Remote spectral sensing offers an attractive means of mapping river water quality over wide spatial regions. While previous research has focused on development of spectral indices and models to predict river water quality based on remote images, little attention has been paid to subsequent validation of these predictions. To address this oversight, we describe a retrospective analysis of remote, multispectral Compact Airborne Spectrographic Imager (CASI) images of the Ohio River and its Licking River and Little Miami River tributaries. In conjunction with the CASI acquisitions, ground truth measurements of chlorophyll-a concentration and turbidity were made for a small set of locations in the Ohio River. Partial least squares regression models relating the remote river images to ground truth measurements of chlorophyll-a concentration and turbidity for the Ohio River were developed. Employing these multivariate models, chlorophyll-a concentrations and turbidity levels were predicted in river pixels lacking ground truth measurements, generating detailed estimated water quality maps. An important but often neglected step in the regression process is to validate prediction results using a spectral residual statistic. For both the chlorophyll-a and turbidity regression models, a spectral residual value was calculated for each river pixel and compared to the associated statistical confidence limit for the model. These spectral residual statistic results revealed that while the chlorophyll-a and turbidity models could validly be applied to a vast majority of Ohio River and Licking River pixels, application of these models to Little Miami River pixels was inappropriate due to an unmodeled source of spectral variation.
While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.
The bane of non-linear waveform inversion based on the Born scattering model has been the need of a close starting model--particularly for the broad components of the velocity field. Without a close starting model the method is susceptible to failure by getting stuck in a local minima. Since these broad components of the velocity field are larger than the wavelengths of the signal, they do not scatter energy. These velocity components mainly effect the transmission of energy. Along the lines of Woodward (1990), we show that the Rytov approximation is better suited than the Born inversion for these components. The Rytov approximation can be combined with the Born approximation to form a hybrid method to perform an improved non-linear waveform inversion. This hybrid method is more effective with starting models that are not close in the broad components and may provide an optimal waveform inversion.
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