Corn Evapotranspiration Estimates Derived From a Remote Sensing Based Within Canopy Air Temperature Model.

Accurate estimates of spatially distributed corn evapotranspiration (ET) using remote sensing (RS) inputs are needed to improve corn water management and irrigation scheduling. An improved crop water management (and monitoring) is becoming more and more relevant with time because of the increasing competition and demand for water from municipal, industrial, recreational, and other water users. There are several remote sensing-based ET methods in the literature varying in complexity and input data (intensity) needs. However, a method is preferred if it is easier to apply and is reliable while yielding good estimates of vegetation (e.g., corn) water consumption. The RS ET method has to perform well for a wide range of vegetation water use rates (millimeters or inches per day) and irrigation strategies (full to limited irrigation).  In this study, corn ET was estimated using a RS-based within canopy air temperature model that utilizes airborne multispectral images (for creating ET maps) and independently using a ground-based radiometer (to estimate ET at discrete locations). The RS ET method also uses weather station data (i.e., air temperature, vapor pressure, and horizontal wind speed) and estimates of corn height and leaf area index. Results of the corn water use estimates were evaluated using an array of methods and instrumentation, including the soil water balance (with soil water content sensors), weighing lysimeters, and eddy covariance energy balance (flux) stations. The accuracy and limitations of the method are discussed.