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Remote sensing of seasonal stratification dynamics in the southern Irish Sea

Neil, Claire and Cunningham, Alex and McKee, David and Polton, Jeff A. (2012) Remote sensing of seasonal stratification dynamics in the southern Irish Sea. Remote Sensing of Environment, 127. pp. 288-297. ISSN 0034-4257

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Abstract

In early summer, a well-defined front forms at the southern boundary of Irish Sea between thermally stratified and tidally mixed waters. The Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) was used to predict the location of this front, and data from the MODIS Aqua sensor was used to derive a five-year time series of red-waveband remote sensing reflectance (Rrs667) and sea surface temperature (SST) for two representative areas on either side of the frontal region. Pronounced annual cycles in Rrs667 and SST were observed at both locations, with summer minima in Rrs667 being roughly coincident with SST maxima. Previous studies have shown that Rrs667 in the Irish Sea is mainly determined by near-surface concentrations of suspended mineral particles. The annual cycles in reflectance therefore indicate that vertical mixing of the water column in winter supplies fine particles to the surface layer which settle out in calmer summer conditions. While the temporal resolution achievable by remote sensing in a single year was limited by the high incidence of cloud cover, monthly averaged data for the whole five-year period revealed differences of the order of one month in the timing of the reflectance cycle on the mixed and stratified sides of the front. This difference produced transient patterns of reflectance in early summer (and occasionally in autumn) in which sharp contrasts coincided with the location of the tidal front predicted by the POLCOMS model. We conclude that optical remote sensing in shelf seas can provide information not only on changing concentrations of optically significant materials, but also on the location and timing of dynamical processes such as seasonal front formation, and note that such information is of potential value for tuning and validating numerical models of shelf sea ecosystems.