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This document constitutes the first Progress Report of the CryoRad-FO project, and is the project deliverable number 04.2, as required by the Technical Management Annex of the Contract between ASI and the National Research Council. The purpose of the document is to report the progress of the project in the 3 months of activity 23/06/2021 - 23/09/2021. The activities carried out, the critical issues addressed, the measures taken to mitigate them are described and the activities to be undertaken in the following quarter are indicated.

Several studies have shown the capabilities of Synthetic Aperture Radar to map sea currents in ocean regions mainly characterized by large flows. We consider the well known method based on the analysis of the Doppler Centroid. The Doppler, as, in general, the scattering from the sea, is sensitive to several phenomena, occurring between the upper ocean and atmospheric boundary layers. To investigate such phenomena, we considered the combined use of both ENVISAT calibrated amplitude and Doppler data in conjunction with hindcast wind information provided by atmospheric models as well as Wind and Doppler Geophysical Model Functions (W/D-GMF) developed, in the literature, for C-Band systems. This integrated analysis for the interpretation of the Doppler surface currents measurements was carried out on a case study located in the Mediterranean Sea which is characterized by a general low circulation regime: specifically, the coastal region around the city of Naples. In this case study, we show that generally, wind plays a direct significant role in the observed Doppler surface current. The availability of an oceanographic numerical model for one of the analyzed cases also allowed us to attempt to interpret the effect of the typical thermohaline circulation pattern on the Doppler anomaly.

This study focuses on the use of satellite remote sensing to map coastal erosion vulnerability in two Italian sites: Pianosa Island (Tuscany) and Piscinas (Sardinia). For both areas we focused on the land/water transitional ecosystem, with the aim of identifying potential coastal erosion phenomena and to demonstrate the role of benthic habitats in preserving the value of coastal environments. The method made use of ancillary and multispectral satellite data from 2016 to 2018. For this study, the first 7 bands of the VIS-NIR region of Sentinel-2 were used, all reprocessed at the spatial resolution of 10 m. The TOA (Top of Atmosphere) radiance products were atmospherically corrected and processed using the Sen2Coral add-on-tool and the BOMBER code (Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images). Maps of marine substrates and bathymetry were obtained and revealed their influence on the coastal dynamics. Then, in case of Piscinas, SAR images (COSMO SkyMed and Sentinel-1B) were added to the analyses. COSMO-SkyMed allowed us to identify the coastline and to obtain qualitative indicators about the absence/presence of changes in coastal dune system, the most relevant terrestrial element of the site. Sentinel-1B supported, by adopting an inversion process scheme, the analysis of the wave state impacting the coast. By merging the satellite products, the coastal erosion vulnerability maps have been generated based on substrate type in shallow waters and sand volume variation on land: rocky bottoms and stable meadows of phanerogams seemed preserving the coast, while the substrate characterized by a loss of phanerogams and a decrease in sand volumes might be considered more vulnerable. The results confirm that the coast of Pianosa is not suffering from coastal erosion, while the vulnerability maps of Piscinas seem to be closely linked to episodic events so that the Piscinas dune system might be considered safe from coastal erosion processes

Young sea ice types, namely grease-pancake ice (GPI), and thin ice floes as well, mainly compose the marginal ice zone (MIZ) during the freezing period. Such types of sea ice are considered the primary source of the sea ice fringing the Antarctica continent during winter season through the process called the "pancake cycle", and are massively produced in the Arctic as a result of the sea ice extent and volume decline. The properties of such sea ice types are exceedingly difficult to measure autonomously, due to their size, transient nature, and the hostile environment where they form and grow. However, some internal properties of the ice cover, such as the thickness, can in principle be inferred by measuring the modifications induced by the presence of the sea ice cover on the propagation of gravity waves coming from the open ocean. In this regard, the available spaceborne SAR imagery can be operated as a synoptic wave buoy and measure the attenuation and the shortening of incoming ocean waves inside GPI fields. The SAR inversion procedure is based on knowledge of the open sea wind-generated waves spectrum. It is essential that such waves come from the open ocean before traveling inside the GPI field. The mapping between ocean wave spectrum and SAR image spectrum is provided by the closed nonlinear integral transformation by Hasselmann and Hasselmann (Hasselmann and Hasselmann 1991) and later developments, which account for the SAR image cross-spectrum (Engen and Johnsen 1995). The wind wave spectrum is thus changed according to the rheological properties and to the thickness of the ice layer crossed in order to achieve the best fit between the observed SAR image spectrum and the simulated one. Finally, swell waves are estimated directly from the observed SAR image spectrum regardless of their provenience. The ability of different viscous layer models to describe the attenuation of gravity waves propagating in GPI covered ocean has been already investigated (De Santi et al. 2018). In particular, the Keller's model (Keller, 1998), the two-layer viscous model (De Carolis and Desiderio 2002) and the close-packing model (De Santi and Olla 2017) have been extensively validated by using wave attenuation data collected during two different field campaigns (Weddell Sea, Antarctica, April 2000; Western Arctic Ocean, autumn 2015). For thin GPI, good retrievals of ice thickness have been obtained by considering the ice layer as the only source affecting the wave dynamics, so that the wind input can be disregarded. Validation of these viscous layer models is now extended by inverting different ERS and Sentinel 1 SAR images acquired in the Arctic.

