Conference Agenda

The post-2000 Global Biodiversity Conservation (GBC) Framework of the United Nations requires countries to report on targets that include species or ecosystem-specific biodiversity status information. The GBC and other political frameworks require that localized biodiversity patterns and the establishment of biodiversity status baselines are considered. The GBC also mandates governments to report on baseline biodiversity status information. Here, we introduce an easy to implement and spatial explicit biodiversity indicator that relies on readily available earth observation (EO) data and insect-species data from citizen science. Using Uganda as an example, we show how the EO-based indicator can be implemented, with scaling ramifications for its use across Africa. Insects, specifically lepidopterans, including butterflies and moths, are used as indicator species for overall ecosystem intactness and biodiversity. Losses in insect diversity and abundance also significantly affects ecosystem function and ecosystem services, specifically through pollination, a key determinant for agro-ecological systems productivity and ultimately food security. Insect occurrences from the iNaturalist platform were predicted using spectral features from Corpenicus Sentinel-2 timeline data, 25-meter tree height metrics, from the Global Ecosystem Dynamics Investigation (GEDI) missions’ data, and 4-km climate variables. The climate variables (temperature and rainfall regimes) were sourced from TerraClimate. Using random forest (RF) machine learning and the EO-based and climate predictors, insect species (lepidopterans) diversity and richness was predicted over Uganda. The RF model results depict the biotic suitability of the species (assemblages). The lepidopterans diversity/richness outputs were augmented with the 1-km Global Human Footprints data, to render insect-based biodiversity status and ecosystem vulnerability patterns over Uganda. This work provides valuable insights that can inform conservation strategies and policies aimed at addressing ecosystem vulnerability across Africa. Emphasizing the crucial role played by ESA Copernicus data, the procedure and results support informed decision-making for ecosystems protection, amid a climate and biodiversity crisis.

With Planetary Biology, EMBL is joining forces to measure, understand, and apply the molecular processes of life on a planetary scale (Earth).

Spanning multiple ecosystems of our planet Earth, the Planetary Biology research theme aims to understand, from the molecular to the population level, how microbes, plants, and animals respond to each other and to their environment. Having precise environmental measurements are paramount for these efforts. To achieve those goals, EMBL will combine direct investigation of diverse planetary ecosystems from field studies with the controlled laboratory research on experimental model systems.

In practice, EMBL started this year the expedition Traversing European Coastlines (TREC) across 120 land-sea sampling sites and engaging with 29 countries. To do so, it developed Mobile Laboratory that brings cutting-edge technology and methodology to the field to enable unprecedented experimental standardisation, high-end fresh sample processing, and data integration.

The EU-funded, EMBL-coordinated, project BIOcean5D is a joint effort of 31 partners across Europe, synergizing with the TREC activities in the sampling and data gathering activities.

It is EMBL’s ambition that current and future projects will produce new knowledge and discoveries that will help to provide our societies, governments, and regulatory agencies with the ability to best predict the possible effects of environmental changes and impacts.

Many parameters of the marine physical environment of the Arctic are rapidly changing. Especially in coastal areas the ongoing cryosphere loss has profound effects on shallow subtidal habitats and communities, including shifts in distribution of seaweed biomass and species composition along the depth gradient. The objective of this study was to assess whether and, if so, by how much biomass, total abundance, and taxon composition of the seaweed-associated fauna had changed concomitantly. In Kongsfjorden, Svalbard, the seaweed-associated fauna at Hansneset has been sampled at 2.5, 5, 10, and 15 m in 1996/98, 2012/13, and 2021. Taxonomic composition differed considerably between 1996/98 and 2012/13, while it remained similar to the latter in 2021. Taxonomic composition varied also with depth and this effect was independent on year of observation. The fauna biomass increased with depth between 2.5 and 15 m in 1996/98. Contrarily, it decreased with depth in 2012/13. In 2021, peak biomass was found at 5 m depth, while the remaining depths showed similar values of about 30-50% less. Overall, biomass and abundance of the seaweed-associated fauna increased about twofold, on average, between 2012/13 and 2021, returning to 1996/98 values. While mainly cirripeds caused this increase in biomass, the biomass of bryozoans decreased from 2012/13 to 2021.

Pixel-to-pixel spectral variation is often treated as a biodiversity indicator. However, due to the lack of comprehensive data this approach could not be validated yet to a large extent.

We present a systematic test of the spectral variation hypothesis (SVH) in temperate grasslands. Grassland simulations based on field data and radiative transfer models to mimic the spectral behavior of the communities were used to bridge the data lack.

We observe that spectral diversity is mainly driven by spatial resolution. Species richness is not necessarily linked to optical trait diversity. Therefore, the basic assumption of the SVH is not met in all habitat types.

This study clearly underlines the context-dependency of the SVH and we point out that, although of promising value for distinct ecosystems, it is not universally applicable. We call out for strong collaboration between remote sensing experts and ecologists to improve the reliability of future biodiversity monitoring missions.

The structural complexity of forests is considered a reliable and repeatable indicator of biodiversity, as well as providing a detailed picture on forest degradation and restoration. The link between structural diversity and habitat provision has been previously studied using in-situ measurements, national forest inventories and local remote sensing techniques such as airborne laser scanning (ALS). These methods lack either the spatial resolution or the scalability and replicability to cover large regions and compare structures in different forests. In recent years, with the increasing amount of data collected by the LiDAR mission GEDI, a global dataset of estimated structural metrics has been collected. GEDI L2A and L2B data provide full waveform data from which the following metrics can be extracted: relative height, plant area index, cover, plant area vegetation density and foliage height diversity, among others. However, this data by itself is still not providing a detailed picture of structural diversity at scale due to its discrete sampling nature.

In this study, GEDI L2A and L2B data in combination with the Spanish National Forest Inventory (NFI-4) is used to build a large dataset of calibration and validation of structural metrics that provide a picture on the structural complexity of forests and tree-covered regions. This dataset is then used to train Neural Network models using a complex suite of Earth Observation products and bioclimatic variables, to obtain the most important sources for structural diversity monitoring and provide continuous maps at high resolution. These maps can then be correlated with biodiversity datasets to investigate the degree of importance of each structural variable when it comes to metrics such as species richness, occurrence or abundance.

