Abstract:The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρ w ) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. ρ w relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low-to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in ρ w retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries' monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color

The Forel-Ule colour comparator scale has been applied globally and intensively by oceanographers and limnologists since the 19th century, providing one of the oldest oceanographic data sets. Present and future Forel-Ule classifications of global oceanic, coastal and continental waters can facilitate the interpretation of these long-term ocean colour data series and provide a connection between the present and the past that will be valuable for climate-related studies.Within the EC-funded project CITLOPS (Citizens' Observatory for Coast and Ocean Optical Monitoring), with its main goal to empower endusers, willing to employ community-based environmental monitoring, our aim is to digitalize the colours of the Forel-Ule scale to establish the colour of natural waters through smartphone imaging. The objective of this study was to reproduce the Forel-Ule scale following the original recipes, measure the transmission of the solutions and calculate the chromaticity coordinates of the scale as Wernand and Van der Woerd did in 2010, for the future development of a smartphone application. Some difficulties were encountered when producing the scale, so a protocol for its consistent reproduction was developed and is described in this study. Recalculated chromaticity coordinates are presented and compared to measurements conducted by former scientists. An error analysis of the spectral and colourimetric information shows negligible experimental errors.

This document presents the WAter COlor from Digital Images (WACODI) algorithm, which extracts the color of natural waters from images collected by low-cost digital cameras, in the context of participatory science and water quality monitoring. SRGB images are converted to the CIE XYZ color space, undergoing a gamma expansion and illumination correction that includes the specular reflection at the air-water interface.The XYZ values obtained for each pixel of the image are converted to chromaticity coordinates and Hue color angle (a w ), which is a measure of color. Based on the distributions of a w in sub-sections of the image, an approximation of the intrinsic color of the water is obtained. This algorithm was applied to images acquired in 2013 during two field campaigns in Northern Europe. The Hue color angles were derived from hyperspectral measurements above and below the surface, carried out simultaneously with image acquisition. When for each station a specific illumination correction was applied, based on the corresponding hyperspectral data, a good fit (r 2 5 0.93) was obtained between the image and the spectra Hue color angles (slope 5 0.98, intercept 5 20.03). When a more generic illumination correction was applied to the same images, based on the sky conditions at the time of the image acquisition (either overcast or sunny), a slightly inferior, but still satisfactory fit resulted. Results on the application of the WACODI algorithm to the first images collected by the public via the smartphone application or "APP," developed within the European FP7 Citclops, are presented at the end of this study.

Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors in terms of spectral, spatial and temporal resolutions to (i) estimate the seawater reflectance signal and then SPM concentrations and (ii) monitor the dynamics of SPM in the Rhône River plume characterized by moderately turbid surface waters in a micro-tidal sea. Consistent remote-sensing reflectance (Rrs) values are retrieved in the red spectral bands of these four satellite sensors (median relative difference less than~16% in turbid waters). By applying a regional algorithm developed from in situ data, these Rrs are used to estimate SPM concentrations in the Rhône river plume. The spatial resolution of OLI provides a detailed mapping of the SPM concentration from the downstream part of the river itself to the plume offshore limits with well defined small-scale turbidity features. Despite the low temporal resolution of OLI, this should allow to better understand the transport of terrestrial particles from rivers to the coastal ocean. These details are partly lost using MODIS coarser resolutions data but SPM concentration estimations are consistent, with an accuracy of about 1 to 3 g¨m´3 in the river mouth and plume for spatial resolutions from 250 m to 1 km. The MODIS temporal resolution (2 images per day) allows to capture the daily to monthly dynamics of the river plume. However, despite its micro-tidal environment, the Rhône River plume shows significant short-term (hourly) variations, mainly controlled by wind and regional circulation, that MODIS temporal resolution failed to capture. On the contrary, the high temporal resolution of SEVIRI makes it a powerful tool to study this hourly river plume dynamics. However, its coarse resolution prevents the monitoring of SPM concentration variations in the river mouth where SPM concentration variability can reach 20 g¨m´3 inside the SEVIRI pixel. Its spatial resolution is nevertheless sufficient to reproduce the plume shape and retrieve SPM concentrations in a valid range, taking into account an underestimation of about 15%-20% based on comparisons with other sensors and in situ data. Finally, the capabilities, advantages and limits of these satellite sensors are discussed in the light of the spatial and temporal resolution improvements provided by the new and future generation