Accurate assessment of vegetation canopy optical properties plays a critical role in monitoring natural and managed
ecosystems under environmental changes. In this context, radiative transfer (RT) models simulating vegetation canopy
reflectance have been demonstrated to be a powerful tool for understanding and estimating spectral bio-indicators. In this
study, two narrow band spectroradiometers were utilized to acquire observations over corn canopies for two summers.
These in situ spectral data were then used to validate a two-layer Markov chain-based canopy reflectance model for
simulating the Photochemical Reflectance Index (PRI), which has been widely used in recent vegetation photosynthetic
light use efficiency (LUE) studies. The in situ PRI derived from narrow band hyperspectral reflectance exhibited clear
responses to: 1) viewing geometry which affects the light environment; and 2) seasonal variation corresponding to the
growth stage. The RT model (ACRM) successfully simulated the responses to the viewing geometry. The best
simulations were obtained when the model was set to run in the two layer mode using the sunlit leaves as the upper layer
and shaded leaves as the lower layer. Simulated PRI values yielded much better correlations to in situ observations when
the cornfield was dominated by green foliage during the early growth, vegetative and reproductive stages (r = 0.78 to
0.86) than in the later senescent stage (r = 0.65). Further sensitivity analyses were conducted to show the important
influences of leaf area index (LAI) and the sunlit/shaded ratio on PRI observations.
Climate change is heavily impacted by changing vegetation cover and productivity with large scale monitoring of vegetation only possible with remote sensing techniques. The goal of this effort was to evaluate existing reflectance (R) spectroscopic methods for determining vegetation parameters related to photosynthetic function and carbon (C) dynamics in plants. Since nitrogen (N) is a key constituent of photosynthetic pigments and C fixing enzymes, biological C sequestration is regulated in part by N availability. Spectral R information was obtained from field corn grown at four N application rates (0, 70, 140, 280 kg N/ha). A hierarchy of spectral observations were obtained: leaf and canopy with a spectral radiometer; aircraft with the AISA sensor; and satellite with EO-1 Hyperion. A number of spectral R indices were calculated from these hyperspectral observations and compared to geo-located biophysical measures of plant growth and physiological condition. Top performing indices included the R derivative index D730/D705 and the normalized difference of R750 vs. R705 (ND705), both of which differentiated three of the four N fertilization rates at multiple observation levels and yielded high correlations to these carbon parameters: light use efficiency (LUE); C:N ratio; and crop grain yield. These results advocate the use of hyperspectral sensors for remotely monitoring carbon cycle dynamics in managed terrestrial ecosystems.
Patterns of change in vegetation growth and condition are one of the primary indicators of the present and future
ecological status of the globe. Nitrogen (N) is involved in photochemical processes and is one of the primary resources
regulating plant growth. As a result, biological carbon (C) sequestration is driven by N availability. Large scale
monitoring of photosynthetic processes are currently possible only with remote sensing systems that rely heavily on
passive reflectance (R) information. Unlike R, fluorescence (F) emitted from chlorophyll is directly related to
photochemical reactions and has been extensively used for the elucidation of the photosynthetic pathways. Recent
advances in passive fluorescence instrumentation have made the remote acquisition of solar-induced fluorescence
possible. The goal of this effort is to evaluate existing reflectance and emerging fluorescence methodologies for
determining vegetation parameters related to photosynthetic function and carbon sequestration dynamics in plants. Field
corn N treatment levels of 280, 140, 70, and 0 kg N / ha were sampled from an intensive test site for a multi-disciplinary
project, Optimizing Production Inputs for Economic and Environmental Enhancement (OPE). Aircraft, near-ground,
and leaf-level measurements were used to compare and contrast treatment effects within this experiment site assessed
with both reflectance and fluorescence approaches. A number of spectral indices including the R derivative index
D730/D705, the normalized difference of R750 vs. R705, and simple ratio R800/R750 differentiated three of the four N
fertilization rates and yielded high correlations to three important carbon parameters: C:N, light use efficiency, and grain
yield. These results advocate the application of hyperspectral sensors for remotely monitoring carbon cycle dynamics in
terrestrial ecosystems.
This manuscript details the development and validation of a unique forward thinking instrument and methodology for
monitoring terrestrial carbon dynamics through synthesis of existing hyperspectal sensing and Light Detection and
Ranging (LIDAR) technologies. This technology demonstration is directly applicable to linking target mission concepts
identified as scientific priorities in the National Research Council (NRC, 2007) Earth Science Decadal Survey; namely,
DESDynI and HyspIRI. The primary components of the Hyperspec-LIDAR system are the ruggedized imaging
spectrometer and a small footprint LIDAR system. The system is mounted on a heavy duty motorized pan-tilt unit
programmed to support both push-broom style hyperspectral imaging and 3-D canopy LIDAR structural profiling. The
integrated Hyperspec-LIDAR sensor system yields a hypserspectral data cube with up to 800 bands covering the spectral
range of 400 to 1000 nm and a 3-D scanning LIDAR system accurately measuring the vertical distribution of intercepted
surfaces within a range of 150 m with an accuracy of 15 mm. Preliminary field tests of the Hyperspec-LIDAR sensor
system were conducted at a mature deciduous mixed forest tower site located at the Smithsonian Environmental
Research Center in Edgewater, MD. The goal of this research is to produce integrated science and data products from
ground observations that will support satellite-based hybrid spectral/structural profile linked through appropriate models
to monitor Net Ecosystem Exchange and related parameters such as ecosystem Light Use Efficiency.
Understanding the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic CO2 uptake by terrestrial vegetation. Stress factors may cause sub-optimal photosynthetic function resulting in down-regulation (i.e., reduced rate of photosynthesis). Photosynthetic down-regulation is related to changes in the apparent spectral reflectance of leaves. 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). This study examines the determination of ecosystem photosynthetic light use efficiency (LUE) from satellite observations, through measurement of vegetation spectral reflectance changes associated with physiologic stress responses. This approach is possible using the Moderate-Resolution Spectroradiometer (MODIS) on Terra to provide frequent, narrow-band measurements of high radiometric accuracy. Data from reflective MODIS ocean bands were used over land to calculate the Photochemical Reflectance Index (PRI), an index that is sensitive to reflectance changes near 531nm associated with vegetation stress responses exhibited by photosynthetic pigments. MODIS PRI values were compared with LUE calculated from values of CO2 flux measured at the overpass time at a flux tower located in a Douglas fir forest on Vancouver Island in Canada. Preliminary results show a relationship between MODIS PRI and LUE when using MODIS observations in the backscattering direction. These results compare well to previous work at a boreal aspen forest suggesting this approach may be generally useful.
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