A comparison of operational remote sensing-based models for estimating crop evapotranspiration

Abstract

The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux ( H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET = 0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil + vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50 W m −2 for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30 W m −2) and highest r 2 values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6 mm d −1. The reflectance-based crop coefficient model yielded RMSD values of 0.4 mm d −1, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry.