IntroductionTo describe the dynamics of the global carbon cycle

requires an accurate determination of the spatial and temporal distribution of photosynthetic CO2 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 = fapar Qin = APAR

where: G is gross ecosystem production (GEP), the uptake of

carbon through photosynthesis measured using eddy covariance techniques;

Qin is the incoming photosynthetically active radiation (PAR), directly measured at the flux towers;

fapar is the fraction of PAR absorbed by green vegetation;APAR is the PAR absorbed by vegetation or fapar Qin; is the light use efficiency. Leaves have multiple responses to stress that have

specific effects on leaf spectral reflectance. These include:• Xanthophyll cycle pigments (531 nm)• Solar Induced Fluorescence (685, and 740 nm)• Altering the amount of photosynthetic pigments:

chlorophyll a and b, anthocyanins and carotenoids (multiple wavelengths)

• Leaf water content (970, 1240, 1630 nm)

MethodsData 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 CO2 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 CO2 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 fapar estimated from the Normalized Difference Vegetation Index (NDVI).

ConclusionsAll of the indices were able to detect most of the variance in LUE within and between days. The fluorescence index did the best (r2=0.82), followed by PRI (r2=0.70) and the Gitelson carotenoid index (r2=0.66), with the NDII doing the poorest (r2=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.

ResultsWe 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 (ap) in the presence of multiple other pigments using three different wavelength bands:

[R(1)-1- R(2)-1] x R(3) apFor carotenoid concentrations the following wavelengths were used:

1 = 515 nm2 = 565 nm3 = 790 nm, (Figure 4).

TransectHyperspectral 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 PanelA second ASD measured a calibration

panel when transect measurements were collected. These data were matched with

transect measurements to calculate reflectance factors.

Flux TowerCO2 fluxes measured using eddy covariance techniques. The tower also measured metrological conditions and incident PAR.

USING SPECTRAL REFLECTANCE TO DETERMINE LIGHT USE USING SPECTRAL REFLECTANCE TO DETERMINE LIGHT USE EFFICIENCYEFFICIENCYKarl F. Huemmricha, Lawrence Corpb, Andrew Russc, Elizabeth Middletond, William Kustasc, John Pruegerc, and Yen-Ben Chengd

aUniversity of Maryland Baltimore County, Code 614.4, Goddard Space Flight Center, Greenbelt, MD 20771, USA, karl.f.huemmrich @ nasa.govbSSAI, cUnited States Department of Agriculture – Agricultural Research Service, dNational 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 cb