Conference Agenda

SADyMAS is designed as a continuation of the successful collaborative DRAGON 5 project ReSCCoME (“Remote Sensing of Changing Coastal Marine Environments”), which was coordinated by the same European and Chinese PIs and which focussed on the development of new algorithms for the analysis of multi-mission SAR data. SADyMAS will continue along the same lines through an adaptation of those algorithms to additional sensors, expanded regions of interest, and extended temporal coverages.

SADyMaS consists of three research packages (RP), each addressing a relevant aspect of small-scale ocean dynamics: the state of vulnerable coastal regions and their changes (addressed in the RP on “Intertidal Regions”), the spatio-temporal distribution of sub-mesoscale oceanic dynamics (“Sub-mesoscale Eddies”), and the generation and lifetime of oceanic fronts on continental shelves (“Oceanic Fronts”). The project consortium is formed by internationally renowned experts in each research field.

To ensure a high degree of cross-fertilization and synergy effects among the partners, cross-cutting themes have been identified, the synergism of EO data, support of Young Scientists, and dissemination and outreach. Responsibilities for each RP and cross-cutting theme are equally distributed among all partners.

The project partners have identified marginal seas in Europe (North and Mediterranean Sea), China (South China Sea) and the North Atlantic (Labrador Sea). These marginal seas host areas of interest, of which large quantities of EO data will be analysed, with specific focus on several research topics that are specific for the respective regions.

Intertidal regions are particularly sensitive to natural and anthropogenic hazards. RP “Intertidal Regions” will aim to optimize their monitoring by including multi-modal SAR data into existing monitoring schemes that are based on optical EO data and in-situ observations. Where possible, in-situ and airborne campaigns (deploying optical/IR sensors carried by an UAV) will provide complementary information that is needed for a full understanding of the observed SAR image features.

Sub-mesoscale oceanic eddies in marginal seas are important for the mixing of the upper ocean layer and the transport and distribution of material, including marine pollutants. The RP “Sub-mesoscale eddies” will focus on the detection and identification of multi-scale eddies on SAR imagery and on their spatio-temporal variation and coverage. Complete eddy features and associated parameters will be retrieved by means of AI techniques and comparisons with numerical simulations.

Frontal systems, be they of oceanic origin or driven by atmospheric processes, are of fundamental importance for a full assessment of the complex dynamic processes in coastal and marginal seas. The RP “Oceanic fronts” will address these features and how their detectability on SAR and optical/IR imagery depends on oceanic and atmospheric conditions.

The PREDICTOR project, which extends the GREENISH project (ID. 58351), aims to provide analyses with novel and traditional synthetic aperture radar (SAR) technologies applied to coastal zones (i.e., the Mediterranean basin and the Eastern Chinese Coast) subjected to natural (e.g., sea level rise, flooding) or anthropogenic (e.g., ground subsidence, coastal deterioration, etc.) disasters.

PREDICTOR will provide analyses over coastal areas to quantify the combined risks due to the SLR, urban growth, coastal land deterioration/erosion, urban and coastal ground deformations, and other natural and anthropogenic phenomena, by also exploiting freely available sets of InSAR data/products through the European Copernicus Services [1] and by upscaling some analyses over China with ad-hoc experiments. One of the principal goals of this project is to exploit multi-polarized/multi-satellite SAR data to develop some new SAR-based methods, also assisted by AI, to enhance our capability to detect and analyze changes occurring in coastal regions, which are due to the intricate interactions between ocean (sea) and coast. At the same time, the project wants to develop and apply new indicators based on SAR data that could characterize/quantify the different condition risks to which the population is exposed in high-urbanized coastal cities (thus extending the work already started during the Dragon 3, 4, and 5 projects by the same team).

More specifically,tThe PREDICTOR project will conceptualize and develop innovative SAR methods based on AI algorithms, which can help semi-automatically extract historical and up-to-date information on the vulnerability to disasters of selected areas of interest, with a specific focus on coastal hazards. One of the most critical applications of RS technologies concerns detecting and monitoring changes occurring on Earth’s surface using multi-temporal RS images [2–4]. In this context, optical RS sensors have extensively been used for addressing change detection (CD), with a variety of heterogeneous applications [5-8]. Unlike optical sensors, SAR sensors have less been exploited for CD purposes [9–12]. Specifically, a way to identify “changed areas” quickly and efficiently is to generate a series of synthetic change detection indices (CDIs) using SAR images with both incoherent (i.e., amplitude) and coherent (i.e., phase) information [13-14].

