Abstract:Surface Energy Balance Algorithms for Land (SEBAL) and Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) are satellite-based image-processing models that calculate evapotranspiration (ET) as a residual of a surface energy balance. Both models are calibrated using inverse modelling at extreme conditions approach to develop imagespecific estimations of the sensible heat flux (H) component of the surface energy balance and to effectively remove systematic biases in net radiation, soil heat flux, radiometric temperature and aerodynamic estimates. SEBAL and METRIC express the near-surface temperature gradient as an indexed function of radiometric surface temperature, eliminating the need for absolutely accurate surface temperature and the need for air temperature measurements. Slope and aspect functions and temperature lapsing are used in METRIC applications in mountainous terrains. SEBAL and METRIC algorithms are designed for relatively routine application by trained professionals familiar with energy balance, aerodynamics and basic radiation physics. The primary inputs for the models are short-wave and long-wave (thermal) images from satellite (e.g. Landsat and MODIS), a digital elevation model and ground-based weather data measured within or near the area of interest. ET 'maps' (i.e. images) developed using Landsat images provide means to quantify ET on a field basis in terms of both rate and spatial distribution. METRIC takes advantage of calibration using weather-based reference ET so that both calibration and extrapolation of instantaneous ET to 24-h and longer periods compensate for regional advection effects where ET can exceed daily net radiation. SEBAL and METRIC have advantages over conventional methods of estimating ET using crop coefficient curves or vegetation indices in that specific crop or vegetation type does not need to be known and the energy balance can detect reduced ET caused by water shortage, salinity or frost as well as evaporation from bare soil.

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Successful uses of crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CROPGRO–Soybean model from typical information provided by crop performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site‐specific soil traits for use in crop models, and (iii) to explore the extent to which our approach allowed the crop model to reproduce observed genotype × environment (GE) interactions, cultivar ranking, and year‐to‐year yield variability. Crop performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For ‘Stonewall’, a maturity group VII cultivar, the RMSE of 769 kg ha−1 between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha−1 For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CROPGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype × environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.

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