Abstract:Monitoring river plume dynamics and variations in complex coastal areas can provide useful information to prevent marine environmental damage. In this work, the Robust Satellite Techniques (RST) approach has been implemented and tested on historical series of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor, for the first time, Suspended Particulate Matter (SPM) anomalies associated to river plumes. To this aim, MODIS-Aqua Level 1A data were processed using an atmospheric correction adequate for coastal waters, and SPM daily maps were generated applying an algorithm adapted from literature. The RST approach was then applied to these maps to assess the anomalous presence of SPM. The study area involves the Basilicata region coastal waters (Ionian Sea, South of Italy). A long-time analysis (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) conducted for the month of December allows us to find that the maximum SPM concentration value was registered in December 2013, when an extreme hydrological event occurred. A short-time analysis was then carried out applying RST to monitor the dynamics of anomalous SPM concentrations. Finally, the most exposed areas, in terms of SPM concentration, were identified. The results obtained in this work showed the RST high potential when used in combination with standard SPM daily maps to better characterize and monitor coastal waters.
Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST) approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize "false detections" caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy.
Standard chlorophyll-a (chl-a) algorithms, which rely on Moderate Resolution Imaging Spectro-radiometer (MODIS) data aboard the Aqua satellite, usually show different performances depending on the area under consideration. In this paper, we assessed their accuracy in retrieving the chl-a concentration in the Basilicata Ionian Coastal waters (Ionian Sea, South of Italy). The outputs of one empirical (Med-OC3) and two semi-analytical algorithms, the Garver-Siegel-Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, have been compared with ground measurements acquired during three different measurement campaigns. The achieved results prove the poor accuracy (adjusted R 2 value of 0.12) of the investigated empirical algorithm and, conversely, the good performance of semi-analytical algorithms (adjusted R 2 ranging from 0.74 to 0.79). The coexistence of Coloured Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP) has likely determined large errors in the reflectance ratios used in the OCx form algorithms. Finally, a local scale assessment of the bio-optical properties, on the basis of the in situ dataset, allowed for the definition of an operational local scale-tuned version of the MODIS chl-a algorithm, which assured increased accuracy (adjusted R 2 value of 0.86). Such a tuned algorithm version can provide useful information which can be used by local authorities within regional management systems.
Oceanographic cruises have been conducted on the Condor seamount (SW Faial Island, Azores archipelago, NE Atlantic) since 2009 to collect in situ data and understand potential seamount effects on local biodiversity. Satellite data have been concurrently collected to infer the space-time upper-ocean optical property variability and the associated physical processes. The main limitation of this analysis is the persistent and significant cloud coverage above the region that, especially in some seasons, can significantly hinder satellite data availability. This study was meant to test the robust satellite technique (RST) over the Condor seamount, assess its capability to estimate multiyear trends and identify space-time anomalies. To this aim, 11-year MODIS/AQUA level 2-derived chlorophyll-a (chl-a) data were used. Results achieved for October 2010 show, within a large-scale analysis, the presence of well-defined areas of near-surface chl-a anomalies, highlighting the occurrence of a trapping effect due to flow-topography interaction processes. Regarding the Condor area, the chl-a anomalies detected along the eastern side of the seamount were linked to a strong vertical mixing that provided sufficient inorganic nutrients requested for productivity. The achieved results, whose accuracy was also tested through a comparison with in situ data, are consistent with those independently obtained by other authors who described the phytoplankton variability around the Condor seamount. This study shows the high potential of the RST approach to assess the chl-a variability in the space-time domain in oligotrophic regions such as the Azores, allowing the identification of the most important areas to be preserved and/or managed
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