The European Space Agency (ESA) activity “Biodiversity+ Precursors” is a contribution to the joint EC-ESA Earth System Science Initiative to advance ESS and its response to global challenges. The Precursor BIOMONDO focuses on biodiversity in freshwater ecosystems. Based on analysis of scientific and policy priorities, the main knowledge gaps and challenges in biodiversity monitoring, drivers of change and requirements related to EBV workflows were compared to possibilities offered by Earth Observation (EO). These findings were the basis for the development of innovative integrated earth science solutions that combine EO based products, biodiversity modelling (Delft3D, Heat Tolerance and River Connectivity) and in situ data using advanced data science and information technology. All datasets were compiled in analysis ready data cubes that can be accesses through the BIOMONDO Freshwater Laboratory. Machine Learning (ML) techniques were used to analyse time series of data generating Thematic Ecosystem Change Indices (TECIs) that show data anomalies, such as change in habitat conditions, water quality, lake water temperature, and land cover land use (LCLU) in the catchment. The validation of the integrated products and possibilities for upscaling of the methodology are key tasks within BIOMONDO and beyond and interested biodiversity experts have been given access to the novel BIOMONDO Freshwater Laboratory, to evaluate the impact and benefit of the developments. Results from three Pilots at sites in Europe and Asia focusing on three different topics - eutrophication, heatwaves and connectivity - are presented.

Due to human-induced climate change, wildfires are increasing in intensity, frequency and duration around the world in recent years. Wildfires kill and displace people, damage property and infrastructure, burn vegetation, threat biodiversity, increase CO2 emission and pollution, and cost billions to fight. Therefore, near real-time wildfire monitoring is of critical importance to support effective emergency response and management.

SAR4Wildfire is a project funded by the European Space Agency’s EO Science for Society Program. SAR, capable of penetrating clouds and smoke and imaging day and night, is an essential data source for wildfire monitoring when optical sensors are hindered by dense smoke or cloud cover or darkness. The overall objective of this research is to evaluate Sentinel-1 SAR time series and a deep learning for near real-time wildfire progression monitoring.

The KTH team has developed several deep learning frameworks for near-real time wildfire monitoring exploiting Sentinel-1 SAR dense time series, and tested the methods in various wildfire sites around the world (an example is shown in Fig. 1). The results have demonstrated that Sentinel-1 SAR is able to detect burned areas effectively (e.g, Ban et al. 2021, Zhang et al, 2021, Hu et al., 2022, Zhang et al, 2023 and Zhao and Ban, 2023), and thus has the potential to play an important role in near real-time wildfire monitoring when SAR data are readily available on a daily basis.

Ban, Y., Zhang, P., Nascetti, A., Bevington, A. R., Wulder, M. A., 2020. Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning. Nature Scientific Reports, 10(1), 1–15.

Hu, X., P. Zhang, Y. Ban, M. Rahnemoonfar. 2023. GAN-based SAR and optical image translation for wildfire impact assessment using multi-source remote sensing data. Remote Sensing of Environment, Volume 289, 113522. https://doi.org/10.1016/j.rse.2023.113522.

Zhang, P., Y. Ban, and A. Nascetti. 2021. Learning U-Net without Forgetting for Near Real-Time Wildfire Monitoring by the Fusion of SAR and Optical Time Series. Remote Sensing of Environment, Vol. 261, 112467. https://doi.org/10.1016/j.rse.2021.112467.

Zhang, P., Y. Ban, A. Nascetti. 2023. Total-variation regularized U-Net for wildfire burned area mapping based on Sentinel-1 C-Band SAR backscattering data. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 203, pp 301-313, https://doi.org/10.1016/j.isprsjprs.2023.07.024.

Zhao, Y. and Y. Ban. 2023. Assessment of L-Band and C-Band SAR for

Burned Area Mapping in Various Biomes Using Deep Learning. IEEE Transaction on Geoscience and Remote Sensing (submitted).

High-resolution monitoring of rivers is important because rivers are severely affected by climate change and frequency/magnitude of extreme events are changing fast. Advanced in-situ monitoring technologies have to be combined with satellite earth observation (EO) to obtain accurate, reliable and spatio-temporally resolved information for effective decision support, risk assessment, climate change adaptation investment analysis, and operational forecasting/surveillance.

Traditional hydrometric monitoring of rivers is in-situ and station-based. In-situ monitoring networks lack spatial resolution, have been declining in many regions, and data accessibility is increasingly restricted because of growing conflicts between countries over water resources allocation. In order to solve this problem, hydrometric monitoring using satellite earth observation has to be combined with drone-borne hydrometric monitoring technology for validation, deployment in remote and inaccessible regions, and to enable reliable and accurate river discharge estimation.

The Horizon Europe UAWOS project (https://uawos.dtu.dk/) develops an unmanned airborne water observing system providing key hydrometric variables (bathymetry, velocimetry, water surface elevation) at high spatial resolution/coverage, and data-based products/services supporting management and decision making. UAWOS integrates airborne data streams with Copernicus water bodies and water level services for cross validation and to estimate river discharge from satellite EO data.

This contribution will outline the UAWOS work programme and report initial results of UAS hydrometry surveys in Sweden, using (i) radar altimetry for water surface elevation mapping, (ii) water penetrating radar and sonar for bathymetric mapping and Doppler radar for surface velocity monitoring. The combination of these datasets for river discharge estimation as well as for validation and enhancement of satellite altimetry datasets will be discussed.

Due to the anthropogenic impact on the climate system, the necessity of understanding and monitoring atmospheric physics has grown dramatically. Understanding wind and temperature patterns in the atmosphere is critical and highly needed for improving both climate models and numerical weather prediction models. Currently, there is a significant data gap for continuous observations above a height of 5 km, the maximum height of commercial compact wind radars and lidars. The purpose of EULIAA is to create a lidar array capable of detecting atmospheric wind and temperature autonomously from 5 km to 50 km on a 24/7 basis.

