Irrigation scheduling using hybrid remote sensing-based evapotranspiration model informed by unmanned aerial system acquired multispectral and thermal imagery

Abstract

Estimating daily crop evapotranspiration (ET) is a critical component in tracking soil water availability for near real-time irrigation management. Energy and water balance models are two common approaches for estimating daily crop ET. These models informed with remotely sensed imagery can estimate crop ET spatially aiding both uniform and spatial irrigation management. A hybrid remote sensing-based ET model consisting of the two-source energy balance and water balance models was used in scheduling variable rate and uniform irrigation in maize and soybean fields. Variable rate irrigation was scheduled using two approaches: 1) a hybrid model informed with calibrated high resolution unmanned aerial system multispectral reflectance and thermal infrared imagery and 2) a water balance model informed with satellite multispectral reflectance imagery. The variable rate irrigation approaches were compared to uniform and non-irrigated approaches to quantify the effects on dry grain yield and net irrigation applied when managing variable rate irrigation using the hybrid and water balance models. A separate field study was completed to assess how well the remote sensing-based ET model could schedule uniform irrigations of an entire field.