Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic CO 2 uptake. Vegetation stress results in photosynthetic down- regulation, decreasing ecosystem carbon uptake. Present approaches to determine ecosystem carbon exchange rely on meteorological data as inputs to models that predict the relative photosynthetic function in response to environmental conditions inducing stress (e.g., drought, high/low temperatures). Also associated with photosynthetic down-regulation are changes in the apparent spectral reflectance of leaves. This study examines the determination of ecosystem photosynthetic light use efficiency (LUE) based on vegetation spectral reflectance changes associated with physiologic stress responses. Light use efficiency (LUE) models, driven by remotely sensed inputs, have been used to estimate productivity for a number of ecosystems. At it simplest, a LUE has the form: G =  f apar Q in =  APAR where: G is gross ecosystem production (GEP), the uptake of carbon through photosynthesis measured using eddy covariance techniques; Q in is the incoming photosynthetically active radiation (PAR), directly measured at the flux towers; f apar is the fraction of PAR absorbed by green vegetation; APAR is the PAR absorbed by vegetation or f apar Q in ;  is the light use efficiency. Leaves have multiple responses to stress that have specific effects on leaf spectral reflectance. These include: 1.Xanthophyll cycle pigments (531 nm) 2.Solar Induced Fluorescence (685, and 740 nm) 3.Altering the amount of photosynthetic pigments: chlorophyll a and b, anthocyanins and carotenoids (multiple wavelengths) 4.Leaf water content (970, 1240, 1630 nm) Methods Data were collected at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) fields (39.03°N, 76.85°W) at USDA Beltsville Agricultural Research Center. Agricultural Research Service researchers grew corn (Zea mays L., 'Pioneer 33A14') and collected CO 2 flux throughout the 2007 growing season. Throughout six days: 21 June (day of year 172), 2 July (183), 9 July (190), 31 July (212), 9 August (221), 14 August (226); hyperspectral reflectance measurements were made using an ASD FieldSpec along a transect in the corn field. The length and position of the transect were chosen to be representative of the tower footprint. The net CO 2 flux was partitioned into gross ecosystem production (GEP) and ecosystem respiration. Two half-hourly flux values were averaged for comparison with average reflectance from transect. LUE was calculated as the ratio of GEP and the PAR absorbed by the canopy (APAR). APAR is the product of incident PAR and f apar estimated from the Normalized Difference Vegetation Index (NDVI). Conclusions All of the indices were able to detect most of the variance in LUE within and between days. The fluorescence index did the best (r 2 =0.82), followed by PRI (r 2 =0.70) and the Gitelson carotenoid index (r 2 =0.66), with the NDII doing the poorest (r 2 =0.43), although relationship between leaf water from the NDII and LUE would be much higher if data from June 21 were removed. These indices required the use of narrow band and hyperspectral data, highlighting the importance of these types of data to detect physiological changes in vegetation. Further, this study shows that frequently measured reflectances can be used to determine fluxes at hourly time scales over a number of days. Reflectance data collected multiple times in a day provide a method of early detection of plant stress indicating the need to develop methods for measuring sites frequently. Results We examined the use of spectral reflectance indexes to detect LUE at hourly intervals and between days. We found a number of different approaches can be used to estimate LUE from reflectance. In the plots each colored line connects observations from the same day. The Photosynthetic Reflectance Index (PRI) detects changes in Xanthophyll cycle pigments from reflectance at 531 nm compared to a reference band at 570 nm (Figure 1). We used the ratio of the first derivatives of the reflectance spectra at 685 and 720 nm to detect effects of solar induced fluorescence on reflectance curve (Figure 2). To detect leaf water content the Normalized Difference Infrared Index was used, NDII is the normalized difference of reflectances at 858 and 1629 nm. We used Gitelson’s model for detecting concentrations of a leaf pigment ( a p ) in the presence of multiple other pigments using three different wavelength bands: [R( 1 ) -1 - R( 2 ) -1 ] x R( 3 )  a p For carotenoid concentrations the following wavelengths were used: 1 = 515 nm 2 = 565 nm 3 = 790 nm, (Figure 4). Transect Hyperspectral reflectance measurements using an ASD field spectrometer were collected at 101 points along a 300 foot transect at hourly intervals through each measurement day. Calibration Panel A second ASD measured a calibration panel when transect measurements were collected. These data were matched with transect measurements to calculate reflectance factors. Flux Tower CO 2 fluxes measured using eddy covariance techniques. The tower also measured metrological conditions and incident PAR. USING SPECTRAL REFLECTANCE TO DETERMINE LIGHT USE EFFICIENCY Karl F. Huemmrich a, Lawrence Corp b, Andrew Russ c, Elizabeth Middleton d, William Kustas c, John Prueger c, and Yen-Ben Cheng d a University of Maryland Baltimore County, Code 614.4, Goddard Space Flight Center, Greenbelt, MD 20771, USA, nasa.gov b SSAI, c United States Department of Agriculture – Agricultural Research Service, d National Aeronautics and Space Administration Figure 1 Figure 2 Figure 3 Figure 4 Aerial View of Study Area Conceptual diagrams showing how leaf optical properties can vary due to changes in a) xanthophyll pigments, b) fluorescence, and c) chlorophyll concentration. a c b