Use of WorldView-2 Along-Track Stereo Imagery to Probe a Baltic Sea Algal Spiral

Abstract

The general topic here is the application of very high-resolution satellite imagery to the study of ocean phenomena having horizontal spatial scales of the order of 1 kilometer, which is the realm of the ocean submesoscale. The focus of the present study is the use of WorldView-2 along-track stereo imagery to probe a submesoscale feature in the Baltic Sea that consists of an apparent inward spiraling of surface aggregations of algae. In this case, a single pair of images is analyzed using an optical-flow velocity algorithm. Because such image data generally have a much lower dynamic range than in land applications, the impact of residual instrument noise (e.g., data striping) is more severe and requires attention; we use a simple scheme to reduce the impact of such noise. The results show that the spiral feature has at its core a cyclonic vortex, about 1 km in radius and having a vertical vorticity of about three times the Coriolis frequency. Analysis also reveals that an individual algal aggregation corresponds to a velocity front having both horizontal shear and convergence, while wind-accelerated clumps of surface algae can introduce fine-scale signatures into the velocity field. Overall, the analysis supports the interpretation of algal spirals as evidence of a submesoscale eddy and of algal aggregations as indicating areas of surface convergence.

Properties and evolution of a submesoscale cyclonic spiral

“…In analogy to dense filaments (McWilliams et al, 2009;McWilliams, 2017), the secondary circulation is an overturning cell with intense downwelling in the eddy center and weak upwelling at the periphery. Besides the ability to accumulate ice (Manucharyan and Thompson, 2017), the convergent near-surface flow may concentrate any other buoyant flotsam in submesoscale cyclones, such as plastic debris (van Sebille et al, 2020;Barboza et al, 2019;Turner et al, 2019), oil (D'Asaro et al, 2018, macroalgae (Zhong et al, 2012), buoyant plankton (Hernández-Hernández et al, 2020), or cyanobacteria (Marmorino and Chen, 2019).…”

Brief Communication: Mesoscale and submesoscale dynamics in the marginal ice zone from sequential synthetic aperture radar observations

“…To estimate the velocity field from a pair of sequential SAR images, we implement the following procedure: (i) image calibration for every image in the pair; (ii) selection of overlapping image fragments, their normalization and filtering; and (iii) calculation of horizontal velocity field for image fragments using one of the methods for velocity estimation from image sequences (e.g., Emery et al, 1986;Chen, 2011;Marmorino and Chen, 2019). The Sentinel-1 images are calibrated to obtain the normalized radar cross-section units.…”

“…This issue is addressed during the normalization step but might be difficult to overcome for very thin ice whose backscatter is very sensitive to the described changes in the viewing geometry and near-surface winds. For demonstration, here we use the maximum cross-correlation method (MCC) (Emery et al, 1986;Qazi et al, 2014) to retrieve the surface velocity vectors, but we acknowledge that more elaborated velocimetry methods can also be used for this purpose (see e.g., Chen, 2011;Marmorino and Chen, 2019). The preliminary analysis of SAR data from various dates in summer 2017 suggests that MCC works rather effectively for typical sea ice concentrations encountered along the ice edge and in the marginal ice zone, provided the movement of ice floes is apparent in the sequential SAR images.…”

Brief Communication: Mesoscale and submesoscale dynamics of marginal ice zone from sequential SAR observations

“…The procedure of velocity estimation from sequential SAR images has several main steps: i) image calibration for every image in the pair, ii) selection of overlapping image fragments, their normalization and filtering, iii) calculation of horizontal velocity field for image fragments using one of the methods for velocity estimation from image sequences (e.g. Emery et al, 1986;Chen, 2011;Marmorino and Chen, 2019). At first, S-1 images were calibrated to obtain normalized radar cross-section units.…”

“…We also 85 acknowledge that more elaborated alternative methods can be used for this purpose (see e.g. Chen, 2011;Marmorino and Chen, 2019). Given the spatial resolution of the SAR images of 88×87 m in range and azimuth directions, and the time lag between sequential images equal to 48 minutes, the velocity detection threshold in this case would be 0.03 m s -1 , similar to (Marmorino and Chen, 2019).…”