Determining maize water stress through a remote sensing-based surface energy balance approach

Abstract

Determining water stress levels of vegetated surfaces is crucial for irrigation scheduling. This paper aims to evaluate a new method for obtaining crop water stress index (CWSI) based on the estimation of sensible heat flux using an aerodynamic temperature gradient approach. Data were collected on a deficit irrigated maize field at a research farm located in Greeley, Colorado, USA, in 2017 and 2018. The irrigation treatment used subsurface drip. Weather data were measured on-site at 3.3 m above ground level. RED and NIR surface reflectance data were obtained on-site through multispectral radiometer measurements. Nadir surface temperature data were measured using infra-red thermometers at 1 m above canopy. CWSI estimated values were used to assess daily soil water stress index (SWSI), calculated from measurements of volumetric soil water content (VWC) and management allowed depletion (MAD) of 40%. Results show that SWSI is best represented through a non-linear rational CWSI function. Modeled CWSI estimates were compared to measured surface heat fluxes, resulting in a mean bias error of − 0.02 and a root mean square error of 0.09, while errors were 0.02 and 0.06 when compared with observed CWSI based on canopy transpiration measured with plant sap flow devices. Results seem to validate the proposed sensible heat flux-based CWSI model. The CWSI approach presented could be used to manage irrigation and conserve water resources for maize in semi-arid regions.