The Mediterranean near-real-time multi-sensor processing chain has been set up and is operational in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS). This work describes the main steps operationally performed to enable single ocean colour sensors to enter the multi-sensor processing applied to the Mediterranean Sea by the Ocean Colour Thematic Assembly Centre within CMEMS. Here, the multi-sensor chain takes care of reducing the inter-sensor bias before data from different sensors are merged together. A basin-scale in situ bio-optical dataset is used both to fine tune the algorithms for the retrieval of phytoplankton chlorophyll and the attenuation coefficient of light, Kd, and to assess the uncertainty associated with them. The satellite multi-sensor remote sensing reflectance spectra agree better with the in situ observations than those of the single sensors. Here, we demonstrate that the operational multi-sensor processing chain compares sufficiently well with the historical in situ datasets to also confidently be used for reprocessing the full data time series.

Macroscopic stratospheric aerosol properties such as surface area density (SAD) and volume density (VD) are required by modern chemistry climate models. These quantities are in continuous need of validation by observations. Direct observation of these parameters is not possible, but they can be derived from optical particle counters (OPCs) which provide concentration (number density) and size distributions of aerosol particles, and possibly from ground‐based and satellite‐borne lidar observations of particle backscatter coefficients and aerosol type. When such measurements are obtained simultaneously by OPCs and lidars, they can be used to calculate backscatter and extinction coefficients, as well as SAD and VD. Empirical relations can thus be derived between particle backscatter coefficient, extinction coefficient, and SAD and VD for a variety of aerosols (desert dust, maritime aerosols, stratospheric aerosols) and be used to approximate SAD and VD from lidar measurements. Here we apply this scheme to coincident measurements of polar stratospheric clouds above McMurdo Station, Antarctica, by ground‐based lidar and balloon‐borne OPCs. The relationships derived from these measurements will provide a means to obtain values of SAD and VD for supercooled ternary solutions (STS) and nitric acid trihydrate (NAT) PSCs from the backscatter coefficients measured by lidar. Coincident lidar and OPC measurements provided 15 profile comparisons. Empirical expressions of SAD and VD as a function of particle backscatter coefficient, β, were calculated from fits of the form log(SAD/VD) = A + B log(β) using β from the lidar and SAD/VD from the OPC. The PSCs were classified as STS and NAT mixtures, ice being absent.

A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents Chl-a retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (R_rs) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of Chl-a observed in the literature for this basin. The R_rs and Chl-a time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea.

Inherent optical properties (IOPs) and concentrations of the sea water components are key quantities in supporting the monitoring of the water quality and the study of the ecosystem functioning. In coastal waters, those quantities have a large spatial and temporal variability, due to river discharges and meteo-marine conditions, such as wind, wave and current, and their interaction with shallow water bathymetry. This short term variability can be adequately captured only using Geostationary Ocean Colour (OC) satellites, absent over the European seas.In this study, to compensate the lack of an OC geostationary sensor over the North Adriatic Sea (NAS), the Virtual Geostationary Ocean Colour Sensor (VGOCS) dataset has been used. VGOCS contains data from several OC polar satellites, making available multiple images a day of the NAS, approaching the temporal resolution of a geostationary sensor.Generally, data from different satellite sensors are characterized by different uncertainty sources and consequently, looking at two satellite images, it is not easy to ascertain how much of the observed differences are due to real processes. In the VGOCS dataset, the inter-sensor differences are reduced, as the satellite data were adjusted with a multi-linear regression algorithm based on in situ reflectance acquired in the gulf of Venice. Consequently, the use of the adjusted spectra as input in the retrieval of the IOPs and the concentrations allows performing a reliable analysis of the short-time bio-optical variability of the basin.In this work, we demonstrate the suitability of VGOCS to better characterise the river-sea interaction and to understand the influence of the river forcing on the short time variability of IOPs and concentrations in the coastal areas. This variability will be analysed for different case studies characterised by a different regime of river discharges, using meteorological, hydrological, and oceanographic fields as ancillary variables. This new approach and the availability of this new set of data represent an opportunity for interdisciplinary studies, in support to and interacting also with modelling implementations in river-sea areas.