The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. CMEMS serves a wide range of users (more than 15,000 users are now registered to the service) and applications. Observations are a fundamental pillar of the CMEMS value-added chain that goes from observation to information and users. Observations are used by CMEMS Thematic Assembly Centres (TACs) to derive high-level data products and by CMEMS Monitoring and Forecasting Centres (MFCs) to validate and constrain their global and regional ocean analysis and forecasting systems. This paper presents an overview of CMEMS, its evolution, and how the value of in situ and satellite observations is increased through the generation of high-level products ready to be used by downstream applications and services. The complementary nature of satellite and in situ observations is highlighted. Long-term perspectives for the development of CMEMS are described and implications for the evolution of the in situ and satellite observing systems are outlined. Results from Observing System Evaluations (OSEs) and Observing System Simulation Experiments (OSSEs) illustrate the high dependencies of CMEMS systems on observations. Finally future CMEMS requirements for both satellite and in situ observations are detailed.

Sea ice concentration (SIC) is an important sea ice parameter for sea ice navigation, environmental research, and weather and ice forecasting. We have developed and tested a method for estimation of the Baltic Sea SIC using SENTINEL-1 synthetic aperture radar (SAR) and Advanced Microwave Scanning Radiometer 2 passive microwave radiometer (MWR) data. Here, we present the method and results for January 2016. Ice concentration grids of Finnish Meteorological Institute daily ice charts have been used as reference data in this paper. We present a comparison of four SIC estimation methods with our reference data. In addition to the combined SAR/MWR SIC estimation method, we also compare SIC estimates produced using SAR alone and two MWR-based methods. The main target of this paper was to develop and test a high-resolution SIC estimation method suitable for operational use.

We have studied the incidence angle (θ0) dependence of the sea ice backscattering coefficient (0.°) for Sentinel-1 (S-1) extra wide (EW) mode dualpolarization (HH/HV) synthetic aperture radar (SAR) imagery acquired over the Kara Sea under winter and summer melting conditions. The determination of the 0.° versus θ ₀ dependence was based on SAR image pairs acquired on ascending and descending orbits over the same sea ice area with a short time difference. The SAR noise floor was subtracted from the HV images. From the image pairs 1.1 by 1.1 km windows representing level first-year ice (LFYI) and deformed first-year ice (DFYI) were manually selected, and a linear regression was fit between the resulting 0.° and θ0 differences of the windows to estimate the slope b ₁ (dB/1°) between 0.° and θ0. For example, under winter condition b ₁ for DFYI at HHand HV-polarizations was found to be -0.24 and -0.16 dB/1°, respectively, and b ₁ for LFYI at HH-polarization was -0.25 dB/1°. It was not possible to determine a reliable b ₁ for LFYI at HV due to a contamination effect of the S-1 noise floor. The b ₁ values at HH compared well with previous studies. They can be used to compensate the 0.° incidence angle variation in the S-1 EW SAR images with good accuracy. The HH b ₁ values are applicable to other S-1 imaging modes and other C-band SAR sensors like RADARSAT-2. Unfortunately, the HV b ₁ values are specific to the S-1 EW mode due to the noise floor problem.

High-resolution ice concentration information is required for navigation purposes, validating ice models, and data assimilation. The currently available operational ice concentration products are based on microwave radiometer data, and their typical resolution is several kilometers. We present an algorithm for the estimation of ice concentration based on dual-polarized (HH/HV) C-band synthetic aperture radar (SAR) data. The algorithm is based on the multilayer perceptron (MLP) neural network. Ice concentration estimated based on the HH channel is used as one MLP input, and the local incidence angle is used as another. The additional inputs are based on the HV channel. Digitized Finnish Ice Service ice charts, which were also used as the training data, the SAR-based HH-channel ice concentration, and the ice concentration based on a radiometer algorithm are used as reference data sets. The results for the dual-polarized algorithm show improvement compared to the algorithm based on HH-polarized SAR data only.

We propose an unsupervised method for iceberg detection over sea ice-free waters. The algorithm is based on the segmentation and nonparametric constant false alarm rate (SnP-CFAR) approach. Unlike in parametric CFAR detection, in our method, there is no need to define target, guard, and background areas explicitly. Instead, we apply the CFAR detection to the pixels within each detected segment and the background is formed of the nearby pixels not included in the target segment. By using nonparametric background probability density function (PDF) estimates, we also eliminate the need of assuming a specific type of a background PDF. We compared the detection results with the operational Danish Meteorological Institute (DMI) Gamma-CFAR algorithm results. The results were evaluated against icebergs manually identified by the Finnish Meteorological Institute (FMI) Ice analysts. Our method also exhibits a reduced number of false alarms. We present results of iceberg detection based on the SAR channel-cross-correlation (CCC). CCC was able to distinguish many of the true targets with a low number of false alarms. However, CCC seems to miss some of the true targets and its main use would be in confirming iceberg observations.

In the 4th Ice Analyst Workshop 2014, ice analysts made ice concentration (IC) analysis based on two synthetic aperture radar (SAR) images over the Gulf of Bothnia located in the northern Baltic sea. The ice analysts were divided into five work groups and each group assigned ICs to predefined polygons. This exercise gave us a good opportunity to compare the analysis performed by the work groups with the results produced by our dualpol-SAR IC estimation algorithm. The ice analyzes were performed and the algorithm was run in a resolution of 100 m. For reference, we also included the polygon median values produced by the ARTIST sea ice (ASI) radiometer-based algorithm producing the ICs in a 3.125-km grid. This kind of comparisons are very important to evaluate the automated algorithms with respect to the human interpretation. Human interpretation available in the ice charts is currently considered as the best available IC information.