The ability of viscous layer models to describe the attenuation of waves propagating in grease-pancake ice covered ocean is investigated. In particular, the Keller's model (Keller,; https://doi.org/10.1029/97JC02966), the two-layer viscous model (De Carolis & Desiderio,; https://doi.org/10.1016/S0375-9601(02)01503-7) and the close-packing model (De Santi & Olla,; arXiv:1512.05631) are extensively validated by using wave attenuation data collected during two different field campaigns (Weddell Sea, Antarctica, April 2000; western Arctic Ocean, autumn 2015). We use these data to inspect the performance of the three models by minimizing the differences between the measured and model wave attenuation; the retrieved ice thickness is then compared with measured data. The three models allow to fit the observation data but with important differences in the three cases. The close-packing model shows good agreement with the data for values of the ice viscosity comparable to those of grease ice in laboratory experiments. For thin ice, the Keller's model performance is similar to that of the close-packing model, while for thick ice much larger values of the ice viscosity are required, which reflects the different ability of the two models to take into account the effect of pancakes. The improvement of performance over the Keller's model achieved by the two-layer viscous model is minimal, which reflects the marginal role in the dynamics of a finite eddy viscosity in the ice-free layer. A good ice thickness retrieval can be obtained by considering the ice layer as the only source in the wave dynamics, so that the wind input can be disregarded.

In recent years satellite water quality information have been used for several purposes, such as: 1) regular and specific monitoring, 2) matters that require Near Real Time information (e.g. when cyanobacterial blooms are threatening the swimming water quality) and 3) for the spatial optimisation of monitoring networks. To the aim, image processing chains need to be set up to transform the radiance recorded by satellite sensors into information on water quality, such as the concentrations of water constituents, the physical values on water surface temperature or maps depicting e.g. the spatial extent of aquatic vegetation. Despite these techniques have been developed since about three decades, there are still ongoing developments to produce accurate and consistent products across all water bodies.This is particularly the case for EOMORES, whose target lakes include a wide range of water types. Moreover, as EOMORES makes use of the latest generation of satellite sensors, consolidated image processing techniques need to be adapted according to the resolutions and spectral bands of these newest sensors. This is especially relevant for Sentinel-2 MSI,since for Sentinel-3 OLCI most of the developments are based on its precursor MERIS on Envisat (2002-2012).This document hence illustrates the image processing chain that have been adapted to produce relevant water quality information for the EOMORES study sites. A first section is dedicated to the description of the methods while the last part describe show the processors have been applied to produce maps (both of the past and recent years, including 2018) of water quality. The selected processors are now being used to set up the operational EOMORES service according to WP6.

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science. Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover. We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.

This study shows how different remote sensing techniques can be used to distinguish between surface accumulations (scum) and dense blooms of cyanobacteria in the Curonian Lagoon, the largest lagoon in Europe. Cyanobacteria blooms are a major concern in this region due to water quality issues interfering with the conservation of the whole ecosystem. Chlorophyll-a (Chl-a) concentrations can be extremely high (up to about 500 mg/m 3 in some cases) during cyanobacteria blooms, which are often associated with a surface accumulation of algae. The Medium Resolution Imaging Spectrometer (MERIS) was used to acquire 52 images covering the summers of 2004 to 2011. These images were analyzed to map Chl-a concentrations and the presence of scum using two different band ratio algorithms applied to atmospherically-corrected data. The results identify wind speed as the main driving factor in the surface accumulation of algae, as well as in the spatial distribution of Chl-a. The utility of microwave images was also assessed, as since any cloud cover obviously hampers the use of optical data. Advanced Synthetic Aperture Radar (ASAR) images were collected synchronously with the MERIS data and the normalized radar cross-section (NRCS) signal was corrected for the contribution of wind for the purposes of correlating the results with the MERIS-derived Chl-a concentrations. In general, there was a stepwise decrease in the NRCS for high values of Chl-a (N50 mg/m 3 ) with wind speeds in the range of 2 to 6 m/s. Under these conditions, our results demonstrate that optical and microwave signals can be used in combination to improve our understanding of cyanobacteria blooming.

Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A &ldquo, ready to use&rdquo, approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon.

In case of oil spills due to disasters, one of the environmental concerns is the oil trajectories and spatial distribution. To meet these new challenges, spill response plans need to be upgraded. An important component of such a plan would be models able to simulate the behaviour of oil in terms of trajectories and spatial distribution, if accidentally released, in deep water. All these models need to be calibrated with independent observations. The aim of the present paper is to demonstrate that significant support to oil slick monitoring can be obtained by the synergistic use of oil drift models and remote sensing observations. Based on transport properties and weathering processes, oil drift models can indeed predict the fate of spilled oil under the action of water current velocity and wind in terms of oil position, concentration and thickness distribution. The oil spill event that occurred on 31 May 2003 in the Baltic Sea offshore the Swedish and Danish coasts is considered a case study with the aim of producing three-dimensional models of sea circulation and oil contaminant transport. The High-Resolution Limited Area Model (HIRLAM) is used for atmospheric forcing. The results of the numerical modelling of current speed and water surface elevation data are validated by measurements carried out in Kalmarsund, Simrishamn and Kungsholmsfort stations over a period of 18 days and 17 h. The oil spill model uses the current field obtained from a circulation model. Near-infrared (NIR) satellite images were compared with numerical simulations. The simulation was able to predict both the oil spill trajectories of the observed slick and thickness distribution. Therefore, this work shows how oil drift modelling and remotely sensed data can provide the right synergy to reproduce the timing and transport of the oil and to get reliable estimates of thicknesses of spilled oil to prepare an emergency plan and to assess the magnitude of risk involved in case of oil spills due to disaster.

The use of the visible and near infrared (VNIR) bands of MODIS and MERIS imaging sensors acquired in sunglint conditions to reveal smoothed regions such as those affected by oil pollution is investigated. The underlying physical mechanism that enables oil slick detection is based on the modification of the surface slope distribution composing the wind-roughened sea due to the action of mineral oils. The role of sunglint as the chief mechanism that allows the imaging of oil slick features with VNIR wavelengths is assessed for selected case studies in the Mediterranean Sea. The high rate of acquisition and the frequent occurrence of MODIS and MERIS imagery affected by sunglint, especially in low-latitude seas, can thus significantly contribute to increase the actual oil slick detection capability offered by synthetic aperture radar (SAR) systems. We also show how the combined observations from any of the microwave and optical sensors permit the slick to be followed during its movement. Finally, a simulation study specific to the Mediterranean Sea was carried out in order to demonstrate the feasibility of such an approach supporting SAR observations.

An automatic system capable of discriminating oil spills from other similar sea surface features in Synthetic Aperture Radar images has been developed and tested. This system, called Oil Spill Automatic Detector (OSAD), was originally conceived for C-band SAR images (mostly ERS PRI) and afterward adapted to ENVISAT data. In the framework of the Progetto pilota Rilevamento Inquinamento Marino da Idrocarburi (PRIMI) national project sponsored by the Italian Space Agency, the OSAD system has been greatly improved and is now able to process L- and X-band images from various satellites as well. OSAD performance, confirmed using a different dataset of verified slicks, shows an a priori overall correct classification of 80%. Moreover, new features have been added, such as an enhanced land masking algorithm, a built-in wind and wave extraction module, and oil spill characterization. OSAD has been integrated into a complex hardware and software architecture for operational sea monitoring, alarm generation, and oil slick drift forecasting. The system׳s detection capabilities have been validated during a measurement campaign in the Mediterranean Sea. The new improved system is described herein, with special attention to latest enhancements.

International audience; Wave attenuation coefficients (α, m À1) were calculated from in situ data transmitted by custom wave buoys deployed into the advancing pancake ice region of the Weddell Sea. Data cover a 12 day period as the buoy array was first compressed and then dilated under the influence of a passing low-pressure system. Attenuation was found to vary over more than 2 orders of magnitude and to be far higher than that observed in broken-floe marginal ice zones. A clear linear relation between α and ice thickness was demonstrated, using ice thickness from a novel dynamic/thermodynamic model. A simple expression for α in terms of wave period and ice thickness was derived, for application in research and operational models. The variation of α was further investigated with a two-layer viscous model, and a linear relation was found between eddy viscosity in the sub-ice boundary layer and ice thickness.