EULIAA's lidar units are designed to be compact, efficient, and autonomous. EULIAA will generate novel and innovative data sets in near real-time for integration into European databases Copernicus and GEOSS, filling present data gaps and assisting in the monitoring of the consequences of climate change and evaluating climate protection actions. The basic technology was demonstrated in a field campaign, using an infrared lidar system and is now being transferred to a European industrial partner. The enhanced capability developed within EULIAA will be demonstrated and validated in difficult-to-reach regions (polar, equatorial, and mountain). Based on a high target TRL (6-8), a roadmap for a European lidar array will be developed in collaboration with relevant industrial, standardization, and end-user actors. An extended array of EULIAA’s lidar units will provide unique satellite cal/val capabilities. The EULIAA project brings together 7 partners from 5 countries who are experts in lidar and its subsystems, atmospheric observatories, and atmospheric data providers.

Finland is affected by climate warming that takes place more rapidly at higher latitudes compared to the global mean and increases the risks of multiple hazards in Finland’s forests (Venäläinen et al. 2020). In this work, we focus on three hazards to forests: drought, forest fires and forest pests. In a multi-hazard risk framework, we combine quantitative analysis of satellite-based data, modelling, drought forecasting and climate scenario applications. The hazards are analyzed independently, but an initial attempt is also presented to explore the interconnections between hazards by analyzing their correlations as well as coinciding and consecutive events of multiple hazards. The quantitative assessment is complemented by a qualitative analysis of risk interpretation and management which is co-developed with stakeholders. The approaches and use of satellite-based information are different for the three hazards. Droughts were assessed using a new version of the Watershed Simulation and Forecasting System (WSFS-P) from the Finnish Environment Institute (Syke), that was evaluated with soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) mission. The WSFS-P was developed to produce sub-seasonal drought forecasts based on standardized drought indices. Pest mass occurrences were predicted using new statistical models for different moth species for which remotely sensed vegetation phenology (green-up date) was used as explanatory variable. The Canadian Fire Weather Index (FWI) was used to construct impact response surfaces combined with CMIP6-based probabilistic projections of climate change (Fronzek et al. 2022) for the assessment of forest fire risks. Here, we present the framework and results for the three hazards and the use of satellite-observations in their analysis. We show initial steps for the analysis of intercorrelations and combined effects of the individual hazards. Further work will extend the analysis with the FWI to incorporate fuel load information and apply the IRS approach also to droughts and pest risks.

This work was conducted as part of the EIFFEL project funded through the European Union’s Horizon 2020 research and innovation programme under Grant Agreement Νο 101003518.

References:

Fronzek, S., Honda, Y., Ito, A., Nunes, J.P., Pirttioja, N., Räisänen, J., Takahashi, K., Terämä, E., Yoshikawa, M. and Carter, T.R., 2022. Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces. Climate Risk Management, 38:100466, doi:10.1016/j.crm.2022.100466

Venäläinen, A., Lehtonen, I., Laapas, M., Ruosteenoja, K., Tikkanen, O.P., Viiri, H., Ikonen, V.P. and Peltola, H., 2020. Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Global Change Biology, 26(8):4178-4196

Current dynamical seasonal prediction systems present limited skill in the extratropics mainly due to the weak seasonal predictability but also because of the limited representation in the physical models of key processes such as large-scale teleconnections, land-atmosphere feedbacks and realistic initial conditions. The AI4DROUGHT project aims to enhance our understanding and predictive capabilities of summer droughts in Europe at seasonal timescale by the combination of climate models, Earth Observation (EO) and artificial intelligence methods. A hybrid approach is proposed relying on existing state of the art dynamical predictions plus the statistical relations extracted from 2 main sources of predictability; The large-scale modes of variability, which contributes to the onset or termination of droughts, are empirically learned from thousands years of climate simulations and fine tuned later with observational time series. Land-atmosphere feedbacks, critical for the amplification of the former large-scale signal, are mostly driven by local conditions represented by satellite-based variables like soil moisture and vegetation indices. Additionally, eXplainable AI (XAI) techniques are used in order to provide interpretation of the relations learned by the model such as the regionally diverse importance of the different drivers leading to drought in Europe. Finally a causality analysis at shorter time scales (daily/weekly) complements this study to better understand the onset and evolution of drought conditions and their potential cascading effects, i.e. heat waves and wildfires.

The European Space Agency’s (ESA) EO4MULTIHA (High-Impact Multi-Hazards Science), a two-year project contributing to the joint ESA European Commission Earth System Science Initiative, kicked off in September 2023.

EO4MULTIHA capitalizes on the latest advances in satellite Earth Observation technology, including the Copernicus Sentinels series, the ESA’s Earth Explorers, and the meteorological missions to better understand the drivers and dynamics leading to high impact cascading and compounding multi-hazard events, and to improve the estimation of the impacts on society and ecosystems.

The project will develop four science cases, tackling both compound and cascading events, along with the corresponding demonstration cases aiming to derive actionable information from the scientific developments. The outcomes will be part of an open multi-hazard events database designed to facilitate collaborative research and future scientific progress.

Science Cases (SC) 1 and 2 investigate the effects of climate-related extreme events in the Adige River catchment. SC1 focuses on hot/dry events on the Alpine mountainous region, where raising temperatures and lack of snowfalls cause hydrological impacts that compound with heatwaves and wildfires. SC2 evaluates the impact of climate-related extreme events on the middle-lower course of the river: data driven tools will be implemented to describe the interactions between climate-related hazards, coastal hazards such as sea level rise and saltwater intrusion, anthropogenic land use, and water quantity and quality parameters. SC3 is located in the Southeast region of the UK where the impacts of hot/dry compound in a scenario of sustained high temperatures and their effects on the stability of the terrain and geologically driven events will be evaluated. SC4 focuses on the small island developing State of Dominica to evaluate the multi-hazard scenario mainly from a wet compound and volcanic perspective (i.e., successive storms, landslides, volcanic hazards, and cross-border issues) using digital twins and advanced modelling.

