Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.
Most methods for interpreting water-leaving reflectance assume vertical homogeneity in the water column. This approach is relevant to quite homogeneous waters or to very turbid waters where reflectance is related to the optical properties of a thin upper surface layer. In weakly turbid and stratified waters, water-leaving reflectance depends on the vertical profile of each optically significant component. This study presents a semi-empirical inversion method to estimate Total Suspended Matter (TSM) concentrations from reflectances in sediment-dominated and stratified waters. The method derives from the concept of a remote sensing estimate of concentration, which is calculated from the profile of suspended matter concentration and from the profile of diffuse attenuation coefficient. A simplification is introduced when the diffuse attenuation profile is not known. The simplified method can be applied to satellite data to retrieve 'optically-effective' concentrations showing better correlations with reflectances than subsurface concentrations. The method is extensively described in the paper and is applied, as an example, to field measurements and to SPOT data over the Ebro river mouth area. Its potential use in connection with numerical modelling of suspended sediment transport is also discussed.
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