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TitleImpact of spectrally dependent gain errors in hyperspectral data on the determination of chlorophyll concentrations in vegetation
 
AuthorSoffer, R J; Neville, R A; Staenz, K; White, H PORCID logo
SourceIEEE International Geoscience and Remote Sensing Symposium proceedings 4423533, 2007 p. 3233-3236, https://doi.org/10.1109/IGARSS.2007.4423533
Year2007
Alt SeriesNatural Resources Canada, Contribution Series 20181534
PublisherIEEE
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
ProgramCanada Centre for Remote Sensing Divsion
AbstractIn support of Phase A work on the proposed Hyperspectral Environment and Resource Observer (HERO) mission, the sensitivity of chlorophyll concentrations derived from vegetation red-edge analysis to a spectrally dependent error in the radiometric calibration of the hyperspectral data is investigated. A typical ground-based reflectance spectrum taken from a boreal forest Aspen leaf is converted to top-of-atmosphere (TOA) radiance using the MODTRAN atmospheric correction model as implemented in the Imaging Spectrometer Data Analysis System (ISDAS). The resulting spectrum is then subjected to a randomly generated Spectral Gain Error (SGE). This process is repeated a statistically significant number of times to produce a simulated data set. By varying the magnitude of the SGE, several simulated data sets are produced representing different levels of relative calibration accuracies in the spectral domain. The simulated TOA data sets are then converted back to ground-based reflectance, once again using MODTRAN. For each pixel in the resulting data sets, red-edge parameters are determine using an Inverted Gaussian (IG) technique (red-edge inflection point - ?p, reflectance minimum - ?0, and ? = ?p - ?0) as well as a couple of commonly applied Vegetation Indices (R740/R720 and R 710/R760). Each of these parameters results in a distribution of results, the width of which is dependent on the magnitude of the SGE applied to the data set providing the relationship between the parameters and the SGE. In order to tie these results to chlorophyll concentration levels, the PROSPECT Vegetation Model is used to invert the original spectrum. The sensitivity of each of the indices to the chlorophyll concentration is determined by varying its value in the PROSPECT model while holding the other three model parameters constant. Using these relationships, the sensitivity of the chlorophyll concentrations to the SGE is determined. A specification of the acceptable error in the chlorophyll concentration levels would then dictate the required level of accuracy in the spectral gain calibration. The relationship between the SGE and the chlorophyll concentration retrieval based upon any of the five investigated red-edge parameters is shown to be directly proportional.
GEOSCAN ID311889

 
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