The aim of the research reported here is to evaluate Synthetic Aperture Radar (SAR) capability to estimate the wind vector and associated directional wave spectrum. Two ERS–2 SAR images of the Mediterranean Sea, one over the Sicily Channel and one over the Ligurian Sea, were selected as case studies. Wind speed was estimated using SAR calibrated backscatter response, in conjunction with empirically derived ERS scatterometer models such as CMOD4 and CMOD–IFREMER. The predictions of these models were then compared with the actual sea surface wave spectra either provided by in situ measurements or resulting from the inversion of the SAR image spectrum. SAR detected effects of both wind and wave features, induced either by atmospheric boundary layer instability or by land shadowing, were also used as reliable indicators of wind direction.

A theoretical model describing the propagation of gravity waves on ice-covered waters is developed. The ice–water system is approximated as a two-layer viscous and immiscible Newtonian fluid. The model is validated against existing viscous models by using laboratory and in situ collected wave data.

Near-infrared (NIR) satellite images of the oil spill event caused by the Fu Shan Hai wreck on 31 May 2003 in the waters between Sweden and Denmark were compared with numerical simulations provided by the MIKE 21 oil drift model. Assuming a skewed probability density function (pdf) of oil parcel thicknesses, a model of the NIR image oil-water contrast reflectance was developed to characterize the expected oil slick distribution in terms of average and maximum oil slick thickness. Since MIKE 21 Spill Analysis (SA) also allows non-uniform distribution of oil volume within the oil slick, both distributions were thus compared by coincidence of the Moderate Resolution Imaging Spectroradiometer (MODIS/Aqua) acquisition, which imaged the oil slick 3 days after the oil spill started. Results showed an excellent agreement in the numerical values of both the expected average and the maximum thickness. In addition, repartition of the oil volume within the slick in the usual thin (sheen) and thick (brown) parts resulted, consistent with the empirical rule of 20% and 80% of the total oil volume, respectively.

Multi-sensor satellite data are used to assess cyanobacteria blooms in the Curonian Lagoon. The exploitation of SAR, in combination with optical data, is investigated to take full advantage from the all-weather, night/day SAR imaging capability. A dataset of images has been analyzed to: 1) study the effect of cyanobacteria on microwave signals; 2) assess the daily evolution of cyanobacteria bloom from multi-sensors data; and 3) evaluate the dependence of dynamics of blooms on winds. The results show a significant correlation (R 2 > 0.8, p

A synthetic aperture radar (SAR) image of the advancing winter marginal ice zone (MIZ) in the Antarctic, composed of frazil-pancake ice, has been analysed in a new way in order to test the predictions of a recently developed theory of wave dispersion in pancake ice which treats the ice as a viscous layer (Keller, 1998). In the image, obtained in April 2000, the structure of the wave spectrum in the MIZ and its change from the open-water spectrum are consistent with a pancake layer thickness of 0.2-0.3 m. Intensive in situ measurements of the pancake ice in the MIZ some 280 km W of the image location were made from FS Polarstern during a period covering the satellite imaging, and yielded a mean ice thickness of 0.24 m. We conclude that this technique is giving realistic results for ice thickness, whereas earlier work based on a different dispersion theory (mass loading) tended to over-estimate thickness. After further validation, it is therefore possible that the SAR wave technique can become an accepted method for remotely sensing ice thickness in pancake icefields.

SAR spaceborne capability to detect marine oil spills through damping of short gravity-capillary waves has been extensively demonstrated during past years. In contrast, it has not yet been found the optimal use of VIS/NIR imaging sensors for detection and monitoring of oil polluted areas. We propose the use of Modis images acquired in sun glint conditions to reveal smoothed regions such as those affected by oil pollution. According to Cox and Munk model, the physical mechanism that allows detection of oil slicks under sun glint imaging of clear sea surface is based on the modification of the wind-generated wave slopes distribution due the action of mineral oils. The methodology is demonstrated for a number of case studies occurred in the Mediterranean Sea and North Atlantic from 2001 to 2004. For each case, the oil slicks were detected by ERS SAR imaging and the corresponding Modis images were acquired within a few hours the SAR passage under sun glint conditions. The implemented procedure compares the water-leaving Cox and Munk sun glint reflectance with the reflectance measured by Modis at the top of the atmosphere (TOA). To accomplish the task, the Modis imaging parameters and an estimate of the wind vector are provided as input. The ECMWF analysis wind fields are considered for the purpose. It was found that the ratio between the TOA reflectance and the C&M reflectance enhances the capability to detect oil slicks. Moreover, an extensive analysis of the atmospheric effects on oil slick detection has been carried out by performing simulations using the 6S code. Preliminary results show that atmosphere contribution to the reflectance has little impact on oil slick detection, so that implementation of a surveillance procedure could be envisaged.

