A review on substances and processes relevant for optical remote sensing of extremely turbid marine areas, with a focus on the Wadden Sea

The interpretation of optical remote sensing data of estuaries and tidal flat areas is hampered by optical complexity and often extreme turbidity. Extremely high concentrations of suspended matter, chlorophyll and dissolved organic matter, local differences, seasonal and tidal variations and resuspension are important factors influencing the optical properties in such areas. This review gives an overview of the processes in estuaries and tidal flat areas and the implications of these for remote sensing in such areas, using the Wadden Sea as a case study area. Results show that remote sensing research in extremely turbid estuaries and tidal areas is possible. However, this requires sensors with a large ground resolution, algorithms tuned for high concentrations of various substances and the local specific optical properties of these substances, a simultaneous detection of water colour and land-water boundaries, a very short time lag between acquisition of remote sensing and in situ data used for validation and sufficient geophysical and ecological knowledge of the area. © 2010 The Author(s)

Spectra of a shallow sea-unmixing for class identification and monitoring of coastal waters

Ocean colour-based monitoring of water masses is a promising alternative to monitoring concentrations in heterogeneous coastal seas. Fuzzy methods, such as spectral unmixing, are especially well suited for recognition of water masses from their remote sensing reflectances. However, such models have not yet been applied for water classification and monitoring. In this study, a fully constrained endmember model with simulated endmembers was developed for water class identification in the shallow Wadden Sea and adjacent German Bight. Its performance was examined on in situ measured reflectances and on MERIS satellite data. Water classification by means of unmixing reflectance spectra proved to be successful. When the endmember model was applied to MERIS data, it was able to visualise well-known spatial, tidal, seasonal, and wind-related variations in optical properties in the heterogeneous Wadden Sea. Analyses show that the method is insensitive to small changes in endmembers. Therefore, it can be applied in similar coastal areas. For use in open ocean situations or coastal or inland waters with other specific inherent optical properties, re-simulation of the endmember spectra with local optical properties is required. However, such an adaptation requires only a limited number of local in situ measurements

Controlled diffusion processes wite successive rewards

Participatory science is not, as perhaps is believed, something of the 21st century. In this manuscript we show that over a century ago it were not only scientists who collected oceanographic data but also merchant sailors. A good example of such globally collected data are Forel-Ule observations, from which the first date back to 1889. This hardly explored (NOAA) dataset, containing around 228,000 of so-called ocean colour observations, was recently analysed on trends. Some of the material here presented refers to a recent publication ‘Trends in Ocean Colour and Chlorophyll Concentration from 1889 to 2000, Worldwide’ (Wernand et al., 2013). Since the launch of satellite-mounted sensors globe-wide monitoring of chlorophyll, a phytoplankton biomass proxy, became feasible. Just as satellites, the Forel-Ule (FU) scale record (a hardly explored database of ocean colour) has covered all seas and oceans - but already since 1889. We provided evidence of the usefulness of the Forel-Ule scale observation record dating back to 1889 from which changes of ocean surface chlorophyll can be reconstructed with confidence from this record. Our analysis has not revealed a globe-wide trend of increase or decrease in chlorophyll concentration during the past century; ocean regions have apparently responded differentially to changes in meteorological, hydrological and biological conditions at the surface related to global warming. Since 1889 chlorophyll concentrations have decreased in the Indian Ocean and in the Pacific; and increased in the Atlantic Ocean, the Mediterranean, the Chinese Sea, and in the seas west and north-west of Japan. Clearly, explanations of chlorophyll changes over long periods should focus on hydrographical and biological characteristics typical of single ocean regions, not on those of ‘the’ ocean. To facilitate climate change research we recommend the reintroduction and use of the Forel-Ule scale to expand the historic database. Accordingly, through participatory science, with the help of the public, we like to establish this goal. We suggest the manufacturing and distribution of a new type, easy to make, Forel-Ule scale, recently developed within the EU-project „Citizens’ Observatory for Coast and Ocean Optical Monitoring“ (Citclops). Additionally, within the same project a smartphone App is being developed to facilitate public involvement in worldwide collection of Forel-Ule data

Ocean trends: The arithmetic mean derived chlorophyll concentration per year (with the no. of obs.) with superposed lines: weighted (no. of obs.) least-squares regression lines (blue or green line) indicate a bluing trend or greening trend of a sea, the 95% confidence interval of the mean (dotted line) and of the observations (solid line).

<p> A sea with no significant trend is indicated by a black line. Regression coefficients are indicated at the top of each graph.</p

The number of <i>FU</i>-observations per dataset unmasked and obtained by the use of 3 data-extraction masks.

<p>Off-coast indicates the areas where <i>FU</i>-data were collected.</p

Examples of the Ecolight modelled <i>R</i>_RS (sr⁻¹) for case 1 waters (panel a) and case 2 waters (panels b, c and d) with variable composition (see also <b>Table 4</b>).

<p>In panel (a) only the input chlorophyll concentration is varied between 0.1 and 40 mg m⁻³. For case 2 waters the input CDOM₄₄₀ absorption (a₄₄₀) is varied between 0.01 and 1.00 m⁻¹ with chlorophyll and mineral concentration of both 0 (panel b), fixed chlorophyll concentration of 0.1 mg m⁻³ and a mineral concentration of 0 (panel c) and fixed chlorophyll concentration of 0.1 mg m⁻³ and a mineral concentration of 0.2 g m⁻³ (panel d). From these <i>R</i>_RS spectral signatures the chromaticity coordinate set and <i>FU</i> number were calculated.</p

Representation of the oceans and seas identified and arranged in terms of their colour as calculated from all available observations extracted under BM2 or BM3 (see

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063766#pone-0063766-t004" target="_blank"><b>Table 4</b></a><b>).</b> The Barents Sea is the most greenish sea (bottom) and the Equatorial Pacific is the most bluish ocean (top).</p

Sea trends: the arithmetic mean <i>FU</i> values () per year (with the no. of obs.) with superposed lines; weighted (no. of obs.) least-squares regression lines (blue or green line) indicate a bluing trend or greening trend of a sea, the 95% confidence interval of the mean (dotted line) and of the observations (solid line).

<p> A sea with no significant trend is indicated by a black line. Regression coefficients are indicated at the top of each graph.</p