In most coastal waters, riverine inputs of suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) are the primary optically active constituents. Moderate- and high-resolution satellite optical sensors, such as the Operational Land Imager (OLI) on Landsat-8 and the MultiSpectral Instrument (MSI) on Sentinel-2, offer a synoptic view at high spatial resolution (10–30 m) with weekly revisits allowing the study of coastal dynamics (e.g., river plumes and sediment re-suspension events). Accurate estimations of CDOM and SPM from space require regionally tuned bio-optical algorithms. Using an in situ dataset of CDOM, SPM, and optical properties (both apparent and inherent) from various field campaigns carried out in the coastal waters of the estuary and Gulf of St. Lawrence (EGSL) and eastern James Bay (JB) (N = 347), we developed regional algorithms for OLI and MSI sensors. We found that CDOM absorption at 440 nm [a_g (440)] can be retrieved using the red-to-green band ratio for both EGSL and JB. In contrast, the SPM algorithm required regional adjustments due to significant differences in mass-specific inherent optical properties. Finally, the application of regional algorithms to satellite images from OLI and MSI indicated that the atmospheric correction (AC) algorithm C2RCC gives the most accurate remote-sensing reflectance (R_rs) absolute values. However, the ACOLITE algorithm gives the best results for CDOM estimation (almost null bias; median symmetric accuracy of 45% and R² of 0.78) as it preserved the R_rs spectral shape, while tending to yield positively bias SPM (88%). We conclude that the choice of the algorithm depends on the parameter of interest.

We present a novel motorized platform, a custom-built jet-ski designed for acoustic and radiometric measurements in optically shallow coastal waters. This platform integrates in-water radiometers with single-beam and multi-beam acoustic sensors, along with a suite of active and passive instruments (conductivity-temperature-depth, fluorescence, and backscattering sensors). Such a configuration is particularly well-suited for environments where water depth, bottom type, and optical properties must be jointly characterized. Here, we perform a detailed assessment of the platform's design and its impact on the uncertainty of radiometric measurements and derived apparent optical properties (AOPs). Using a Monte Carlo approach, we propagated uncertainties associated with radiometric measurements, sensor depth, platform tilt, and self-shading corrections. The total uncertainty budget was decomposed to identify the relative contribution of each component. Our results show that over half of the total uncertainty originates from upwelling radiance measurements at two depths near the sea–air interface. The shallower sensor ( $$$ \sim $$$ 15 cm) contributes 37–48% of the uncertainty, while the deeper sensor ( $$$ \sim $$$ 30 cm) accounts for 19–27%. Downwelling irradiance above the surface contributes an additional 12–20%. Overall, the uncertainty associated with in-water radiometry from the jet-ski was comparable to that of above-water systems (e.g., HyperSAS) operated simultaneously at two stations. These findings support the use of the jet-ski platform for research applications in optically shallow waters, offering a reliable and mobile solution for spatially extensive sampling and remote sensing validation.

We present the vicarious calibration and validation of atmospheric correction for the airborne Watersat Imaging Spectrometer Experiment (WISE) sensor, deployed over the south-west shores of Anticosti Island (Gulf of Saint Lawrence). Coincident in situ water-leaving reflectance was acquired from the research vessel Coriolis II (HyperSAS, above-water radiometry) and from a jet-ski (underwater radiometry). The HyperSAS Hyperspectral Surface Acquisition System dataset was used for calibration, while the jet-ski dataset was split into 30% calibration and 70% validation. We evaluated Ratio and Empirical Line (EL) calibration approaches and investigated the influence of residual sun glint. Modeled vs. measured at-sensor reflectance residuals show a clear dependence on angular distance to the specular direction, demonstrating that geometry-dependent glint bias affects vicarious gain estimation. The Ratio approach performs adequately with a limited calibration range (∼50% accuracy at 500 nm) but degrades when applied to a broader matchup dataset. In contrast, the EL method provides more stable gains by accommodating both additive and multiplicative discrepancies (∼20% accuracy at 500 nm). These results highlight the importance of accounting for residual sun glint in airborne aquatic imaging spectroscopy (for calibration or other purposes) and support the EL with Standard Major Axis regression as the most robust calibration strategy for quantitative retrieval of water-leaving reflectance from WISE.