Conference Agenda

Anthropogenic processes and natural hazards exacerbate risks in coastal zones and megacities. Coastal risk is influenced by the combination of sea level rise (SLR), resulting from climate change, associated tidal evolution, and the local land sinking. Moreover, there is an increasing concern about the growing urbanization of the world’s low-lying coastal regions responsible for associated coastal hazards (e.g., flooding in built areas). In this context, the ESA-DRAGON V GREENISH project (https://dragon5.esa.int/projects/, [1]) provided extensive research and development analyses of areas in Europe and China subject to climate change induced (e.g., Sea Level Rise, 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. The main project goals were: i) Studying the ground deformation in coastal/deltaic regions with conventional and novel interferometric SAR approaches; ii) Monitor changes in urbanized areas via coherent and incoherent change detection analyses; iii) Studying interactions between ocean currents and coasts, such as coastal erosion, using high resolution optical and SAR satellite images; iv) Assessing SLR, tidal evolution, and hydrogeological risks in urban coastal areas; and finally v) Training Young Scientists.A number of planned activities have been achieved, and some of further analysis related to new improvements have been carried out. The main results will be presented at the final meeting scheduled for June 2024.

Specifically, we are going to present the main relevant outcomes concerning the following studies:
1) Analysis of long-term land deformation and flood risks of coastal regions:
In this study, ground displacement time series of Shanghai and its coastal region, spanning 2018 to 2021, were derived by applying the SBAS technique to two independent sets of SAR images collected by the X-band COSMO-SkyMed (descending orbits) and the C-band European Copernicus Sentinel-1 (ascending orbits) sensors. The east-west and up-down deformation time series, calculated for every coherent point common to both SAR datasets, were retrieved using the minimum acceleration multi-track InSAR method. To study the flooding risk over the selected area, several simulations using the LISFLOOD-FP were performed. Long-term ground deformations of Chongming Island over the last decade were also investigated using ALOS-1, RADARSAT-2, and Sentinel-1 SAR satellite datasets collected from 2007 to 2020.
2) Use of SAR synthetic parameters for the evaluation of risks due to differential movements of buildings:
In this study case we preliminary processed an archive of Sentinel-1A SAR data collected over the Shanghai region to retrieve the ground deformation time-series at single-look scale [2]. Then, we tested the validity of some InSAR based descriptors of potential risk conditions [3], which are mainly linked to the differential movements across single buildings/infrastructures [4]. In particular, these indices, correlate the damage risk of the infrastructure to their geometric features and to their measured rate of deformation [4]. The final goal, is to obtain some insights on the hazard conditions in order to mitigate possible heavy impacts from climate change phenomena affecting the urban areas [5].
3) InSAR ground displacement time-series quality assessment:
In this study, we are going to test a new statistical framework for ground displacement time-series quality assessment, enhancing the major role played by inaccuracies due to time-inconsistent phase-unwrapping (PhU) errors and decorrelation phenomena. Interferometric data processing will be conducted using multigrid InSAR algorithms (e.g., [6]) in order to directly analyze the InSAR ground displacement products and their precisions at the single-look scale. Accordingly, we aim to provide the displacement measurements with a synthetic indicator of the goodness of those estimates.
4) Coastal megacity geohazard analyses using Persistent Scatterer Interferometry (PSI):
In this context, our investigation focuses on two regions within the Istanbul coastal megacity, utilizing the PSI method, employing data from Sentinel-1 and ALOS-2. The first region under scrutiny is the Golden Horn, a natural estuary susceptible to deformation owing to human-induced coastline filling. The second area examined is the Avcilar-Beylikduzu region, prone to gradual landslides. Over the period between 2015 and 2020, displacement rates of approximately 20 mm/year and 10 mm/year in the Line of Sight (LOS) direction were identified along the coast of the Golden Horn and the landslide-prone region, respectively.
5) Spaceborne SAR methodologies for sea surface current estimation:
The study focuses on the use of SAR data for the analysis of marine phenomena influencing the sea surface. Specifically, the estimation of sea surface currents using the DCA method [7], is conducted, showcasing its capability to retrieve sea surface velocity information from SAR measurements.
REFERENCES:
[1] Q. Zhao et al., «Innovative remote sensing methodologies and applications in coastal and marine environments», Geo-spatial Information Science, vol. 0, fasc. 0, pp. 1–18, set. 2023, doi: 10.1080/10095020.2023.2244006.
[2] R. Lanari, O. Mora, M. Manunta, J. J. Mallorqui, P. Berardino, e E. Sansosti, «A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms», IEEE Transactions on Geoscience and Remote Sensing, vol. 42, fasc. 7, pp. 1377–1386, lug. 2004, doi: 10.1109/TGRS.2004.828196.
[3] F. Pratesi, D. Tapete, G. Terenzi, C. Del Ventisette, e S. Moretti, «Rating health and stability of engineering structures via classification indexes of InSAR Persistent Scatterers», International Journal of Applied Earth Observation and Geoinformation, vol. 40, pp. 81–90, ago. 2015, doi: 10.1016/j.jag.2015.04.012.
[4] V. Macchiarulo, P. Milillo, M. J. DeJong, J. González Martí, J. Sánchez, e G. Giardina, «Integrated InSAR monitoring and structural assessment of tunnelling‐induced building deformations», Structural Contr & Hlth, vol. 28, fasc. 9, set. 2021, doi: 10.1002/stc.2781.
[5] L. O. Ohenhen e M. Shirzaei, «Land Subsidence Hazard and Building Collapse Risk in the Coastal City of Lagos, West Africa», Earth’s Future, vol. 10, fasc. 12, p. e2022EF003219, dic. 2022, doi: 10.1029/2022EF003219.
[6] M. D. Pritt, «Phase unwrapping by means of multigrid techniques for interferometric SAR», IEEE Transactions on Geoscience and Remote Sensing, vol. 34, fasc. 3, pp. 728–738, mag. 1996, doi: 10.1109/36.499752.
[7] V. Zamparelli, F. De Santi, A. Cucco, S. Zecchetto, G. De Carolis, e G. Fornaro, «Surface Currents Derived from SAR Doppler Processing: An Analysis over the Naples Coastal Region in South Italy», Journal of Marine Science and Engineering, vol. 8, fasc. 3, Art. fasc. 3, mar. 2020, doi: 10.3390/jmse8030203.