SAR spaceborne capability to detect marine oil spills through damping of wind-generated short gravity-capillary waves has been extensively demonstrated during past years. In contrast, it has not yet been found the optimal use of optical/NIR imaging sensors for detection and monitoring of polluted areas. We propose the use of Modis images acquired in sun glint conditions to reveal smoothed regions such as those affected by oil pollution. The underlying physical mechanism is based on the modification of the surface slopes distribution composing the roughened sea due to the action of mineral oils. The methodology is demonstrated for selected case studies in the Mediterranean Sea and North Atlantic where spills were detected by ERS SAR imaging. The corresponding Modis images acquired within a few hours were under sun glint conditions according to satellite imaging geometry and wind field distribution over the selected areas. Results of a detailed study about the effective applicability of the method is discussed. The importance of these results are based on the possible extensive exploitation of combined Modis and SAR data in view of the high repetitive coverage (about two times a day).

This paper is focused on the analysis of SAR imagery of the Mediterranean Sea to estimate the directional wave spectrum and the wind vector. It is discussed the potential of using fetch-limited wave spectral parameterisation, which is currently used to represent wind generated waves, in the case of SAR imaging of swells. As the interest is also focused on the role of the SAR-estimated wind vector to get reliable estimates of the wave spectra, the spectral form used in this study is that due to Donelan et al.. ERS-1 SAR imagery, which is co-located with instrumented directional buoys belonging to the Italian Buoy Network (RON, Rete Ondametrica Nazionale), was exploited. The main purpose is to study SAR capability to discriminate between locally generated wind waves and old wave systems. Three case studies were selected and analysed. They are devoted to compare wave spectral properties typical of swell systems observed at buoy stations located off-shore La Spezia, Ponza island and Alghero with SAR inverted directional wave spectra.

Over the past decade, synthetic aperture radar (SAR) sensors have demonstrated their ability to provide accurate wind speed and direction measurements in the ocean. Although SAR capability to image the sea surface at a suitable spatial resolution, SAR wind field retrieval in marine coastal areas still remains an open issue. The aim of this work is to assess the potential and limits of the Bayesian approach to the C-band NRCS SAR inversion problem for the retrieval of wind fields in marine coastal areas. A dataset of 139 SAR images gathered by the European ERS-2 and ENVISAT satellites during 2007 was analyzed to estimate the sea surface wind vectors over three selected coastal sites in the Mediterranean Sea, offshore Puglia region (Italy). The sites were instrumented to monitor marine and environmental parameters, including wind intensity and direction for further comparison with SAR wind estimations. Results discussed in this study show that the statistical approach in the retrieval of SAR wind speed can be applied in coastal areas with performances comparable with those obtained off-shore. In contrast, the SAR inversion procedure was not able to retrieve wind direction with the same accuracy.

This paper deals with the analysis of SAR imagery of the Mediterranean Sea to estimate the directional wave spectrum and the wind vector. As case study an ERS-2 SAR acquired on 13 November 1997 (orbit 13417, frame 2889) which includes Lampedusa Island in the Sicily Channel was selected. Lampedusa was chosen as test site because of its privileged location in the centre of the Mediterranean and because it hosts a fully equipped meteorological station. Besides, the selected SAR image shows a striking feature from which the wind direction can be reliably estimated.Wave field and wind vector from SAR image were compared with predictions from the WAM wave model and the wind output of the ECMWF atmospheric model, respectively. The retrieval of directional two-dimensional wave spectrum from SAR image was carried out by means of the classical Hasselmann & Hasselmann inversion scheme and the SAR image cross-spectrum methodology, respectively. Assuming the wind direction is known independently, SAR data was then analysed to retrieve the wind speed by using the predictions from empirical backscatter models, such as CMOD4 and CMOD-IFREMER. Wind vector retrieval results were validated against in situ measurements provided by the Lampedusa airport anemometer.

Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A “ready to use” approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon.