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Es ma on of a number of parameters using simula on models has proven to be a valuable source of informa on from which we can assess the impact of scenarios that would be diffi cult to determine experimentally, or for which it would be diffi cult to conceptualize an appropriate experiment design. However, simula on models require extensive inputs that are not always easily found or exist at the spa al or temporal resolu on needed for the models. Many simula on models require energy inputs that represent the energy balance of the surface, and there have been several a empts to derive diff erent inputs. There have been various methods to es mate solar radia on from combina ons of air temperature, al tude, and precipita on. Albedo has been es mated from several diff erent methods using either combina ons of refl ectance or simple regression models. Long-wave radia on from the atmosphere has been es mated using regression models of vapor pressure and air temperature. Many of these parameteriza ons have been derived using locally available data, and eff orts are needed for broader evalua on of these methods. Crop simula on models produce a variety of es mates for plant growth; among these are leaf area index, biomass, and ground cover. These parameters can be measured directly, o en a laborious task and not at the scale needed for model evalua on, or they can be es mated from remotely sensed observa ons. This approach not only provides an independent measure of the crop parameters to compare with model simula ons, but a poten al feedback into the model simulaon to help correct the model over me. Challenges remain in our eff orts to improve models and provide the input necessary to further our ability to understand the complexi es of the interac ons in the soil–plant–atmosphere con nuum. J.L. Ha ield, USDA-ARS, Na onal Laboratory for Agriculture and the Environment, 2110 University Blvd., Ames, IA 50011 (jerry.ha ield@ars.usda.gov). doi:10.2134/advagricsystmodel2.c9

Parameterization of Energy Balance Components and Remote Sensing in Systems Modeling