Date of this Version
Published in Advances in Agricultural Systems Modeling (2011) Series 2: 261-281. DOI:10.2134/advagricsystmodel2.c9
Estimation of a number of parameters using simulation models has proven to be a valuable source of information from which we can assess the impact of scenarios that would be difficult to determine experimentally, or for which it would be difficult to conceptualize an appropriate experiment design. However, simulation models require extensive inputs that are not always easily found or exist at the spatial or temporal resolution needed for the models. Many simulation models require energy inputs that represent the energy balance of the surface, and there have been several attempts to derive different inputs. There have been various methods to estimate solar radiation from combinations of air temperature, altitude, and precipitation. Albedo has been estimated from several different methods using either combinations of reflectance or simple regression models. Long-wave radiation from the atmosphere has been estimated using regression models of vapor pressure and air temperature. Many of these parameterizations have been derived using locally available data, and efforts are needed for broader evaluation of these methods. Crop simulation models produce a variety of estimates for plant growth; among these are leaf area index, biomass, and ground cover. These parameters can be measured directly, often a laborious task and not at the scale needed for model evaluation, or they can be estimated from remotely sensed observations. This approach not only provides an independent measure of the crop parameters to compare with model simulations, but a potential feedback into the model simulation to help correct the model over time. Challenges remain in our efforts to improve models and provide the input necessary to further our ability to understand the complexities of the interactions in the soil–plant–atmosphere continuum.