Estimation of crop water stress index and leaf area index based on remote sensing data

Abstract

Abstract Estimation of crop water stress index (CWSI) and leaf area index (LAI) over large-irrigation schemes requires the use of cutting-edge technologies. Combinations of remote sensing techniques with ground-truth data have become available for use at the catchment level. These approaches allow us to estimate actual evapotranspiration and have the capability of monitoring crop water status and saving irrigation water in water-scarce regions. This study was conducted in the eastern Mediterranean Region of Turkiye. Fully distributed CWSI maps were generated and we assessed the relationship between CWSI and LAI for some specific crops in the winter season of 2021. Landsat 7 and 8 data were used and meteorological data were acquired from two stations in the study area. ‘Mapping Evapotranspiration at high Resolution with Internalized Calibration’ methodology was applied to estimate the energy balance components. CWSI maps displayed spatiotemporal changes in tandem with crop-type variations. Consequently, results presented a high correlation (r = 0.95 and r = 0.99 for wheat and lettuce, respectively) between CWSI and LAI, a moderate correlation (r = 0.44) for potatoes in the winter season. Thus, by utilizing remotely sensed data, the CWSI values would be directly estimated without requiring any in situ measurements of the canopy and air temperature over-irrigation scheme.