Bathymetry is the measurement of water depths. Traditional scientific approaches for measuring water depths includes the collection of in-situ measurements and the underlying cost of field campaigns pose serious difficulties in acquiring such information on a global scale, routinely. Although detailed bathymetric maps can be obtained, only limited areas can be actually surveyed and often no more than once. Thus, apart from the reduced coverage, the dynamics of the changing seafloor, especially along coastal regions where major part of human activities take place, are not well documented. The reasons why space-borne remote-sensing techniques play an essential role in retrieving near-shore bathymetry are threefold. Firstly, space-borne imagery makes it possible to access remote areas with wide coverage at high spatial resolution. Secondly, as space-borne imagery are acquired on a regular basis, a historical data archive from various sensors is available for most areas, which in turns enables scientists to access information from the past. Thirdly, the cost of the data is relatively affordable compared to airborne or ground-based campaigns. While several Earth Observation missions exist, the Copernicus Sentinel-2, operated by the European Space Agency (ESA), is a game changer. The Sentinel-2 mission comprises twin polar-orbiting satellites in the same orbit, phased at 180°. The satellite constellation carries optical instrument payload (MSI, Multi Spectral Imager) that sample 13 spectral bands. The major advantages of Sentinel-2 mission is its systematic monitoring over wide orbital swath width of 290 km, coupled with high revisit time (5 days at the equator and 2-3 days at mid-latitudes) and high spatial resolution (four multispectral bands at 10m). In this context, an innovative and robust method to retrieve maps of the coastal bathymetry from quasi simultaneous Sentinel-2 imagery is being developed. Our methodology allows to measure directly both swell celerities (c) and swell wavelengths () from a satellite optical dataset, where c and  are jointly used to retrieve the depth of the coastal seafloor, robustly.
Swell celerity describes the phase velocities of superimposed ocean waves, each of which has a particular wavelength (i. e. the distance between two successive crests). As swell velocity and wavelength are controlled by bathymetry, quantifying these parameters from space allows for robust retrieval of water depths independently of water turbidity, which usually hampers our capacity for robust measurements or limits the maximum reachable water depths. Furthermore, it is the Sentinel-2 mission characteristics of unpreceded spatial coverage, short temporal repeatability and systematic acquisitions at sufficient resolution, which pave the way for advanced scientific developments. The combination of the above mentioned specifications, an innovative scalable approach and the availability of systematically acquired and archived satellite data, would allow for routine bathymetric mapping of shallow-depth coastal areas at global scale. The method preferably applies to the zone between the coast and an area of depth less than or equal to half the wavelength of the waves (typically up to a hundred meters deep), with the exception of the wave breaking zone. Thus, it extends considerably our previous capability for monitoring coastal regions’ dynamics.

The mapping and monitoring of intertidal bathymetry is of high importance to all coastal activities, including – but not limited to – coastal protection works, storm surge modelling and safety of navigation. Ship or airborne mapping is cost, time and labor intensive, which has led to an increase need for alternative methods.
Satellite-Derived Bathymetry (SDB) describes the methods of using optical, multi-and hyper- spectral imagery to derive shallow water depth down to the light penetration depth. Both, technology of Satellite-Derived Bathymetry workflows and satellite sensors, have significantly increased over the last decade. Thus, the objective of this oral presentation is, where Satellite-Derived Bathymetry technologies stand now. We will focus on the technology advances of modern physical inversion methods in contrast to the early, empirical SDB approaches. We demonstrate modern analysis on adjacency correction and coupled retrieval of atmosphere and IOPs, which results in a high degree of operational data processing, standardization and finally high quality of the bathymetric data. It will be demonstrated by validation exercises against acoustic bathymetric survey data. We showcase results from three projects: (1) MARSAT, the German national funded project to foster the use of space application in the maritime industry, in which SDB is being used to update nautical charts and create ready to use electronical nautical charts (ENCs). These charts bring benefits to all insufficient charted shallow water areas – which represents more than half of the world’s shallow water zone. (2) Belize Coastal Zone Management projects, funded by the Inter-American Development Bank, where SDB data – together with other EO data on coastline, benthic and turbidity - have been a key information layer for defining coastal zone strategies to reduce erosion risk of vulnerable coastlines. (3) EMODnet Bathymetry, the European portal for harmonized bathymetric data formed by about 50 European hydrographic entities, where SDB has been used to update coastal bathymetric data gaps. Based on these findings, we will draw conclusions on where SDB methods find its place for mapping and monitoring of coastal waters nowadays, and which future developments with respect to standards, data analysis, future sensors and capacity building strategies are required to improve the SDB performance to fill the demand of the coastal and offshore users.

