The global optimal solution (GOS) has proven to be very accurate for deriving water surface velocities from contemporaneous image pairs, but previous studies have used shore-based radars or satellite measurements with resolutions on the order of a kilometer or tens of meters to establish this. In contrast, the objective of this paper is to derive a GOS velocity field from infrared (IR) (3-5 mu m) images having a 1-m pixel size. Because IR images can frequently exhibit a low signalto-noise ratio, a newly developed GOS technique with a local similarity metric is used to retrieve velocities from such low-contrast data sets. To demonstrate the utility of this new method, we use airborne data collected in the tidal Potomac River over 11 days and spatially varying flood, ebb, and slack conditions. The resulting GOS-derived velocity estimates are compared against acoustic Doppler current profiler (ADCP) measurements taken simultaneously with the airborne collections. Velocity magnitudes and directions are found to have GOS versus ADCP correlations of 0.93 and 0.99, respectively. This high correlation suggests that the GOS technique may be applicable to a variety of lower contrast imagery that can now be collected by a low-altitude remotely controlled aircraft.

We present here results that demonstrate the potential of near-infrared (NIR)-red models to estimate chlorophyll- a (chl- a) concentration in coastal waters using data from the spaceborne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrometer (MERIS), the use of sensors such as HICO has become critical for coastal ocean color research. Algorithms based on two- and three-band NIR-red models, which were previously used very successfully with MERIS data, were applied to HICO images. The two- and three-band NIR-red algorithms yielded accurate estimates of chl- a concentration, with mean absolute errors that were only 10.92% and 9.58%, respectively, of the total range of chl- a concentrations measured over a period of several months in 2012 and 2013 on the Taganrog Bay in Russia. Given the uncertainties in the radiometric calibration of HICO, the results illustrate the robustness of the NIR-red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to HICO data as they relate to estimating chl- a concentration in productive coastal waters. Inherent limitations due to the characteristics of the sensor and its orbit prohibit HICO from providing anywhere near the level of frequent global coverage as provided by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining water quality in select coastal areas and the cross-sensor applicability of NIR-red models and provide an indication of what could be achieved with future spaceborne hyperspectral sensors in estimating coastal water quality.

We present here results that strongly support the use of NIR-red algorithms, developed based on the spectral channels of MERIS, as standard tools for estimating chlorophyll-a (chl-a) concentration in turbid productive waters. We used an extensive set of MERIS imagery and in situ data collected between 2008 and 2010 in the Azov Sea and the Taganrog Bay, Russia. The overall estimation errors were only 5.5% of the total range of chl-a concentrations measured, illustrating the high accuracy of the MERIS-based NIR-red algorithms without the need for case-specific re-parameterization. The NIR-red algorithms were also applied to a series of images acquired by HICO over the same region in 2012–13, after the demise of MERIS. The results demonstrated the strong potential of HICO as a reliable tool for determining coastal water quality.

We discuss a new inverse model to estimate surface velocity using an image sequence with more than one tracer. The method employs the global optimal solution technique, which covers an image scene with subarrays or tiles, within which both velocity components are specified as bilinear forms. Substitution into the tracer conservation equation for each tracer yields a system of equations for the velocity field, which is constrained to fit the image data in a least-squares sense over the entire image domain. Solution is achieved iteratively by a Gauss-Seidel method. A numerical model is used as a benchmark to examine the accuracy of this new technique. Image pairs from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the NASA Terra and Aqua spacecraft and the hyperspectral imager for the coastal ocean instrument currently onboard the International Space Station are also used to demonstrate the new inverse model with actual ocean data. The derived vector field from MODIS is then compared with the velocity field obtained by the single-tracer technique, and the results are found to be qualitatively equivalent.

Data from the Hyperspectral Imager for the Coastal Ocean (HICO) were used to identify gradients and patchiness due to chl-a and suspended matter in the Yangtze Estuary and the Hangzhou Bay in China. HICO images were classified using the spectral analysis to identify frontal systems and areas of plankton patchiness in these water bodies. The results obtained from the analysis were compared with results obtained from previous studies conducted in these water bodies. The chl-a concentrations estimated from atmospherically corrected HICO images were comparable to chl-a concentrations measured in situ previously in this region. The spatial patterns of chl-a distribution derived from the HICO data matched well with the spatial patterns derived from a MODIS image acquired on the same day.