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

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 the 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.

Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation

“…Data availability for crop model evaluation on aspects other than yields is still a strong limitation, especially at large‐scale applications. Remote sensing products may fill this gap to some extent (e.g., Cetin et al., 2023; Jiang et al., 2023; Jin et al., 2018; Yue et al., 2023).…”

Substantial differences in crop yield sensitivities between models call for functionality-based model evaluation

“…Data availability for crop model evaluation on aspects other than yields is still a strong limitation, especially at large-scale applications. Remote sensing products may fill this gap to some extent (e.g., Cetin et al, 2023;Jiang et al, 2023;Jin et al, 2018;Yue et al, 2023).…”

Comparison of actual evapotranspiration by the google earth engine evapotranspiration flux (EEFlux) to the METRIC model using remote sensing data and in-situ climate observations

“…ETa estimation by the EEFlux and METRIC model at a 30 m resolution scale can be useful for hydrological applications as well as agricultural water management and water balance calculations at large-scale irrigation catchments. However, as reported by many researchers [2,4,5,7], research results need some sort of justification and/or validation. The novelty of this study is to compare the ETa by the EEFlux product with the ETa-METRIC model for checking the accuracy of the EEFlux product over AID during the 2020 water year.…”

“…The typical character of all direct methods is to compute ETa at a point scale such as the soil water budget approach, lysimeters, eddy covariance, and scintillometer. Therefore, these techniques generally fail to show the ETa variations in the field [1] if compared to the remote sensing (RS)-based surface energy balance models [2][3][4]. Among RS-based surface energy balance models is the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model [5][6], which is one of the more widely used to estimate ETa in operational irrigation practices at a large scale.…”

“…For this reason, automated calibration algorithms for the METRIC model were designed to reduce the errors that might happen to the users. For example, the R-METRIC was applied based on the METRIC model over a large irrigation catchment, i.e., Akarsu Irrigation District (AID) in Turkiye [2,4], which automatized hot and cold pixel selection that decreased the possibility of human error [11].…”