Moreover, PREDICTOR will extend the work done within the D4 and D5 projects for the study of inter-relations between SLR and coastal ground displacements by testing the validity of the adopted models and searching for new, improved models.

A specific analysis will also be carried out to recover the spatial distribution of surface currents of internal water bodies (lakes) [15].

Finally, an innovative research line concerns the design of improved InSAR processing chains to estimate soil moisture and water content, complementing the methods based on the exclusive use of SAR amplitude information, using multi-polarized/multi-looked interferograms. Among them, a class of methods relies on the complete exploitation of wrapped InSAR phase triplets [16]. They possess interesting statistical properties, and among those are insensible to the time-conservative phase contributions, such as the deformation, topographic errors, atmospheric nuisance, and others. Furthermore, these phase triplets have nonclosure residuals. However, the contribution that prevents the closure of the triplets can also be profitably used to detect another physical parameter (soil moisture, wet biomass content, etc.) with innovative processing chains. Some scholars have estimates soil moisture from these phase triplets [17-18]. However, the expected results are still unsatisfactory, and there is room in the PREDICTOR project for further analyses.

References

[1] https://land.copernicus.eu/en/products/european-ground-motion-service

[2] Hansen et al., doi:10.1016/j.rse.2011.08.024.

[3] Bruzzone et al., doi: 10.1109/36.843009.

[4] Zhu et al., doi: 10.1016/j.isprsjprs.2017.06.013.

[5] Brunner et al., doi: 10.1109/TGRS.2009.2038274.

[6] Tapete and Cigna, doi: 10.3390/rs10040561

[7] Yang et al., doi: 10.1117/1.JRS.8.083639

[8] Hafner et al., doi:10.1109/LGRS.2021.3119856.

[9] Rignot and Vanzyl, doi: 10.1109/36.239913.

[10] Pirrone et al., doi: 10.1109/IGARSS.2017.8128171.

[11] Bovolo and Bruzzone, doi: 10.1109/TGRS.2005.857987.

[12] Moser and Serpico, doi: 10.1109/TGRS.2006.876288.

[13] Mastro et al., doi:10.3390/rs14143323.

[14] Ito and Hosokawa, doi: 10.1109/IGARSS.2002.1026802.

[15] Amadori, M. et al. 2021, DOI: 10.3390/rs13122293.

[16] Falabella and Pepe, doi: 10.1109/TGRS.2022.3216083.

[17] De Zan et al., doi: 10.1109/TGRS.2013.2241069.

[18] S. Zwieback et al., doi: 10.1109/TGRS.2017.2702099.

The project is to propose novel and effective methods to generate marine added-value products starting from remotely sensed measurements mainly coming from satellite radar sensors. Hence, the proposed piece of research is framed into the “Ocean & coastal zone” Dragon-6 thematic area. The various sub-topics addressed within this domain include the understanding of “marine dynamic environment”, the analysis of “sea surface characteristics”, the Earth Observation (EO) support for “marine disasters” management, the exploitation of radar measurements to observe “algae and phytoplankton” blooms. These applications are of paramount importance for both the scientific and the end-user communities especially in the management of coastal areas. The proposed added-value products are generated mainly from Synthetic Aperture Radar (SAR) satellite measurements (but include also microwave radiometer and scattereometer and ancillary optical measurements) and are devoted to open sea and coastal environments, even under extreme weather conditions.

The project relies on a deep co-operation between Chinese and European (including Italy, UK and Spain) partners that call for a complementary expertise. This co-operation started with Dragon-3 and has been reinforced up to the Dragon-5 project no. 57979.

