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 sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions.

The algorithms of Novoa et al. (2017) and Gons et al. (2005) were recalibrated and applied to Sentinel2 data to respectively retrieve suspended particulate matter (SPM) and chlorophyll a (chl a) concentration in the environmentally and economically important intertidal zones. Sentinel2-derived chl a and SPM concentration distributions were analyzed at the scale of an oyster farm over a variety of tidal conditions. Sentinel2 imagery was then coupled with ecophysiological modeling to analyze the influence of tide-driven chl a and SPM dynamics on oyster clearance and chl consumption rates. Within the studied oyster farming site (Bourgneuf Bay along the French Atlantic coast), chl consumption rate mirrored the changes in chl a concentration during neap tides, whereas oyster clearance and chl consumption rates were both negatively impacted by high SPM concentration during spring tides.

At the end of the SPOT4 mission, a four-month experiment was conducted in 2013 to acquire high spatial (20 m) and high temporal (5 days) resolution satellite data. In addition to the SPOT4 (Take5) dataset, we used several Landsat5, 7, 8 images to document the variations in suspended particulate matter (SPM) concentration in the turbid Gironde and Loire estuaries (France). Satellite-derived SPM concentration was validated using automated in situ turbidity measurements from two monitoring networks. The combination of a multi-temporal atmospheric correction method with a near-infrared to visible reflectance band ratio made it possible to quantify SPM surface concentration in moderately to extremely turbid waters (38–4320 g·m⁻³), at an accuracy sufficient to detect the maximum turbidity zone (MTZ) in both estuaries. Such a multi-sensor approach can be applied to high spatial resolution satellite archives and to the new ESA Sentinel-2 mission. It offers a promising framework to study the response of estuarine ecosystems to global changes at unprecedented spatio-temporal resolution.

High resolution satellite data of the Medium Resolution Imaging Spectrometer in full resolution mode (MERIS FR, pixel size is 300 m) were used to study the impact of suspended particulate matter (SPM) on oyster‐farming sites in a macrotidal bay of the French Atlantic coast where SPM concentration can exceed 100 g m⁻³. Because MERIS standard SPM concentration retrieval saturates at about 50 g m⁻³, we developed an alternative method for turbid nearshore waters. The method consists in the combination of the Semi‐Analytical Atmospheric and Bio‐Optical (SAABIO) atmospheric correction with a regional bio‐optical algorithm based on a linear relationship between SPM concentration and the reflectance band ratio at 865 and 560 nm. MERIS FR‐derived SPM concentrations were validated from 10 up to 300 g m⁻³, and then merged with oyster ecophysiological responses to provide a spatial picture of the impact of SPM concentration on oyster‐farming sites. Our approach demonstrates the potential of high resolution satellite remote sensing for aquaculture management and shellfish‐farming ecosystems studies.

Editorial: Remote Sensing for Aquaculture

Seagrasses are marine flowering plants that form extensive meadows from the inter-tidal zone up to ~50 m depth. As biological and ecological Essential Biodiversity Variables, seagrass cover and composition provide a wide range of ecosystem services. Inter-tidal seagrass meadows provide services to many ecosystems, so monitoring their occurrence, extent, condition and diversity can be used to indicate the biodiversity and health of local ecosystems. Current global estimates of seagrass extent and recent reviews either do not mention inter-tidal seagrasses and their seasonal variation, or combine them with sub-tidal seagrasses. Here, using high-spatial and high-temporal resolution satellite data (Sentinel-2), we demonstrate a method for consistently mapping inter-tidal seagrass meadows and their phenology at a continental scale. We were able to highlight varying seasonal patterns that are observable across a 23° latitudinal range. Timings of peaks in seagrass extent varied by up to 5 months, rather than the previously assumed marginal to non-existent variation in peak timing. These results will aid management by providing high-resolution spatio-temporal monitoring data to better inform seagrass conservation and restoration. They also highlight the high level of seasonal variability in inter-tidal seagrass, meaning combination with sub-tidal seagrass for global assessments will likely produce misleading or incorrect estimates.