Conference Agenda

Coastal marine environments, being invaluable ecosystems and host to many species, are under increasing pressure caused by anthropogenic impacts such as, among others, growing economic use, coastline changes and recreational activities. A continuous monitoring of those environments is of key importance for the identification of natural and manmade hazards, for an understanding of oceanic and atmospheric coastal processes, and eventually for a sustainable use of those vulnerable areas.

The project “Remote Sensing of Changing Coastal Marine Environments” (ReSCCoME) addresses research and development activities that focus on the way, in which the rapidly increasing amount of high-resolution EO data can be used for the surveillance of marine coastal environments, and how EO sensors can detect and quantify processes and phenomena that are crucial for the local fauna and flora, for coastal residents and local authorities.

ReSSCoME is organized into five research packages (RP), each addressing a relevant aspect of changing coastal marine environments: the state of vulnerable coastal regions and their changes (addressed in the RP on intertidal regions and coastline changes), the impact of growing economic use on coastal environments (offshore wind farms and oil pollution), and the growing threat of plastic debris and green tides (coastal pollution).

Intertidal regions are particularly sensitive to natural and anthropogenic hazards. Hence, RP ‘Intertidal regions’ focuses on an optimization of the monitoring of those regions by including multi-modal SAR data into existing monitoring schemes that are based on optical EO data and in-situ observations.

China and Northern Europe are hot-spots for actual and future developments of offshore wind energy. As such, RP ‘Offshore wind farms’ will provide information on wind resources, wake effects and environmental impacts, which are needed by wind energy industries during the entire lifecycle of a wind farm.

The detection and quantification of marine oil pollution and the identification of its sources are crucial for the pollution monitoring in coastal marine waters. RP ‘Offshore oil pollution’ addresses these tasks through a synoptical use of EO data and the automated processing of large quantities of SAR data (Big Data).

Floating marine litter is a global problem, with millions of plastic items ending up in the sea. In addition, harmful algal blooms such as green tides are posing a threat to coastal marine environments. RP ‘Coastal pollution’ addresses both aspects and will help in both optimizing the detection and quantification of marine litter, and understanding the dynamics of green tides.

Finally, coastlines are changing rapidly worldwide as a result of both (quasi-) natural and anthropogenic pressures. RP ‘Coastline changes’ will demonstrate the use of EO data for an accurate long-term quantification of coastline changes, which is needed by coastal managers for a sustainable development of coastal environments.

The project consortium is formed by internationally renowned experts in each of the research fields. In order to ensure a high degree of cross-fertilization and synergy effects among the partners, five cross-cutting themes were identified, the synergism of EO data, handling and processing of Big Data, identification of coastal stress factors, support of Young Scientists, and dissemination and outreach. Responsibilities for each RP and cross-cutting theme are equally distributed among all partners.

The partner affiliations are based on, or close to, five European (Norwegian, North, Baltic, Black, and Mediterranean Sea) and three Chinese marginal seas (Bohai, Yellow and South China Sea). These marginal seas host five areas of interest, of which large quantities of EO data are being analysed, and in which complementing in-situ campaigns will be run. In addition, the western Java Sea will serve as a test and validation area for newly developed algorithms.

This study provides the first-year progress advancements achieved within the framework of the ESA-MOST Dragon-5 project ID 57979. The latter aims at demonstrating the benefits of radar products for coastal area monitoring. Within this context, improving the understanding of the marine environment, advancing the analysis of sea surface properties and boosting the exploitation of EO satellites for the management of marine disasters is addressed. In detail, the study provides innovative added-value products to observe coastal areas characterized by harsh environments, even under extreme weather conditions, by means of multi-polarization and multi-frequency Synthetic Aperture Radar (SAR) satellite imagery together with complementary microwave satellite instruments as scatterometers and radiometers on-board of operational and planned missions operated by ESA, ESA TPM and Chinese EO.

