Other Applications

CleanSeaNet (CSN) is the European satellite-based Oil Spill Monitoring and Vessel Detection service, developed and operated by the European Maritime Safety Agency (EMSA). The service provides analysis of images, mainly from SAR but also from optical missions, to detect possible oil spills on the sea surface, and identify potential polluters. The service was launched in April 2007 and is now operational for a decade, supporting Member States’ actions to combat deliberate or accidental pollution in the marine environment.

CSN currently relies on data from five different synthetic aperture radar (SAR) satellites: Sentinel-1A, Sentinel-1B, Radarsat-2, TerraSAR-X and TANDEM-X. Data from these satellites is acquired and processed in near real time (NRT) by a set of contracted ground-stations and then transferred to EMSA for further value adding and distribution to end users. Information extracted from these satellite images is combined with EMSA specific datasets (i.e. vessel reporting information) and includes: oil spills location and relevant parameters (area, length and confidence level of the detection), potential polluter identification, vessel detections (including estimates of width, length, heading and confidence level of the detection) and wind and swell obtained from the SAR data. The analysis of the SAR images is semi-automatic, with trained operators that are able to distinguish between natural phenomena and discharges from vessels. Moreover, as from the phasing-in of Sentinel-1 in CSN service in the second half of 2015, the percentage of data coming from Sentinel has increased steadily, achieving 75% of the total satellite data input of CSN in 2017.

With the continuous monitoring of the European and neighbouring waters, a substantial amount of data could be gathered so far, enabling trend analysis over time. Over the past decade, almost 25,000 images have been delivered by the CSN service, providing coverage of 4,300 million km2 of sea surface. The number of possible spills detected in European waters has dropped by half during this period, from an average of 11 possible spills per km² monitored in 2007 to five possible spills per km² monitored in 2017. In this period, CSN also provided support to European coastal States in responding to 31 large accidental spills and oil related emergencies.

This paper provides an overview of the service, describing the coverage and technical features, and giving relevant statistics on the detected spills during the last decade. The trend of the results is analysed and the impact of Sentinel-1 data usage is also addressed. In fact, the spatial resolution and quality of Sentinel-1 made it possible to detect smaller spills than before: The average size of spills detected in 2017 and 2016 was 25% smaller than in 2015. Additionally the optimisation of CleanSeaNet planning, with the use of new tools, increased the ratio of sea to land surface captured on the images in 2017.

Results demonstrate the overall the long-term deterrent effect of CleanSeaNet, which together with the national and regional actions of the coastal states clearly contributed to a significant reduction in the total number of spills.

The possibilities of the polarimetric technique proposed in (Ivonin et al. 2016) to characterize the oil slicks visible on C-band (Radarsat-2) and X-band (TerraSAR-X) synthetic aperture radar (SAR) images are studied. The technique is based on the model of the electromagnetic scattering on the sea surface developed in (Kudryavtsev et al., 2003), and is related to the ratio between the damping within the slick of the short capillary-gravity waves and damping of wave breakings. This ratio is indicating by a parameter called RND (ratio of Resonant to Nonresonant signal Damping).
The technique is valid for oil slicks with thicknesses less than the radar wave skin depth, which excluded the dependence of the technique on the dielectric constant of oil. Moreover, the technique in the current version can work with the SAR images acquired at the incidence angle greater than 27- 29° when the Kirchhoff reflections from the slopes of long wind waves can be neglected.
For testing we used six SAR scenes, four acquired by Radarsat-2 and two by TerraSAR-X, containing spills of crude oil, oil emulsion, and plant (vegetable) oil (which was used to simulate simulating natural biogenic slicks), which were released in the North Sea during the Oil-on-Water Exercises in 2011 and 2012. Some of them had a high signal-to-noise ratio (SNR) in comparison to the minimal level of the signal in slicks; therefore the noise subtraction was applied before the image processing.
By processing it is shown that the mineral oils (crude oil and emulsions) considering in terms of the parameter RND and the Bragg wave number correlate well between themselves locating on the plot along a single line, which covers both C- and X- frequency bands as well as a range of incidence angles 27-49°. This fact supports the assumption that the RND parameter can describe in a general way the properties of oil slicks even sensing in different frequency bands and at different incidence angles.
Due to such coherent behavior of the RND parameter it was possible to determine the zone of RND, within which the mineral oils (crude oil and its emulsion) should lay with a high probability. The plant oil should lay outside of this zone. The width of the zone was defined by the RND standard deviation obtained during calculations. The measure of the data reliability to belong to or to be out of the zones is governed by the RND standard deviation also.
This work was supported by the Russian Foundation for Basic Research (grant no. 18-55-20010) and by the Research Council of Norway through the NORRUS project (grant no. 233896).

1. Ivonin et al. (2016). Interpreting sea surface slicks on the basis of the normalized radar cross-section model using RADARSAT-2 copolarization dual-channel SAR images. Geophysical Research Letters, 43(6), 2748-2757.

2. Kudryavtsev et al. (2003). A semiempirical model of the normalized radar cross-section of the sea surface 1. Background model. Journal of Geophysical Research: Oceans, 108(C3).

Microwave radar is a very perspective tool for all-weather monitoring of organic films (slicks) which appear in radar imagery of the surface of water bodies as areas of reduced backscattering due to damping of short wind waves. Single band/one polarized radar seems to be insufficient for detailed film characterization, hence, new capabilities of the use of  multi-polarization and multiband radar for monitoring of film slicks have been actively discussed in the literature.