Moreover, master and PhD students will actively take part in the modelling and analysis of the phenomena under study, i.e. coastal water pollution, coastal erosion, in-land water body observation, metallic target detection, typhoon/cyclone monitoring, observation of algae, microplastic aggregation monitoring, etc., as well as in the development of effective and reliable algorithms for the generation of added-value products from remotely sensed measurements. The project also aims at stimulating the use of complementary microwave satellite instruments, including scatterometers and radiometers on-board of operational and planned missions operated by ESA, ESA TPM and Chinese EO.

The proposed piece of research will involve the development of tailored models of the processes under study combined with Artificial Intelligence (AI) methodologies that allow the interpretation and the processing of SAR measurements, collected under different imaging modes (including measurements acquired using SAR conventional and compact- polarimetric modes), to derive the above-mentioned user friendly added-value products. The latter include, but are not limited to, maps of targets at sea (including aquacultures, ships, wind farms, algae and aggregated of plastics) 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.

In summary, the project will address the following main assets:

a) Promoting an "intelligent", i.e., physically based, exploitation of SAR measurements to generate end-user friendly added-values marine products.

b) Developing new AI-based models/methods to deal with the synergistic exploitation of microwave satellite measurements to address marine key issues for coastal area monitoring.

c) Boosting the co-operation between Chinese and European partners by taking full advantage of their respective expertise, including the training of Young Scientists.

Project’s outcomes will be disseminated through publications related to the Dragon 6 symposia at mid-term and kick-off stages, as well as annual progress reports on the status of the projects will be provided during Dragon 6 symposia. The main achievements will be also presented at dedicated ESA-/NRSCC-sponsored workshops and international symposia and will be submitted on peer-reviewed journals and refereed conferences.

The proposed project is financially backed on both European and Chinese funds.

Objective and methods:

With the increasing availability of different medium and high-resolution remote sensing data, the fusion of multiple data sources to generate high-temporal-spatial- resolution data for grassland monitoring has been widely used, among which the fusion of Landsat 8 with Sentinel-2 and MODIS is the most common. Data fusion processors have been developed in the EO community for a longer time already. Now, Harmonized Landsat Sentinel-2(HLS) could be download from the Earthdata Search and Land Processes Distributed Active Archive Center. However, there is currently limited research that considers fusing Chinese high-resolution data(such as GF-1 and GF-6 WFV) with foreign medium and high-resolution data(Such as Sentinel-2 and Landsat) and does not introduce low spatial resolution data information.

Sandy land is one of the results of land desertification, which refers to land mostly covered by sand or sandy soil (sand-covered land), including desert. Knowledge of the spatial distribution and variation in sandy land is important to better understand desertification processes, land resource management, and environmental research. The emissivity of sandy soil in the TIR bands has remarkable characteristics. In addition, the green transition increasing attention is paid to the thermal insulation efficiency of building. Several thermal mission can support related information needs such as Landsat TIRS, Ecostress and at coarser resolution also Sentinel-3. The upcoming Copernicus LSTM mission will provide systematic global TIR observations at high spatial resolution. Certainly, synergies between such mission need to be explored. Here the Chinese SDG-Sat mission is highly relevant. It provides 30m TIR observation yet over a small spatial footprint. Therefore, complementary use need to be explored and potential for fusion these sources need be assessed.

Based on the background above, the objectives of this proposal are to focus on the following aspects:

• Evaluate complementary EO data sources from Chinese and European missions in terms of their characteristics for integrated or fused applications. Special focus will be paid to Sentinel2 and Geofen6. Also thermal complementarities between SDG Sat and other LST data sources will be explored.
• Explore the value of different harmonization and fusion processors such as STARFM, sen2like and others.
• Perform a prototype implementation of a fusion processor in a cloud environment
• Evaluate fused data stream in different application contexts

The deliverables include：

(1) Image Processing Algorithms: A prototype of a fusion processor will be developed and transformed into an cloud based on demand service prototype.

(2) Geospatial Products: Fused China-EU EO data of study area. Spatial distribution of heat emission of study area. Spatial distribution of sandy land in Xilin Gol League, China

(3) Technical Reports: Comprehensive technical reports detailing the methods, processes, and techniques used in the project.

(4) Scientific Publications: 3-5 research papers or scientific publications presenting the project's findings, methodologies, and significant results.

(5) Young scientist training: 3-5 young scientist exchange and training