The main phenomena to be investigated include coastal water pollution, coastal erosion, in-land water body observation, metallic target detection and typhoon/cyclone monitoring. The proposed piece of research is focused on the development of tailored models combined with AI methodologies that allow the interpretation and the processing of polarimetric SAR measurements,collected under different imaging modes. As a result, user-friendly outputs are generated that include detection maps of metallic targets as aquacultures, ships and wind farms, wetland coastal erosion/accretion trends due to both anthropogenic and natural phenomena, mapping marine pollutants, modelling, tracking and forecasting extreme weather events as cyclones/typhoons.

As a first-year progress, the following articles were published on peer-reviewed international journals under the framework of the ESA-MOST Dragon-5 project ID 57979:

• T. Meng, X. Yang, K.-S. Chen, F. Nunziata, D. Xie and A. Buono, “Radar Backscattering Over Sea Surface Oil Emulsions: Simulation and Observation," IEEE Transactions on Geoscience and Remote Sensing, in print., 2021.
• V. Corcione, A. Buono, F. Nunziata and M. Migliaccio, “A Sensitivity Analysis on the Spectral Signatures of Low-backscattering Sea Areas in Sentinel-1 SAR Images," MDPI Remote Sensing, no. 13, pp. 1183, 2021.
• E. Ferrentino, A. Buono, F. Nunziata, A. Marino and M. Migliaccio, “On the use of multi-polarization satellite SAR data for coastline extraction in harsh coastal environments: the case of Solway Firth," IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, vol. 14, pp. 249-257, 2021.

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Our aquatic environments are critical for our wellbeing as well as providing vital resources for established maritime industries (fisheries, coastal tourism, energy and mineral production, boat building, shipping and ports activity) and emerging new industries of the Blue Economy (aquaculture, marine renewable energy, bio-products (pharmaceutical and agrichemicals), sea-bed mineral exploration and extraction, Blue Carbon (carbon storage in mangroves, seagrass and saltmarsh) and desalination. The mounting and conflicting pressures from the number of users and uses, coupled with population growth, industrialisation, land use intensification and climate change bring into focus the urgent need for the sustainable management of our aquatic resources and space.

The increasing availability of satellite data from Copernicus Sentinels (Sentinel-3) and Chinese Earth observation missions (e.g. from HY-1) has radically transformed the approaches to monitor and sustainably manage coastal, inland and transitional systems and has stimulated the development of innovative products. This project aims to develop and validate innovative products for transitional and coastal waters to support and improve the water ecosystem services, sustainable management and security.

The optical diversity that characterise many of these aquatic systems often challenges the applicability of EO approaches. As a first step, we investigated the optical diversity in coastal and transitional systems. We used in-situ bio-optical data collected between 2015 and 2019 in coastal waters around Europe. We compared the hyperspectral data with optical water types previously suggested for inland and coastal waters and proposed new frameworks for applications in coastal waters. This information is important for the applicability of retrieval algorithms in coastal and transitional waters. We also investigated the performance of different atmospheric correction models (e.g. Polymer, C2RCC, iCOR for Sentinel-3 OLCI and Acolite, C2RCC, Polymer, Sen2Cor) over coastal waters.

During the reporting period we worked on the development of Harmful Algal Blooms detection algorithms from Sentinel-3 OLCI data. This included a preliminary study of the detection of Pseudo-nitzschia blooms in Galician coast (NW Spain). We employed support vector machines (SVM) and in-situ data of Pseudo-nitzschia abundances to develop a set of novel algorithms for the detection of this diatom in the area. The models showed a robust performance in the independent test data set.

Ongoing work is focused on the testing of algorithms for the retrieval of phytoplankton size classes (PSC) in coastal and transitional systems. A dataset of in-situ fractionated chlorophyll-a data from water bodies around Europe was put together to test algorithms based on pigment analysis, chlorophyll a concentrations and phytoplankton absorption. We will present some initial findings from this benchmarking and will show ways of calibrating existing PSC global algorithms for operational use in local circumstances for Sentinel-3 data. Future work will include testing the detection capabilities of the PSC product using coincident datasets of in-situ Apparent optical properties (AOPs), Inherent Optical Properties (IOPs) and phytoplankton characteristics.