The demonstration cases prioritize the following key aspects: the analysis of multi-hazard interactions and vulnerability, the generalizability, and the ability to handle spatial and temporal dependencies and the assessment of impact chains. The exposure and vulnerability analysis provides crucial information for effective adaptation planning and serves as a valuable resource for decision-makers seeking actionable insights. Generalizability refers to the ability of the proposed methods to be easily applicable to various settings with similar characteristics, ensuring that the insights gained can be widely utilized. Finally, the proposed methods should be capable of considering interactions between hazards, exposure, vulnerability and impacts in space and time.

Precipitation is a key variable of earth observation systems, involved in a wide spectrum of applications (e.g., water resources and early warning systems for flood/drought events). During the last decade, significant research effort has been put towards the development of satellite-based precipitation products (SPPs), however, their deployment in real-world applications has not yet reached the full potential, despite their ever-growing availability, spatiotemporal coverage and resolution. This may be associated with the reluctancy of end-users to employ SPPs, either worrying about uncertainty and biases inherited in SPPs or even due to the existence of multiple SPPs, whose performance fluctuates across the globe, and thus making it difficult to select the most appropriate SPP (some sort of a choice paradox). To provide a remedy to this issue, this work presents a framework that couples machine learning and explainability techniques, along with uncertainty quantification methods. Specifically, we couple: a) ensemble (tree-based) learning algorithms to integrate multiple satellite-based observations (i.e., both precipitation and soil moisture products), b) global and local explainable AI techniques to provide explanations at the model and instance level, respectively, and c) a copula-based approach to quantify the predictive uncertainty of the final integrated product. The proposed framework is showcased on the basis of a benchmark dataset consisted of 1009 cells/locations around the world (Europe, USA, Australia and India), highlighting its robustness as well as its application capability which are independent of specific climatic regimes and local peculiarities.

The United Nations (UN) 2030 Agenda aims at promoting sustainable development at environmental, social and economic level. The definition of the Sustainable Development Goals (SDGs) and of the associated Global Indicator Framework represent a data-driven effort, helping countries in evidence-based decision-making and policies. SDG indicators’ monitoring and reporting across countries can benefit from substantial use of Earth Observation (EO), including satellite and in-situ networks, and of their processing through data analytics and numerical modeling approaches, making the 2030 Agenda implementation robust, viable and faster, both technically and financially.

This poster introduces SDGs-EYES, a major new European initiative aiming at boosting the European capacity for monitoring the UN SDGs. SDGs-EYES addresses current gaps in the UN SDGs monitoring by exploiting data and information coming from the European Copernicus Programme.

The SDGs-EYES project aims to design, implement and deploy a Copernicus-based Service which enhances the usability of EO data offered by Copernicus services and associated Data and Information Access Services, in order to build, access and visualise indicators for monitoring SDGs targets.

The SDGs-EYES Service will permit building of Copernicus-driven indicators based on scientific algorithms and rigorous validation for the SDG monitoring overcoming actual observational and information gaps. The approach will encompass existing indicators’ datasets as well as both state-of-the-art and renewed/improved methods for comparison/evaluation purposes, to provide insights on the level of improvements, assessed via a new grading/tiering system.

The SDGs-EYES scientific approach will be demonstrated with particular reference to three interconnected SDGs, specifically on climate (SDG13), ocean (SDG14) and land (SDG15). These SDGs are mostly focused on the biosphere as foundation of prosperity, development and co-benefits in the society and economy, but also relevant due to their nexus with additional SDGs, targets and indicators related to socio-economic and (geo)political factors (e.g., human health, environmental crimes, water and food insecurity, poverty, conflicts, displacements, migrations). Five Pilots (encompassing EU and extra-EU regions) will be used to demonstrate and validate the SDGs-EYES approach and results, that is application-oriented scientific products, technological solutions and user-tailored services.

Finally, the SDGs-EYES Service will facilitate both understanding and uptake of indicators information by stakeholders (e.g protection agencies, territorial planners, policy makers, investors) that can efficiently incorporate data and tools in their decision-making process, increasing their capability through a scientific and evidence-based rigorous approach, overcoming challenges in accessing and using EO for SGD indicators’ monitoring and reporting.

There is high confidence that the exacerbation of heatwaves can be attributed to climate change. Heatwaves are becoming more intense, occurring more frequently, and lasting longer, and they do not spare the Sahel, a region characterized by a hot and semi-arid climate and projected to be particularly vulnerable to heatwaves. Since populations in the Sahel are primarily relying on climate-sensitive sectors such as agriculture, existing literature, and public discourse around climate change in the Sahel predominantly focus on rainfall variability with less attention given to the causes and consequences of extreme heat.

While there is emerging literature on Sahelian heatwaves processes and an increasing number of studies using climate models or reanalysis data to understand the underlying processes of heat stress exposure in the African continent, few studies focused on extreme heat in the Sahel region. Since there is a clear lack of in-situ data to study past and climate trends of climate extremes in this region, satellite earth observations data provide a unique opportunity to study such trends. As such, our overall goal is to investigate if ESA-Climate Change Initiative’s (ESA-CCI) Land surface Temperature (LST) products provide consistent information on climatic trends of heat extremes by constructing satellite-based heat extremes indices from well-documented heat indices used by the climate science community.

We compare these heat extreme indices built from LST-CCI All Microwave datasets to the ones computed from ERA5 reanalysis in the Sahel region. We focus on the year 2010, during which LST-CCI data reveal that it was the first time that highest temperatures on land persisted for two months consecutively in the Sahel. We compute monthly Thom’s Discomfort Heat Stress Index (DI_Thoms), the monthly anomalies of Maximum day temperature (TX) and Number of Hot days (NHD) for the year 2010 with respect to the 1996-2020 baseline. Our first findings show that LST-built heat indices and reanalysis-built (ERA5) indices exhibit similar trends during the heatwave event, albeit with different amplitudes. We found that the heatwave of 2010 was particularly intense with extreme heat stress exposure over the Sahel during April and May but with more variations of risk levels in the LST-built index.

Our work presents an important evaluation of heat extremes metrics computed from satellite observations and how they compare to reanalysis data. Furthermore, we explore how current and future high-resolution EO derived data products could be used to better monitor heatwaves and improve early warning systems of heat stress.

