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Image correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is adaptable with minimal effort to hyper-or multi-spectral radiometric sensors. By using a single atmospheric correction implementation for land and water, discontinuities in reflectance within one scene are reduced. iCOR derives aerosol optical thickness from the image and allows for adjacency correction, which is SIMilarity Environmental Correction (SIMEC) over water. This paper illustrates the performance of iCOR for Landsat-8 OLI and Sentinel-2 MSI data acquired over water. An intercomparison of water leaving reflectance between iCOR and Aerosol Robotic Network-Ocean Color provided a quantitative assessment of performance and produced coefficient of determination (R 2) higher than 0.88 in all wavebands except the 865 nm band. For inland waters, the SIMEC adjacency correction improved results in the rededge and near-infrared region in relation to optical in situ measurements collected during field campaigns.

Imaging spectrometry of non-oceanic aquatic ecosystems has been in development since the late 1980s when the first airborne hyperspectral sensors were deployed over lakes. Most water quality management applications were, however, developed using multispectral mid-spatial resolution satellites or coarse spatial resolution ocean colour satellites till now. This situation is about to change with a suite of upcoming imaging spectrometers being deployed from experimental satellites or from the International Space Station. We review the science of developing applications for inland and coastal aquatic ecosystems * C. Giardino

In ocean colour remote sensing, the use of Near Infra Red (NIR) spectral bands for the retrieval of Total Suspended Matter (TSM) concentration in turbid and highly turbid waters has proven to be successful. In extremely turbid waters (TSM N 100 mg L −1) however, these bands are less sensitive to increases in TSM. Here it is proposed to use Short Wave Infra Red (SWIR) spectral bands between 1000 and 1300 nm for these extreme cases. This SWIR spectral region is subdivided into two regions, SWIR-I (1000 nm to 1200 nm) and SWIR-II (1200 nm to 1300 nm) which correspond to local minima in the pure water absorption spectrum. For both spectral regions the water reflectance signal was measured in situ with an ASD spectrometer in three different extremely turbid estuarine sites: Scheldt (Belgium), Gironde (France), and Río de la Plata (Argentina), along with the TSM concentration. A measurable water reflectance was observed for all sites in SWIR-I, while in the SWIR-II region the signal was not significant compared to the Signal-to-Noise Ratio (SNR) of current Ocean Colour (OC) sensors. For the spectral band at 1020 nm (present in Ocean and Land Colour Instrument-OLCI, onboard Sentinel-3) and at 1071 nm, an empirical single band TSM algorithm is defined which is valid for both the Gironde and Scheldt estuarine sites. This means that a single algorithm can be applied for both sites without expensive recalibration. The relationship between TSM and SWIR reflectance at 1020 and 1071 nm is linear and did not show any saturation for the concentrations measured here (up to 1400 mg L −1), while saturation was observed for the NIR wavelengths, as expected. Hence, for extremely turbid waters it is advised to switch from NIR to SWIR-I wavelengths to estimate TSM concentration. This was demonstrated for an airborne hyperspectral dataset (Airborne Prism Experiment, APEX) from the Gironde estuary having several spectral bands in the SWIR-I. The empirical single band SWIR TSM algorithm was applied to the atmospherically corrected scene providing a TSM concentration map of the Gironde from mouth to more upstream with concentrations expected in this region ranging from a few to several hundreds mg L −1. These results, i.e. the existence of a single relationship for the Scheldt and Gironde, not showing any decrease of sensitivity, highlights the importance of having SWIR bands in future ocean colour sensors for studying extremely turbid rivers, coastal areas and estuaries in the world. A further implication of these results is that there is a TSM limit for application of atmospheric correction algorithms which assume zero SWIR marine reflectance. That limit is defined here as function of wavelength and sensor noise level.

We investigated which variables, including environmental variables and food availability, could predict the spatial distribution and dynamics of benthic macrofauna on an intertidal flat. A time series of macrobenthos and sediment grain size samples was complemented by time series of microphytobenthos and saltmarsh vegetation biomass and sediment grain size from airborne hyperspectral remote sensing, and elevation from laser altimetry. Response models were constructed to predict biomass and species richness of macrobenthos as a function of the environmental variables. Total biomass and species richness was best predicted by a combination of microphytobenthos biomass and sediment characteristics as explanatory variables. Deep deposit feeders and surface deposit feeders also responded best to a combination of variables, with deep deposit feeders responding more strongly to sediment grain size and surface deposit feeders responding more strongly to microphytobenthos biomass. The environmental conditions to reach maximum biomass differed for each macrobenthos species. Application of the response models to the remote sensingderived maps of the environmental variables enabled significant predictions of the spatial distribution of macrobenthos biomass, demonstrating the differences in distribution of the macrobenthos species. The models also revealed the sensitivity of the macrobenthic community to environmental change. In situ and remote sensing data demonstrated a significant fining of the sediment and a (temporal) increase in average microphytobenthos biomass. Field observations also showed a significant increase in species richness and changes in the relative abundance of species, with a decrease in Bathyporeia pilosa, and an increase in Nereis diversicolor, Pygospio elegans and Heteromastus filiformis. Such changes in macrobenthos biomass and species richness were indeed predicted from the response models. The study demonstrates that the synoptic remote sensing techniques combined with field sampling allow efficient ecological mapping and monitoring.