In this paper results of recent field experiments on remote sensing of film slicks using dual co-polarized radars: a satellite X-band radar (TerraSAR-X) and a new X-/C-/S-band radar of the Institute of Applied Physics RAS mounted onboard a research vessel are presented. Assuming that the radar backscatter is a sum of Bragg (polarized) and non Bragg (non polarized) components each of the two can be eliminated from the total intensity of a radar signal at VV and HH polarizations. The Bragg component (BC) is due to radar scattering from resonance cm-scale wind waves, while the non Bragg component (NBC) is hypothesized to be associated with scattering from wave breakers. Therefore, one can analyze separately variations of BC and NBC due to films, and contrasts for both the components in slicks can be estimated. It has been obtained in experiment that contrast (damping ratio) demonstrates similar dependences on radar wave number for both BC and NBC. The contrast values for all the components of radar return depend on azimuth angle (an angle between radar look and wind velocity) being minimal and weakly dependent on radar wavenumber for cross-wind observations; for upwind or downwind observations the contrast grows monotonically with radar wavenumber. In most cases the contrast values for NBC are found to be nearly the same or a little less than contrasts for BC. Supposing that NBC is associated with wave breaking, one can conclude that apart from enhanced damping of cm-scale Bragg waves due to film, the latter also influences processes of wave breaking for longer, dm- and m-scale wind waves. Results of preliminary wave tank observations of steep gravity-capillary waves are presented demonstrating strong influence of films on micro breaking wave features, such as toe- and bulge structures, which presumable play dominant role in radar non Bragg scattering. To understand better  suppression of radar backscatter in slicks further studies of the action of films on wave breaking processes is needed. This work was supported by RSF (Project 18-17-00224).

It is well known that rain events leave fingerprints on synthetic aperture radar (SAR) images acquired over the ocean, but it is not always easy to identify them unambiguously, especially not on C-band SAR images. Rain becomes visible on SAR images acquired over the ocean via several mechanisms: 1) by variations of the sea surface roughness caused by downdraft winds associated with rain cells and by rain drops impinging onto the sea surface (surface scattering) and  2) by scattering and attenuation of the radar beam by rain drops in the atmosphere (volume scattering). Surface scattering is particularly intricate at C-band because the Bragg waves responsible for  the radar backscattering at this radar frequency lie in the transition region, where the impinging raindrops can increase (usually) or decrease the backscattered radar power, and also because volume scattering and attenuation can contribute significantly to the radar signature when the rain rate is high.

In this paper we report about progress that has been made in our study of C-band radar signatures of rain over the ocean. Such studies are relevant also for retrieving sea surface wind fields from C-band scatterometer data. Rain is a main source of error in wind retrieval algorithms, especially when co- and cross-polarized scatterometer data are used, which will be the case in the future. In this study we have analyzed mainly Sentinel-1 SAR images acquired over the South China Sea and have compared them with rain data from the weather radar of the Hong Kong Observatory and from the Global Precipitation Measurement (GPM) mission. In contrast to previously analyzed ERS and Envisat data, the Sentinel-1 data are acquired at VV and VH polarization which allows us to better discriminate between surface and volume scattering. The results of this investigation will be presented, which include also statements on how the radar signatures depend on rain rate, rain type, and wind speed, and how to discriminate radar signatures of rain from radar signatures of “look-alikes”.

During the summer months, from June to September, the Baltic Sea experiences blooms of cyanobacteria (blue green algae) that have several different effects of the ecosystem. Some of the cyanobacteria are toxic. During calm conditions surface accumulations of the cyanobacteria are formed, these are a nuisance, e.g. to tourism. The Swedish Meteorological and Hydrological Institute, SMHI, operates a service named the Baltic Algae Watch System with the aim to observe the spatial extent of blooms and to predict drift patterns. Optical satellite images are primarily used for these products but optical sensors cannot penetrate clouds, and during cloudy conditions gaps are left in the products. Synthetic Aperture Radar (SAR) images are not impeded by clouds, though the radar signal cannot penetrate the ocean surface and therefore only the surface blooms may be observed in these images. Algae blooms observed in SAR images are seen as low backscatter areas and are known to be an oil spill look-alike. Hence, improved information about algae blooms in SAR images will not only be useful in separating them from oil slicks but may also be used as a complementary data source for operational algae watch products.

Here we compare algae blooms observed in spatially and temporally overlapping SAR images and optical images and validate the results with in-situ observations. In July and August 2017 algae blooms in the Baltic Sea were recorded in SAR images from Sentinel-1 and Radarsat-2 as well as optical Sentinel-3 OLCI and Aqua-MODIS images. The Sentinel-1 data is ScanSAR IW images and the Radarsat-2 images are fully polarimetric images. The Radarsat-2 images were collected to overlap in time and space with in-situ measurements. The biomass of algae was estimated as chlorophyll a and also based on microscopy. Algorithms developed by Kahru for the identification of cyanobacteria blooms from optical images were used to separate the blooms into surface and subsurface blooms. In the Sentinel-3 OLCI images and Aqua-MODIS images, we separate the surface and subsurface slicks and compare them with the corresponding Sentinel-1 SAR backscatter values and the damping ratio. The high temporal resolution of both Sentinel-1 and -3 enables us to observe the seasonal evolution of the algae bloom and to investigate the type of algae bloom captured in the SAR images. The high resolution fully polarimetric Radarsat-2 images are used to study the algae blooms. We investigate how the outline of the algae blooms in the SAR images overlap with those observed in the optical images using polarimetric parameters such as the polarization difference and the co-polarized cross-product.