REWRITE ambition is to expand innovative approaches and nature-based solutions for rewilding seascapes constituted by intertidal soft sediment (ISS), bridging biodiversity conservation, climate adaptation and social expectations. This ambition is served by an impressively interdisciplinary consortium (25 partners from the academic and private sectors, representing 8 European tidal coastal states, as well as the UK, Canada and the USA) with recognized expertise on the climate-biodiversity nexus, fostering synergies among disciplines such as Social Sciences and Humanities, natural sciences and resources, and ecosystems management.

To reach its ambition, the strength of REWRITE is the “space for time” approach based on 10 demonstrators from Northern to Southern Europe, and from North America to Europe, illustrating a wide panel of environmental constraints, societal uses, coastal management and stakeholder’s engagement. Coupling remote sensing, modelling and ground-truthing approaches, REWRITE will perform a joint analysis from natural and social sciences to understand the historical and current trajectories of ISS functioning and project the future. This approach offers a strong basis to co-develop robust scenarios using multivariable constraints, including plural and integrated (i.e. environmental, economic and societal) cost valuations, in order to select the best and low-cost options to rewild a resilient European coastline.

Climate change is amplifying the frequency and intensity of extreme weather events [Field et al., 2012]. Of particular interest are large-scale heat and drought compound events, caused by the anomalous persistence of dry/warm air masses in high-pressure systems that deplete the moisture in an area. These compounds lead to profound instantaneous and long-term impacts on the environment and society. The ESA DeepExtremes project (https://rsc4earth.de/project/deepextremes) aims to better forecast and understand such events by developing advanced AI methods on a large database of Sentinel-2 time series. AI algorithms [Camps-Valls, et al., 2021] are trained to forecast future reflectances, which are ultimately analyzed by applying Explainable AI (XAI) [Samek, et al., 2020].

In this work, we present our findings after applying and analyzing a Convolutional Long Short-Term Memory neural network, an architecture specialized in spatio-temporal data analysis. The network was trained for predicting future Sentinel-2 RGB-NIR reflectances by using past reflectances, land cover and elevation maps, as well as meteorological variables. We then used XAI attribution methods such as InputXGradient, GradientSHAP, and IntegratedGradients, which assign an importance score to the variables at the input of the model, given a predicted output.

We feature some illustrative examples where the explanations are especially insightful, as well as a more systematic analysis of several large events unseen during training. One notable challenge is the very high dimensionality of the attribution data (an 8-dimensional tensor). To make sense of this data, several attribution combination strategies (e.g., obtaining mean attribution over different land cover classes), as well as tailor-made visualization methods were developed. With this contribution, we are pushing forward the field of XAI on high-dimensional remote sensing data while simultaneously improving the understanding of forecasting AI models and diving into the dynamics that drive extreme events.

Satellites have been used to analyse and monitor Tropical Cyclones (TCs) since the 1960s. Probing the ocean surface during TC conditions, combined measurements from active scatterometers and passive radiometers are essential. Yet, for various reasons (low/medium spatial resolution, loss of signal sensitivity at high winds, rain contamination), the strong gradients of the surface winds that can occur at scales of a few kilometers are badly resolved by these sensors.

More recently, retrieval methods to estimate ocean surface wind speeds in intense TCs have been demonstrated for spaceborne Synthetic Aperture Radar (SAR) observations. By combining the C-band co- and cross-polarized channels, SAR estimates of TCs ocean surface wind speeds can be performed at a high spatial resolution (~1 km). Though limited in numbers, ESA’s Sentinel-1A and B wide-swath acquisitions, complemented by Canadian SAR missions provide unique and precious information on TCs boundary-layer processes, especially within inner-core regions of strong surface wind gradients.

Existing theories for TCs steady-state and/or dynamical life cycle may thus be re-examined with respect to these unprecedented observations. An extended SAR TC database (ESA-SHOC experiment and MAXSS project) is used to corroborate that fundamental conservation principles constrain TCs radial wind structure. Controlling the radial wind decay, two characteristic radii are exhibited and shown to be critical for TCs life cycle. Based on these radii, analytical solutions for 1) TCs steady-state wind structure and 2) TCs wind structure short-term (~24 hrs) evolution are then derived and demonstrated to be fully consistent with SAR observations.

Implications on how SAR observations may complement global and historical TCs vitals to refine the detection of TCs climate trends are also discussed. Furthermore, possible improvements of the theory using other data sources (e.g geostationary visible/infrared sensors and passive microwave sounders) and future missions (e.g Metop Next-Generation, CIMR, ROSE-L with S1NG and Harmony) are addressed.

Particulate matter concentration is an essential ocean variable to characterize biogeochemical processes at different spatial and temporal scales and to improve ocean productivity estimates which can be used to constrain coupled physical and biogeochemical numerical models. Since direct observations are particularly difficult to be obtained, a reliable proxy is given by the optical backscattering coefficient of marine particles (bbp). Most in-situ bbp observations have limited space/time coverage and are often ship-borne or obtained from Biogeochemical-Argo floats. The use of Lagrangian (i.e., water following) Surface Velocity Programme (SVP) drifting buoys like the ones used to sustain the Global Drifter Program in ocean color studies was pioneered over 3 decades ago. Advances in global broadband satellite communication, low-power microprocessor technology and the availability of self-contained backscatter sensors designed for long-term deployments have revamped the interest in this approach. Here, we describe the integration of a commercially available, high-frequency and multispectral optical backscatter sensor into an SVP drifter to collect bbp measurements near the ocean surface, the BO-SVP drifter. bbp measurements obtained by the BO-SVP drifter are reliable over a wide range of environmental conditions and with acceptable differences respect to independent datasets. BO-SVP drifter captures the satellite sub- and pixel scale variability by the combination of the water following capabilities and a high frequency time sampling that can gain daily variations. This configuration enables to overpass a huge number of pixels in the same day strongly benefit the validation of ongoing (e.g. Sentinel-3/OLCI) and future satellite products (e.g. NASA PACE) and the quantification of related uncertainties.

