Abstract
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.
Highlights
Monitoring suspended particulate matter (SPM, see Table 1 for symbols and acronyms) spatio-temporal distribution in coastal waters is of particular importance for a variety of applications dedicated to coastal management which often implicitly contain an economic interest
Forecasting SPM dynamics in response to natural or anthropogenic forcing is at the same time of high interest for optimizing human efforts related to unbalanced sediments stocks as, for instance, for better anticipating dredging activities in areas affected by coastal environmental changes
The correlation between Rrs and SPM was analyzed in logarithmic scale for the wavelengths available on current ocean color sensors prior to the development and validation of the SPM algorithms (Figure 4)
Summary
Monitoring suspended particulate matter (SPM, see Table 1 for symbols and acronyms) spatio-temporal distribution in coastal waters is of particular importance for a variety of applications dedicated to coastal management which often implicitly contain an economic interest. Forecasting SPM dynamics in response to natural (river discharges, tidal current, waves, etc.) or anthropogenic forcing is at the same time of high interest for optimizing human efforts related to unbalanced sediments stocks as, for instance, for better anticipating dredging activities in areas affected by coastal environmental changes. The latter applications are often based on the development of sediment transport models for which reliable information on SPM variability represents a crucial input for the adjustment of some key parameters [3,4,5]. Information on particulate matter in water masses is crucial for better constraining water quality or pollution [7,8]
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