Imaging spectroscopy for coastal biogeochemistry of estuaries and plumes
Shimoni, M.; Acheroy, M. (2006). Imaging spectroscopy for coastal biogeochemistry of estuaries and plumes, in: Bostater, C.R. et al. (Ed.) Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2006. Stockholm, Sweden, September 11-13, 2006. Proceedings of SPIE, the International Society for Optical Engineering, 6360: pp. U46-U55. https://dx.doi.org/10.1117/12.689992
The coastal zone is an extremely dynamic system. Variations in the concentration of its major constituents occur rapidly over space and time. This is in response to changes in bathymetry and tidal forces coupled with the influences of fronts, upwelling zones and river inflow. Today's researches on the functioning of estuarine and coastal ecosystems, as well as attempts to quantify some of their biogeochernical fluxes are based on highly time consuming and costly sea campaigns and laboratory analyses. On September 2002, an airborne campaign using CASI sensor covered part of the Scheldt estuary (BelgiumNetherlands coastal zone). A 13 sampling stations field survey was realised in order to cover as quickly as possible the wide range of water quality encountered from the mouth of the estuary to the outer limit of the plume. Correlation was searched between classical ground truth measurements and the rich information provided by numerous CASI-SWIR spectral bands carefully chosen. These relations were not sufficient enough to derive synoptic view of the spatial distribution of many biogeochemical parameters in the Scheldt estuary and plume. In this research we found that some biogeochernical parameters of interest in estuaries and plumes that were retrieved using imaging spectroscopy techniques as the MF (Matched filtering) and the MTMF (Mixture Tuned Matched Filtering) are very encouraging. We showed that using those spectra based processing techniques we could accurately obtained the concentration distribution of suspended particulate matter (SPM) and particulate organic matter (POM), that we could not retrieved using the classical statistical techniques. Moreover, using the imaging spectroscopy techniques we significantly improved the coloured dissolved organic matter (CDOM) concentration classification, relatively to the results derived using the multiple regression technique.
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