Seagrasses are one of the most productive and widespread coastal marine habitats. They offer a wide range of ecosystem services including coastal protection, biodiversity preservation, and climate regulation through carbon sequestration. However, seagrass meadows have been declining worldwide due to various stressors like pollution, coastal development, and extreme climate events.

The negative effects of single-stressor events on seagrass have been the focus of research for several years. However, due to on-going climate change, the intensity and frequency of climate events, such as Marine Heatwaves and Tropical Cyclones, have been increasing. Therefore, the importance of research on multi-stressor events is accelerating.

To compare single-stressor with multi-stressor effects, we identified two events over the Great Bahamas Bank: one single stressor event of prolonged increased sea surface temperature, and one multi-stressor event of prolonged increased sea surface temperature followed by two tropical cyclones with direct intersection with underlying seagrass meadows.

For the quantification of changes in ecosystem health, we utilised multi-temporal Sentinel-2 image composites and auxiliary bathymetry data within the cloud computing platform Google Earth Engine to create benthic habitat maps, before and after the studied events.

Preliminary results show a drastic impact of the multi-stressor event on seagrass ecosystem health based on the decreased ecosystem extent and density. We estimate that this decrease could have caused a sequestration potential loss of 2,634 tons of CO2 per km2 per year.

Ongoing investigation will help to improve the understanding of stressor events on Bahamian seagrasses’ extent, condition, and health.

The recently launched Surface Water and Ocean Topography (SWOT) mission is the first wide-swath altimetry mission. SWOT extends the capability of existing nadir altimeters to two-dimensional mapping of the ocean surface at an unprecedented spatial resolution. The fast-sampling phase (1-day repeat orbit) of the mission also allows for resolving the temporal evolution of the mesoscale eddies. Building on this unique opportunity, a SWOT Early Adopter “Adopt-A-Crossover” (AdaC) Initiative focused on the nordic high latitudes (https://www.swot-adac.org/campaigns/norwegian-sea-lofoten-basin/) started early in 2023. This study investigates the mesoscale field in the Nordic Seas emerging from different satellite altimetry concepts and from multi-sensor synergy with other satellite missions over a period spanning the fast sampling phase (April-July 2023) of the SWOT mission. In particular we target the representation of the mesoscale eddies in the Lofoten Basin, which is one of the most eddy-rich areas in the Nordic Seas. Ocean tides are additionally considered based on their complexity within the SWOT mission as well as the complex interactions to mesoscale processes. Preliminary results, based on the comparison between SWOT KaRIn retrievals with conventional altimeters and with the Sentinel-1 and Sentinel-3 missions show the potential for obtaining new insights into the ocean mesoscale dynamics partitioning geostrophic and ageostrophic processes in the Lofoten Basin.

We developed an observation-driven methodology for exploring spatio-temporal dependencies between satellite-derived sea surface height (SSH) and sea surface temperature (SST), Chlorophyll-A (CL-A), and Sea Surface Salinity (SSS) fields. We have defined statistical tools to assess and detect spatial connections between SSH anomalies and satellite fields (SST, CL-A, and SSS) anomalies. This methodology has been applied to a 10-year time series in the highly dynamic Gulf Stream current region.

The resulting space–time analysis reveals a seasonal variation in the overall correlations between SST, CL-A, SSS, and SSH fields and emphasizes different relationships and patterns between positive and negative SSH anomalies. During the summer period, the analysis shows a systematically higher correlation between SSS/CL-A and SSH variability than between SST and SSH. During the coldest months of the year (November to March), the correlation with SSS/CL-A is found to be significantly low as the quality of data is deteriorating. We then integrated these findings with ARGO profile measurements, in the same space-time window, to demonstrate the seasonal impact of the mixed-layer depth.

Future work will further explore the use of the proposed methodology for the reconstruction of SSH from a joint analysis of satellite altimetry data and high-resolution satellite-derived data.

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This study combines in situ and remote sensing approaches, to document the spatial and seasonal variability patterns of the phytoplankton biomass and size classes off the west coast of Spain and Portugal. Through the collaboration of several European institutions, within the Interreg Atlantic Area “Innovation in the framework of the Atlantic deep ocean” (iFADO) project, surface samples for the analysis of pigments by HPLC were collected in a total of 13 research cruises between 2019 and 2023. This independent data set has enabled validation of retrievals of Chlorophyll a, the biomass proxy for phytoplankton, from the Copernicus Marine Environment Monitoring Service (CMEMS), the EC Horizon 2020 Copernicus Evolution - Research for harmonised and Transitional water Observation (CERTO) project and UK EO Climate Information Service (EOCIS). Furthermore, the relative contribution of three phytoplankton size classes were estimated, based on the diagnostic pigments content, and compared with satellite retrievals of relative size classes abundance using a number of models, targetteting the aims of Marine Strategy Framework Directive (MSFD) in an upwelling region.

Seagrasses are species of marine flowering plant that form extensive meadows from the intertidal to depths of up to ~50 m, spanning all continents except Antarctica. Seagrass meadows are globally important ecosystems that directly and indirectly provide a wide range of ecosystem services, such as recreation; key forage, refuge and nursery habitats for fisheries species and non-targeted species; climate regulation through carbon sequestration; coastal stabilisation and water quality mediation. Unlike subtidal seagrasses, intertidal seagrass meadows directly provide services to both marine and terrestrial ecosystems. Hence, they have been highlighted as an Essential Ocean Variables, as monitoring their occurrence, extent, condition and diversity can be used to indicate the biodiversity and health of local ecosystems. Yet, monitoring large intertidal areas is resource intensive and unfeasible in many regions. Current global estimates of seagrass extent and recent comprehensive seagrass reviews either ignore intertidal seagrasses and their seasonal variation or combine them with subtidal seagrasses. Here, using high-resolution Sentinel-2 satellite imagery (~650 images), we demonstrate a cost effective method, combining neural networks and bayesian general additive mixed effects models for consistently mapping intertidal seagrass meadows and their phenology at a continental scale; we further highlight the varying seasonal patterns that are seen spanning a 23 degrees latitudinal range. Timings of peaks in seagrass extent varied by up to 5 months, rather than the previously assumed marginal or non-existent variation in peak timing. Furthermore, we show that the intra-annual range in seagrass cover is lower at lower latitudes and far greater at higher latitudes. These results will aid management efforts by providing high-resolution spatiotemporal monitoring data to better inform seagrass conservation and restoration efforts. This methodology could pave the way for near real-time intertidal habitat monitoring tools at the continental scale. The results also highlight the high level of variability in annual intertidal seagrass cover, meaning combination with subtidal seagrass for global assessments will likely produce misleading or incorrect estimates. Estimates of seagrass extent are used for blue carbon assessments and climate offsetting schemes through protection and restoration, thus these phenology patterns need to be better understood to improve calculations of blue carbon assets.

