the crop coefficient (Kc) method. The analysis of variance (ANOVA) test showed a significant difference between the employed RS-based techniques. During the winter season 2018–2019, the estimated ETa varied from 331.33 mm/season to 389.34 mm/season, with an average of 358.76 mm/season. The irrigation efficiency was estimated to be about 55–63%, with an average of 59.55%. The study developed an algorithm to schedule the operation hours of irrigation pumps in the study area based on actual water requirements and pump capacity. The study highlights the relevance of RS methods and the importance of the equitable distribution of water in small farms to enhance water management.