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.

With high anthropogenic pressure and the effects of climate change (e.g., sea level rise) on coastal regions, there is a greater need for accurate and up-to-date information about the topography of these systems. Reliable topography and bathymetry information are fundamental parameters for modelling the morpho-hydrodynamics of coastal areas, for flood forecasting, and for coastal management. Traditional methods such as ground, ship-borne, and airborne surveys suffer from limited spatial coverage and temporal sampling due to logistical constraints and high costs which limit their ability to provide the needed information. The recent advancements of spaceborne remote sensing techniques, along with their ability to acquire data over large spatial areas and to provide high frequency temporal monitoring, has made them very attractive for topography and bathymetry mapping. In this review, we present an overview of the current state of spaceborne-based remote sensing techniques used to estimate the topography and bathymetry of beaches, intertidal, and nearshore areas. We also provide some insights about the potential of these techniques when using data provided by new and future satellite missions.

Remote-sensing reflectance ($R_{\mathrm{rs}}$), which is defined as the ratio of water-leaving radiance ($L_w$) to downwelling irradiance just above the surface ($E_d(0^{+})$), varies with both water constituents (including bottom properties of optically-shallow waters) and angular geometry. $L_w$ is commonly measured in the field or by satellite sensors at convenient angles, while $E_d(0^{+})$ can be measured in the field or estimated based on atmospheric properties. To isolate the variations of $R_{\mathrm{rs}}$ (or $L_w$) resulting from a change of water constituents, the angular effects of $R_{\mathrm{rs}}$ (or $L_w$) need to be removed. This is also a necessity for the calibration and validation of satellite ocean color measurements. To reach this objective, for optically-deep waters where bottom contribution is negligible, we present a system centered on water’s inherent optical properties (IOPs). It can be used to derive IOPs from angular $R_{\mathrm{rs}}$ and offers an alternative to the system centered on the concentration of chlorophyll. This system is applicable to oceanic and coastal waters as well as to multiband and hyperspectral sensors. This IOP-centered system is applied to both numerically simulated data and in situ measurements to test and evaluate its performance. The good results obtained suggest that the system can be applied to angular $R_{\mathrm{rs}}$ to retrieve IOPs and to remove the angular variation of $R_{\mathrm{rs}}$.

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within $\pm 10\%$ for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Radar altimetry provides unique information on water stages of inland hydro-systems. In this study, the performance of seven altimetry missions, among the most commonly used in land hydrology (i.e., European Remote-Sensing Satellite-2 (ERS-2), ENVIronment SATellite (ENVISAT), Satellite with Argos and ALtika (SARAL), Jason-1, Jason-2, Jason-3 and Sentinel-3A), are assessed using records from a dense in situ network composed of 19 gauge stations in the Inner Niger Delta (IND) from 1995 to 2017. Results show an overall very good agreement between altimetry-based and in situ water levels with correlation coefficient (R) greater than 0.8 in 80% of the cases and Root Mean Square Error (RMSE) lower than 0.4 m in 48% of cases. Better agreement is found for the recently launched missions such as SARAL, Jason-3 and Sentinel-3A than for former missions, indicating the advance of the use of the Ka-band for SARAL and of the Synthetic-aperture Radar (SAR) mode for Sentinel-3A. Cross-correlation analysis performed between water levels from the same altimetry mission leads to time-lags between the upstream and the downstream part of the Inner Niger Delta of around two months that can be related to the time residence of water in the drainage area.