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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #294624
Title: Improved mapping of carbon, water, and energy land-surface fluxes using hyperspectral data

Author
item Schull, Mitchell
item Anderson, Martha
item Kustas, William - Bill
item HOUBORG, RASMUS - European Commission-Joint Research Centre (JRC)

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/15/2013
Publication Date: 5/29/2013
Citation: Schull, M.A., Anderson, M.C., Kustas, W.P., Houborg, R. 2013. Improved mapping of carbon, water, and energy land-surface fluxes using hyperspectral data[abstract]. 2013 Hyperspectral Infrared Imager Symposium, NASA/GSFC, May 29-30, 2013, Greenbelt, MD.

Interpretive Summary:

Technical Abstract: Recent studies have shown that chlorophyll (Cab) can be useful for constraining light-use-efficiency (LUE). LUE defines how efficiently a plant can assimilate carbon dioxide (CO2) given an amount of absorbed Photosynthetically Active Radiation (PAR) and is therefore useful for monitoring carbon fluxes. Since Cab is a vital pigment for absorbing visible light for use in photosynthesis, it has been recognized as a key parameter for quantifying photosynthetic functioning. A LUE-based model of canopy resistance has been embedded into the Two-Source Energy Balance (TSEB-LUE) 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. We investigate the feasibility of leaf chlorophyll to capture these variations in LUEn using field measured Cab over agricultural fields. Initial results show that field measured Cab is non-linearily related to LUEn. The relationship allows for a semi-empirical modification to the existing TSEB-LUE model to account for conditions of stress. Improvements in carbon flux measurements are immediately evident while modifications to latent fluxes result in a better partitioning of transpiration and soil evaporation. The TSEB-LUE model will take advantage of hyperspectral data for use in better estimation of Cab as well as ingest the high temporal thermal data provided by HyspIRI. The observed Cab-LUE relationship allows for integration of the remotely sensed Cab into the TSEB-LUE model, facilitating improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.