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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Publications at this Location » Publication #252263

Title: Parameterization of energy balance components and remote sensing in systems modeling

item Hatfield, Jerry

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 5/24/2010
Publication Date: 5/1/2011
Citation: Hatfield, J.L. 2011. Parameterization of energy balance components and remote sensing in systems modeling. In: Ahuja, L.R., Ma, L., editors. Advances in Agricultural Systems Modeling 2. American Society of Agronomy, Madison, WI. p. 261-281.

Interpretive Summary:

Technical Abstract: Simulation models require extensive inputs necessary to simulate a number of plant growth and energy exchange processes. These inputs are not always readily available at either the spatial or temporal resolution needed for effective use of model. Energy balance models are based on net radiation and its components. Net radiation is divided into solar radiation and longwave radiation and there are examples of methods to estimate solar radiation from temperature and humidity and these have been evaluated over limited data sets. In a similar method incoming longwave can be estimated from air temperature. These estimation methods allow for the use routine meteorological data to provide inputs for information that is spatially deficient. Use of these parameterization methods provides data for model inputs that would not be readily available. Crop simulation models generate various measures of plant growth and remote sensing methods provide a method of being able to either provide these inputs to models or to serve as potential feedbacks for model results. The use of remote sensing provides a way of deriving spatial and temporal observations that is not possible with other approaches. Improvement in parameterization of models would be enhanced by developing a formal mechanism to share different methods of generating input data along with the locations where the methods were evaluated. This would benefit both the experimental and modeling communities in improving our understanding of the dynamics of physical and biological systems.