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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: On the relationship between nominal light use efficiency and leaf chlorophyll

Authors
item Schull, Mitchell
item Anderson, Martha
item Cammalleri, Carmelo
item Houborg, Rasmus -
item Kustas, William

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: May 4, 2012
Publication Date: July 22, 2012
Citation: Schull, M.A., Anderson, M.C., Cammalleri, C.N., Houborg, R., Kustas, W.P. 2012. On the relationship between nominal light use efficiency and leaf chlorophyll[abstract]. International Geoscience and Remote Sensing Symposium 2012. 2012 CDROM.

Technical Abstract: Remotely sensed data allows for indirect estimates of key biophysical and biochemical parameters needed for accurate and reliable assessments of land-surface carbon, energy and water fluxes. Biophysical parameters such as Leaf Area Index (LAI), which provides information useful for determining variations in land-surface fluxes, and leaf chlorophyll content (Cab), an indicator of the overall plant physiological condition, can be used to constrain land-surface models. Since chlorophyll (Cab) is a vital pigment for absorbing light for use in photosynthesis it has been recognized as a key parameter for quantifying photosynthetic functioning and recent studies have shown that it can be used for constraining light-use-efficiency (LUE) and therefore carbon fluxes. LUE defines how efficiently a plant can assimilate carbon dioxide (CO2) given the absorbed Photosynthetically Active Radiation (PAR). A LUE-based model of canopy resistance has been embedded into the Two-Source Energy Balance (TSEB) model to facilitate coupled simulations of transpiration and carbon assimilation. In the TSEB-LUE model, deviations of the canopy LUE from a nominal stand-level value (LUEn – typically indexed by vegetation class) are accommodated by diagnosing an effective LUE that responds to varying conditions of light, humidity, CO2 concentration and leaf temperature. We investigate the feasibility of leaf chlorophyll to capture the variations in LUEn due to changes in vegetation type, crop physiology conditions and phenology. In this research we analyze the correlation between Cab, retrieved from remote sensing using a physically-based approach, and TSEB-LUE model optimized LUEn values across multiple flux tower sites across the U.S. to investigate how differences in land cover type and environmental conditions may affect this relationship. Our initial findings indicate a strong relationship between chlorophyll and LUE however further analysis showed that if we introduce a 3-day lag the results improved. Results are shown for research conducted over a corn field in Maryland.

Last Modified: 12/22/2014
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