A multi-wavelengths algorithm to estimate Suspended Particulate Matter (SPM) from Ocean Color Remote Sensing

Suspended Particulate Matter (SPM) is a major water constituent in coastal waters that are involved in processes such as attenuating light, carrying adsorbed pollutant, and clogging waterways. The geographical distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing on board satellite complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Methods to date for this application are based on empirical relations between SPM and its optical properties and use a single satellite band which is switched for different ranges of turbidity. The necessity to switch bands is due to saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach which, in addition to SPM concentration, also provides estimated uncertainties. The uncertainties are based on sensor accuracy, atmospheric correction as well as uncertainties in empirical inputs used. The approach proposed is general and can be applied to any atmospherically corrected ocean color sensor or in-situ radiometer system. We applied our method to four comprehensive, globally distributed in situ datasets of spectral water reflectance and SPM concentration. Results suggest: 1) a good performance of our multi-wavelength algorithm (SPM > 12gm^-3) in comparison to state-of-the-art algorithms with a bias ranging from 4.5% to 19%, and 2) higher uncertainties in the proposed approach in lower SPM concentrations (SPM < 12gm^-3). The overall performance of the algorithm, despite differences in sediments characteristics, suggest the global applicability of our approach to mapping SPM when nothing is known about the study site, and allowing it to be tuned when more information is available.