The last 15 years have seen a growing interest in the use of satellite lidar observations for the estimation of optical properties of the sea and bio-geophysical variables. PROTEO (Development of a PeRfOrmance simulaTor for ocEan Observations) is a two-years funded project within the program ‘Scientific activities to support the development of Earth Observation missions’ promoted by ASI (Italian Space Agency). The project is part of the CALIGOLA (Cloud Aerosol Lidar for Global Scale Observations of the Ocean-Land-Atmosphere System) mission, which is included in the ASI's ongoing three-year activity plan (2021-2023). The expected impact of the study is to consolidate and extend the applications of the CALIGOLA mission. In the medium to long term, the results will contribute to the definition of possible lidar satellite missions dedicated mainly to marine studies.

The main objectives and activities of the project, which started in October 2023, will be here presented.

This study addresses the pressing issue of marine pollution caused by plastic waste, primarily originating from sources like rivers, coastal areas, and industrial discharges. It focuses on Liberia's five main rivers, employing an adapted TrackMPD model to track particle movement over an extended period. The study introduces innovative outputs: density and beaching maps.

Part of the GDA AID Marine Environment & Blue Economy initiative, in collaboration with the European Space Agency, Asian Development Bank, and World Bank, the project utilizes advanced Earth Observation services. It supports International Funding Institutions in marine and Blue Economy ventures, aligning with the World Bank's PROBLUE and West Africa Coastal Areas (WACA) programs.

Simulations ran daily throughout 2021, incorporating Copernicus marine services data on sea current fields. The results confirm the widespread challenge of plastic pollution in marine environments.

The study region spans the West African coast, with macroplastic particles released from Liberian rivers. The modified TrackMPD model tracked daily releases from known locations, at a spatial scale of approximately 9 km and a temporal scale of 1 day. The density map indicates particle count per pixel, while the beaching map shows density within a coastal buffer zone of about 18 km.

This study underscores the gravity of the plastic pollution crisis, emphasizing the need for global cooperation. It highlights that discharges from Liberia's rivers can affect areas far from their origin. Future improvements may involve using different driving data, including hourly sea current fields, and real-world case studies with in-situ sampling. The World Bank also suggests testing a 3D model and considering factors like biofouling and refloating to estimate plastic sources near Mauritania through backward simulation.

Measuring the Earth energy imbalance (EEI) at the top of the atmosphere is challenging as it is a globally integrated variable whose variations are small (0.5-1 W.m−2) compared to the amount of energy entering the climate system (~ 340 W.m-2). Accuracies better than 0.1 W.m−2 are needed to evaluate the temporal variations of the EEI at decadal and longer time-scales. The CERES experiment provides EEI time variations with a typical uncertainty of ± 0.1 W.m−2 and shows a trend in EEI of 0.50 +/- 0.47 W.m−2 per decade over the period 2005-2019.

The combination of space altimetry and space gravimetry measurements provides an estimate of the global ocean heat content (GOHC) change which is an accurate proxy of EEI (because >90% of the excess of energy stored in response to the EEI is accumulated in the ocean in the form of heat).

In Marti et al. (2021), the GOHC was estimated at global scales based on the combination of space gravimetry and altimetry measurements over 2002-2016. Changes in the EEI were then derived with realistic estimates of its uncertainty.

Here we present the improvements brought to the GOHC and EEI estimates, such as the calculation of the expansion efficiency of heat over the total water column, the improvement of barystatic sea level change solution, the empirical correction of the wet tropospheric correction of Jason-3 altimeter measurements (Barnoud et al., 2022). The space geodetic GOHC-EEI product based on space gravimetry and altimetry is available on AVISO at https://doi.org/10.24400/527896/a01-2020.003.

References:

Barnoud A., Picard B., Meyssignac B., Marti F., Ablain M., Roca R. Reducing the uncertainty in the satellite altimetry estimates of global mean sea level trends using highly stable water vapour climate data records. Submitted to JGR: Oceans.

Marti, F., Blazquez, A., Meyssignac, B., Ablain, M., Barnoud, A., Fraudeau, R., Jugier, R., Chenal, J., Larnicol, G., Pfeffer, J., Restano, M., and Benveniste, J.: Monitoring the ocean heat content change and the Earth energy imbalance from space altimetry and space gravimetry, Earth Syst. Sci. Data, 14, 229–249, https://doi.org/10.5194/essd-14-229-2022, 2022.

Closing the Sea Level Budget (SLB) at monthly, yearly and interannual time scales, at the best level of accuracy and precision is an essential and central problem of modern physical oceanography. First because closing the SLB is a prerequisite for stating that all the important causes for sea level variability are identified and well quantified. Second, closing

the SLB is a useful tool to cross-validate worldwide complex observing systems such as the Argo network, the satellite gravimetry missions GRACE/GRACE-FO and the satellite altimetry constellation, and to keep a close watch on their performances. Third, because closing the SLB is an efficient approach to test the consistency of different observed variables of the climate system such as sea level, ocean temperature and ocean mass, land ice melt and land water storage changes, with regard to conservation laws including those of mass and energy.

Since 2010, ESA has developed the Climate Change Initiative (CCI) programme in order to produce consistent and continuous space-based records for Essential Climate Variables (ECVs). The Sea Level Budget Closure CCI (SLBC_cci) project was conducted from 2017 to 2019 as the first cross-ECV project within CCI. The project aimed at taking advantage of

the improved quality of sea level-related Earth observation datasets produced within the CCI programme to assess the closure of the global mean SLB over 1993-2016.