Thanks to the rapid development of artificial intelligence, new types of wave remote sensing data are obtained from the learning of the data, such as the along-track maximum wave height (MaxH) and wide swath significant wave height(SWH). Those findings enriched our observation of ocean waves and also benefit the assimilation of such data in operational wave models.

MaxH is extremely important for the safety of maritime activities but rarely observed. A machine learning method of obtaining the MaxH from CFOSAT Surface Waves Investigation and Monitoring (SWIM) Level 2 parameters is presented and shows good agreement with buoy MaxH observation. This first demonstration of such MaxH opens a step forward achievement for rogue wave forecast at global scale.

Another new products called “wide swath” significant wave height (SWH) is obtained base on the synchronous observations of SWIM and Scatterometer (SCAT) by deep learning. The wide swath SWH extends the spatial coverage of wave remote sensing and also benefit the assimilation. More than 4 years of SWH data on the HY2 wide swath have been developed by an artificial intelligence model using neural networks. The assessment is performed to study the impact of the combined assimilation of wide swath SWH and directional spectra of Sentinel-1 and CFOSAT. The analysis also focuses on wave climate variability in critical ocean regions such as the Marginal Ice Zone (MIZ) and the Southern Ocean. The work consists in performing combined assimilation tests with the MFWAM wave model and a control test without assimilation. Validation of model results is developed mainly by comparison with independent altimetry data and drifting buoy data from field campaigns. The results show a significant improvement of scatter index of SWH when using combined assimilation of wide swath SWH and directional wave spectra. The increase in data with recent scatterometer missions such as HY2C and HY2D enhances the impact of assimilation, particularly in the case of storms and cyclones in the Pacific and Indian Oceans. We examined the impact of combined assimilation on the wave/ice interactions used for polar oceans. Comparison with buoys in the Arctic Ocean shows a better estimate of SWH under the ice, which improves the impact of waves on the upper ocean layers.