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Title: Simplified expressions for radiation scattering through canopies with ellipsoidal leaf angle distributions

Authors
item Flerchinger, Gerald
item Qiang, Yu - CHINESE ACAD SCI

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 10, 2007
Publication Date: June 7, 2007
Citation: Flerchinger, G.N., Q. Yu. 2007. Simplified Expressions for Radiation Scattering in Canopies with Ellipsoidal Leaf Angle Distributions. Agricultural and Forest Meteorology. 144:230-235.

Interpretive Summary: The exchange of solar radiation process within plant canopies is critical to the plant’s thermal environment, photosynthesis, and growth. Understanding radiation transfer within the plant canopy enhances our ability to predict plant response and to evaluate management and climate scenarios. However, mathematical expressions describing radiation transfer through plant canopies are extremely complex. Simplified equations to compute radiation transfer processes through plant canopies were developed that are very close approximations to the extremely complex expressions. These simplified equations can be more easily implemented into computer simulation models and will lead to more reliable models for evaluation of management and climate scenario influences on plant microclimate and plant response.

Technical Abstract: The ability to simulate the surface energy balance and microclimate within a plant canopy is contingent upon accurate simulation of radiation exchange within the canopy. Accurate radiation simulations require some assumption of leaf angle distribution to compute transmissivity, reflection and scattering of radiation. The ellipsoidal leaf angle density function can very closely approximate real plant canopies but requires complex integrations for every combination of leaf area index, incident radiation, and density function. This paper presents very close approximations to compute the transmissivity and backscattering functions for elliptical leaf angle distributions that can be more easily implemented into simulation models.

   
 
 
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