Modeling energy balance fluxes and evapotranspiration of maize and soybean using multispectral and thermal infrared imagery acquired with an unmanned aerial system

Abstract

Estimating actual crop evapotranspiration (ET) is a critical component in tracking crop water availability and managing irrigation. Various methods presently exist for modeling crop ET utilizing remotely sensed data and imagery as inputs. While more traditional remote sensing platforms like satellite and manned aircraft provide useful information, limitations exist due to lack of spatiotemporal resolution and high cost. Unmanned aerial systems (UAS) now offer greater opportunities for collecting remotely sensed data with enhanced spatiotemporal resolution that may lead to increased accuracy in estimating crop (ET), making near real-time irrigation management more feasible. The two-source energy balance (TSEB) model is one of the methods of estimating crop ET from surface energy balance fluxes. In this research, multispectral and thermal infrared imagery collected with a UAS over maize and soybean fields throughout the growing season at different vegetative stages were used in the TSEB model to estimate spatially distributed surface energy balance fluxes and daily crop ET. The estimated spatially distributed surface fluxes and ET were weighted and aggregated using a two-dimensional flux footprint and validated against measured fluxes from Eddy Covariance systems located within the fields.