Storms over the Baltic Sea and Europe have an enormous impact on the marine habitat. Among other, extremes in wave climate are a potential threat to the population, offshore industry, coastal infrastructure, and shipping. The knowledge of long-term changes in the extreme events in sea wave heights which can be caused by climate change and variability is critical for assessment of flooding risks and coastal protection. The extreme events analysis of wind waves in the Baltic Sea region is currently not very well addressed, as well as satellite observations of the wave heights. Here we discuss the study of multi-mission satellite altimetry data over the Baltic Sea Basin regarding extremes in the wave heights.

The satellite altimetry data for the Baltic Sea region collected up to date about 700 000 measurements, extend for more than 24 years (1991 - 2017) and provide an excellent spatial coverage over the Baltic Sea, allowing to study in details trends in extreme wave heights and their spatial variations. The data were cross-validated with all available regional in-situ wave measurement stations, corrected for biases, and checked for possible erroneous measurements due to moderate and high ice concentration and complex coastline and archipelagos outline.

In this talk for the first time, we present an analysis of 30-yr and 100-yr return periods derived from fitted Weibull and generalized Pareto distributions in significant wave heights in the Baltic Sea measured by the multi-mission satellite altimetry. The results are based on an application of the Annual Maxima method, Initial Distribution, and Peak-Over-Threshold approaches to the satellite altimetry data. Each technique was validated in comparison with in-situ measurements from regional Baltic Sea stations, following the same extreme value analysis method for both satellite data and in-situ data. The validation showed a good correspondence between the resulted return values derived from the satellite altimetry and in-situ data. Interestingly, the results showed that in case of the regional seas, such as the Baltic Sea, the best results obtained for retrieving correct extreme value distribution parameters and return values are for small grid sizes of ~0.3 degrees. When the grid size is increased, the return values derived from the satellite altimetry show more significant discrepancy with the in-situ-derived return values. This result indicates that the commonly used for the global studies 2x2 degrees grid sizes can produce erroneous results for the regional seas applications.

Here we show that the 30-yr and 100-yr return periods of wave heights reveal significant gradients over the Baltic Sea, indicating a decrease in the southern part of the Baltic Sea and an increase in adjacent areas, which can significantly affect coast vulnerability. An application of non-stationary modeling of extreme value distribution also confirmed significant linear trends in return values over the Baltic Sea. We will also discuss a relation between the observed linear trends in wave height extremes and their correlation with the NAO and AO climatic indeces using non-stationary extreme value modelling techniques.

Coastal zones are rapidly changing due to climate change and human activity. These changes need to be monitored frequently in aim to observe the impact on the water quality and ecosystems. These observations are complicated to achieve frequently in space and time with traditional in situ data, therefore the dynamics cannot be seen. New generation Sentinel series satellites S2 and S3 with corresponding MSI and OLCI sensors, developed by European Space Agency as part of the Copernicus Programme, offer very high spatial and temporal resolution and thus permit highly frequent monitoring.

The standard algorithms perform well in optically clear waters where satellite signal is mostly influenced by optically active substances (OAS) as phytoplankton (CHL) and its’ degradation products, colored dissolved organic matter (CDOM) and suspended particulate matter (SPM) in good correlation with CHL. The standard algorithms don’t perform well in optically complex waters where SPM, CDOM and CHL are present in different fractions and have multiple sources, thus act independently. Therefore, it is important to study in detail the properties of OAS and their influence on satellite signal.

In the current study, the focus is on SPM. Pärnu Bay (Baltic Sea) SPM resuspension has three sources: the Pärnu River inflow, waves and coastal erosion. Firstly, the SPM concentration dynamics were described on the 2017-2018 S3 OLCI C2RCC processed cloud and ice-free images with a new approach using SPM algorithms based on optical water types.

Secondly, the particle properties like particle size distribution (PSD) and backscattering ratio (ratio between the particulate backscattering and total particulate scattering) influencing highly the remote sensing signal were also analysed. The backscattering ratio is mostly used as a constant in remote sensing algorithms because its’ component of particulate backscattering is difficult to measure. However, the data of Pärnu Bay showed high variability of this ratio in space (between 0.01 and 0.04 in measured stations) and time and its’ dependence on wavelength, indicating that it cannot be used as a constant in remote sensing algorithms. The high variability is due to its’ dependence on the PSD that was measured for the first time in Pärnu Bay by LISST-100X in August 2018. The results showed high variability in PSD and the particles of all the size classes between 2.5 and 250 micrometers existed in the surface layer and whole water column. One part of the particles were flocs of agglomerated fine unique particles, that influence also backscattering thus the remote sensing signal differently from fine separated particles. The linkage between PSD and backscattering was described by the beam attenuation exponent, backscattering exponent, bulk index of refraction and a power law (Junge-like) distribution and this information could be used in remote sensing in the future to retrieve PSD from satellite data.

The current use of passive microwave (PMW) observations for sea surface temperature (SST) and sea ice is presently very limited in the Baltic Sea due to the contamination from land and from sea ice during winter. At the same time, persistent cloud cover can exist for weeks, preventing SST observations to be made by the Infrared (IR) satellites.
This presentation will discuss the challenges in the present day satellite observing system from the perspective of the operational Copernicus Marine Environment Service (CMEMS) SST product for the Baltic Sea. The cloud and coastal limitations in the current IR and PMW satellites will be presented and the impact of using SST observations from the Copernicus Imaging Microwave Radiometer (CIMR) high priority candidate mission will be assessed.
The CIMR satellite mission will, if it is selected to be build provide daily coverage, sea ice concentration with a spatial resolution better than 5 km and SST better than 15 km. In addition, reduced side lobe contamination will facilitate the use of these observations in coastal regions and in regions close to sea ice. These characteristics will make the CIMR PMW SST observations very suitable for a coastal region like the Baltic Sea and this study discusses the benefits of the CIMR observations to the CMEMS L4 SST analysis through the use of realistic IR observations and simulated CIMR SST observations. The impact has been determined through an analysis of the expected coverage of the CIMR observations relative to the current satellite constellation. In addition, the improvements in the CMEMS SST analysis has been determined through an experiment with simulated CIMR observations. The conclusions from the study clearly demonstrates the benefits of CIMR that could make a unique contribution to the existing satellite SST constellation in order to get an improved CMEMS SST analysis for the Baltic Sea.

Littoral regions encompass complex, unique, small-scale turbulent features such as atmospheric gravity waves, boundary layer rolls, etc. The goal of the Coastal Land Air Sea Interaction (CLASI) project is to utilize in-situ and satellite measurements to better understand air-sea interaction phenomena and to improve the Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS) forecasts of coastal features in littoral regions such as Monterey Bay, CA. Improving model forecasts is beneficial for naval operations such as ship maneuvering, forecasts and trafficability. Coastal wind fields and small-scale turbulent features, in particular, change rapidly due to interactions with topography, wind fronts, breaking waves in the surf zone, etc. and can, therefore, be difficult to resolve and forecast by models. Synthetic aperture radar (SAR) is useful for obtaining high-resolution coastal wind fields, which we derive for Monterey Bay using an X-band SAR wind retrieval algorithm. SAR-derived wind fields resolve atmospheric boundary rolls, Kelvin-Helmholtz waves, and wind fronts that are not present in COAMPS wind field results. Knowledge of these coastal turbulent features' temporal and spatial scaling is critical for significant model improvement. Therefore, the question "What are the small-scale turbulence structures from the coast to the open ocean?" needs to be answered. Wavelet analysis is a valuable tool for analyzing variations of power within a temporal or spatial series. Through the segmentation of a temporal or spatial series in wavelet space, either time-scale (usually frequency or period) or space-scale (usually wavenumber or wavelength), respectively, one can identify the dominant scales of non-stationary, or non-periodic, fluctuations in the temporal or spatial series and where these fluctuations occur in time or space. The traditional Fourier transform analysis method lacks the ability to identify the time or position, shape, or size of the fluctuations in a temporal or spatial series and is better suited for stationary, or periodic analysis. The current literature lacks a cohesive composite of identification, extraction, and analysis techniques for turbulence structures, particularly scales, from both temporal and spatial data sets through wavelet analysis. Therefore, the goal of this presentation is for wavelet analysis applications to time and spatial series, utilizing coastal tower and SAR-derived wind fields from the CLASI dataset as example series. Wavelet analysis enables us to combine the SAR-derived and coastal tower wind fields to retrieve consistent information on spatial and temporal scales of the wind variations in coastal environments, such as Monterey Bay.