Water in oil (WO) and oil in water (OW) emulsions from marine oil spills have different physical properties, volume concentrations, and spectral characteristics. Identification and quantification of these different types of oil emulsions are important for oil spill response and post-spill assessment. Based on image statistics, we proposed here a decision tree method to classify oil type, and oil quantification is further attempted, with results partially validated through spectral analysis and spatial coherence test. The numerical mixing experiments using AVIRIS pixels further indicate that the SWIR bands might be used to develop linear unmixing models in the future once the coarse-resolution oiled pixels are first classified to WO and OW types, and 1295 nm is the optimal wavelength to perform spectral unmixing of mixed coarse-resolution pixels.

We applied MODIS surface reflectance data to analyse the temporal and spatial distribution characteristics of water clarity (Zsd) in the Jiaozhou Bay, Yellow Sea from 2000 to 2018. Zsd retrieval models were regionally optimized using in-situ data with coincident MODIS images, and then were used to retrieve the Zsd products in Jiaozhou Bay from 2000–2018. The analysis of the Zsd results suggests that the spatial distribution of relative Zsd spatial characteristics in Jiaozhou Bay was stable, being higher Zsd in the southeast and a lower Zsd in the northwest. The annual mean Zsd in Jiaozhou Bay showed a significant upward trend, with an annual increase of approximately 0.02 m. Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Zsd in Jiaozhou Bay, respectively.

Coastal zones are essential for the socio-economic well-being of many nations. Coastal regions, which are the location of large population centres, have multiple uses, needs and opportunities, and are particularly exposed to extreme events and climate change. Many key sectors are affected by long-term effects in these zones, such as the monitoring of public/private infrastructures, cultural/natural heritage preservation, risk management, and agriculture. The combined effects of sea level rise (SLR), tidal evolution, modulated ocean currents and extreme events can have numerous impacts to coastal, river delta, and inland water zones, including water management, which in turn lead to cascading and unpredictable impacts on other sectors.

The GREENISH project aims to provide extensive research and development analyses of areas in Europe and China subject to climate change induced (e.g., SLR, flooding, and urban climate threats) and anthropogenic disasters (e.g., ground subsidence over reclaimed-land platforms), with the goal to improve the knowledge and develop new remote-sensing methods. Of great relevance is a detailed understanding of the combined risk of SLR, tidal evolution, storm surges, and ground subsidence in coastal areas and lake-river systems. Global sea-level is rising, and tides are also changing worldwide and these risks are accompanied by increasing concerns about the growing urbanization of the world’s low-lying coastal regions and related coastal hazards (e.g., flooding). Inland water bodies such as lake and river system also experience substantial degradation with rapid economic development. The use of optical, SAR, InSAR, and hyper-spectral data products will be fostered. Some selected case-study areas have been identified, including the Yangtze and Pearl river deltas, Poyang Lake, the Bohai Rim Region (China), the city of Istanbul (Turkey), the Po river delta and the Venice Lagoon (Italy). Flood hazards will be investigated by using satellite SAR and altimeter data, tide gauge data, and by developing proper hydrodynamic models.

The main goal of the project is the well-use of Earth Observation (EO) data and in-situ monitoring information, to detect the long-term evolution of coastal, deltaic and lake-river systems. More specifically, the project aims:

- To study the ground deformation in coastal/deltaic regions with conventional and novel interferometric SAR approaches.

- To monitor changes of urbanized areas via coherent and incoherent change detection analyses.

- To study interactions between ocean currents and coasts, such as coastal erosion, using high resolution optical and SAR satellite images.

- To assess SLR, tidal evolution, and hydrogeological risks in urban coastal areas.

- To study the interactions between Poyang Lake and its connecting rivers.

- To study atmosphere/surface interactions and develop atmospheric phase screen correction methods in multi-temporal SAR images.

- To develop methods to integrate satellite- and ground-based RADAR systems to monitor public infrastructures in Shanghai

- To develop interactive maps of coastal, urban, and inland zones susceptible to primary and secondary risks via GIS.

Use of Earth Observation data represents the key asset of the project and expected results will contribute to further developments and analyses in the years to come, from both the theoretical and experiemtal point of view.