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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: Mapping carbon, water, and energy land-surface fluxes using remotely indicators of canopy light use efficiency from hyperspectral data

item Houborg, Rasmus -

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: May 10, 2012
Publication Date: May 16, 2012
Citation: Schull, M.A., Anderson, M.C., Cammalleri, C.N., Houborg, R., Kustas, W.P. 2012. Mapping carbon, water, and energy land-surface fluxes using remotely indicators of canopy light use efficiency from hyperspectral data [abstract]. 2012 Hyperspectral Infrared Imager Symposium. 2012 CDROM.

Technical Abstract: Remotely sensed data allow 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. Recent studies have shown that it can be useful for constraining light-use-efficiency (LUE), which defines how efficiently a plant can assimilate carbon dioxide (CO2) given the absorbed Photosynthetically Active Radiation (PAR) and is therefore useful for monitoring carbon fluxes. A LUE-based model of canopy resistance has been embedded into a thermal-based Two-Source Energy Balance (TSEB) model to facilitate coupled simulations of transpiration and carbon assimilation. The model assumes that deviations of the observed canopy LUE from a nominal stand-level value (LUEn – typically indexed by vegetation class) are due to varying conditions of light, humidity, CO2 concentration and leaf temperature. The deviations are accommodated by adjusting an effective LUE that responds to the varying conditions. We investigate the feasibility of leaf chlorophyll to capture these variations in LUEn using remotely sensed data. To retrieve Cab from remotely sensed data we use REGFLEC, a physically based tool that translates at-sensor radiances in the green, red and NIR spectral regions from multiple satellite sensors into realistic maps of LAI and Cab. Initial results show that Cab is exponentially correlated to light use efficiency and introducing a 3-day lag between Cab retrievals with REGFLEC and LUEn used in TSEB yields even higher correlations. The improved results may indicate that the stresses induced by the environment may not result in an immediate response in leaf chlorophyll concentrations. The observed relationship provides an avenue for integrating hyperspectral data from HyspIRI for use in the REGFLEC and TSEB tools, facilitating improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.

Last Modified: 8/27/2014
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