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
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.
Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems.
Invasive plant species are causing severe environmental and ecological impacts. This study utilized airborne hyperspectral image data and digital image processing techniques to map one of the most aggressive weeds, kudzu (Pueraria montana), in western Georgia. Minimum Noise Fraction (MNF) transform followed by Spectral Angle Mapper (SAM) produced the best map results among several other procedures. Validation with field data show that this procedure delivered user's accuracy of 83.02% for kudzu-invaded plots and 95.90% for non-invaded plots, with Producer's accuracy of 73.26% and 82.47%, respectively. Further analysis using a GIS-based CART analysis indicates the importance of elevation in limiting the spatial distribution of kudzu.
We assessed the capability of AVIRIS and MODIS to estimate canopy water content. Hyperspectral water retrievals with AVIRIS data, EWT, were compared to in situ leaf water content and LAI measurements at a semi-arid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis also suggested that EWT was significant among seven different vegetation communities. Four MODIS indexes derived from band ratios using the reflectance product and were compared to retrievals of EWT with AVIRIS at both the semi-arid site and a temperate conifer forest. Good statistical agreements were found between AVIRIS EWT and all four MODIS indexes at the semi-arid site in savanna shrub communities. Slightly poorer correlations were found at the forest site where water indexes had better correlation to AVIRIS EWT than vegetation indexes. Temporal patterns of the four indexes in all semi-arid vegetation communities except creosote bush and agriculture show distinct seasonal variation and responded to precipitation at the savanna site. Three years of net ecosystem exchange (NEE) data from eddy covariance measurements at the forest site were compared to the time series of MODIS indexes. MODIS water indexes showed similar seasonal patterns to NEE that were strongest during the period of net carbon sequestration. In contrast, the time series of MODIS vegetation indexes did not yield a good relationship to NEE.
In water limited environments, the density and water content of plant canopies are highly correlated to available soil moisture. Specific absorption bands for liquid water are identifiable and the variation in their depths can be related to canopy water content using high spectral resolution (hyperspectral) imagery. The spectral absorption feature centered at approximately 980 nm has been widely utilized for estimating equivalent water thickness, a measure of the volume of canopy water if it is equally distributed over the area of the pixel. Although it is affected by canopy structure, it is highly correlated with plant water content, and is independent of reflectance changes due to photosynthetic pigments. This study relates the depth of the 980 nm water band absorption, measured by the continuum removal (CR) technique, to crop water stress, and compares these results to other vegetation and plant stress indicators, NDVI and NDWI.