Rencently, a second phase of the SLBC_cci project (so-called SLBC_cci+) has been launched for a 3-year period (2023-2026). Here, we will present the main objectives of the SLBC_cci+ project to pursue the work initiated in the first phase: i) to improve the closure of the global mean SLB and extend the global mean SLB time series, ii) to assess the closure of the SLB at regional scales and iii) to explain the time and space variability in SLB both at global and regional spatial scales. More specifically, the global SLB will be updated up to 2023. We also intend to go further by extending the SLB exercise at regional scale for the 2002-2023 period (called GRACE/Argo era). Extending the SLB to regional spatial scales will allow us to identify regions where the SLB does not close, leading to a regional breakdown of the assessment of the CCI products that represent a large part of the individual components used in the SLB. In addition, the SLBC_cci+ project aspires to tackle questions such as “Is the SLB non-closure due to a structural deficiency of the observing system associated with inconsistent effective resolution across the different observing sub-systems such as Argo, GRACE and satellite altimetry? Or is it due to measurement errors in a single observing sub-system? Can we isolate in terms of time and space the potential errors?”. The project also aims at tackling scientific questions such as the attribution of sea level variations and the assessment of the Earth’s energy budget.

The AIS (Automatic Identification System) has continued to develop over the years to address a wide range of user requirements such as ship identification and tracking, collision avoidance, security alert. The International Maritime Organization (IMO) has mandated carriage of AIS in the Safety Of Life at Sea convention (SOLAS) and nowadays, more than 100 000 shipping vessels are fitted with this mandated equipment to navigate through the world's oceans.

From research and developments to derive surface current from space data and AIS, it’s now possible to improove the near real time knowledge of surface currents, in particular in coastal areas and near the marine areas with high vessels density. A constant research allows a consistent comparison between the surface current from AIS and ocean data like Sea Surface temperature, salinity, SSH anomaly, chlorophylle-a, provided thanks to the extensive controlled and validated data set from spatial multi missions (ESA, CNES, NOAA). Theese later data reflect ocean dynamics to be seen in part through the surface current.

One of the advantages of the surface current products from OS Developments is that they can complement altimetry, SAR, multi-mission and SWOT space data, at several thin scales, in several coastal areas. This could be a way to complement them at scale of a several tens of km.

Algorithms developped can now provide the first tests on current fields for near real time ocean applications.

Marine ecosystems are not only defined by the general abundance of primary producers, but also the In many regions of the ocean, primary production is punctuated by hotspots and blooms that exhibit a high spatial and temporal variability in the phytoplankton biomass. A classic example of how periodic changes in primary production has profound effects on the food web and export of carbon is the annual North Atlantic spring bloom. The suggestion that temporal changes in phytoplankton biomass might be as important as the mean standing stock has lead to interest in how to best  evaluate  the variability of phytoplankton on different  temporal and spatial scales. Variability has been investigated on daily, intra-annual, annual, and inter-annual time scales, as well as on sub-kilometer spatial scales.

Satellite-derived proxies for biomass such as Chlorophyll (Chl) and Particulate Organic Carbon (POC) provide unprecedented coverage in time and space to better estimate the variability in phytoplankton biomass on different scales. One key challenge when working with satellite-derived data is, however, the data gaps due to factors such as clouds, sun angle, and sun glint, which  obfuscate the satellite's view of the ocean. The erroneous data that are flagged are not evenly distributed, but show a patchiness that reflects the temporal and spatial scales of synoptic weather systems in different regions. Consequently, on average only 20% of the derived Chl fields are useful. Such sparse and unevenly distributed datasets create a major challenge for common time-series analysis tools, such as Fourier analysis or Empirical Orthogonal Functions (EOFs), thus hindering efforts to understand the frequency  distribution of the data. A common and very successful approach is to aggregate the daily satellite fields to monthly averages for more or less full spatial coverage. The resulting analyses provide spatial distributions and insight about changes in phytoplankton biomass on seasonal or longer timescales, but truncate high-frequency variability.

To better meet the specific challenges with time series analysis of sparse satellite derived properties, we suggest a new method to estimate dominating timescales of variability: MOving Standard deviation Saturation (MOSS). The approach is similar to semi-variograms and earlier analyses of spatial patchiness, but describes temporal variability rather than the spatial autocorrelation or patchiness in the satellite field. The technique is based on calculating the standard deviation ($\sigma$) of the time series data over moving windows of a set time interval, and repeating for different time-interval windows. The average $\sigma$  for each time-window size ($\bar{\sigma}$) increases from zero for a time window that includes just one data point, to $\sigma$ of the full time series.  The largest possible time window is in effect the full time series. The shape of the resulting curve of $\bar{\sigma}$ vs. the time-interval window size ($w$) is then analyzed to identify a dominating time scale, $\tau_d$ of the time series based on the half saturation constant ($K_M$).

Our results show that the method has the ability to assess dominating timescales in time series where  data coverage is sparse. Analysis of synthetic data sets suggests a threshold where estimated timescales start diverge from actual ones is at about 10% coverage. The main consequences of sparse data is MOSS curves with too gentle slopes, which would exaggerate the dominant timescales. We compensate for this problem by scaling all $K_M$ differently for different coverage in the original  data sets. The scaling further allows us to interpreted the resulting values as timescales of variability.

Arctic marine ecosystems are particularly vulnerable to Hg contamination due to melting of polar ice which traps reactive gaseous mercury. In response to climate-induced warming, Hg concentrations in Arctic marine ecosystems are thus expected to be increasing. We investigated the total Hg (THg) concentration in plankton of different size fractions coarsely representing mesozooplankton, microzooplankton and phytoplankton in the fjords and open sea transect along the West Coast of Greenland. In addition, we investigated the effects of Hg and CH3Hg on the dominant arctic zooplankton species. The lowest THg concentration (18 ± 3.51 ng/g dw) was observed in the > 200 size fraction and the highest concentration (25.5 ± 3.7 ng/g dw) in the size fraction of 50-200 µm, suggesting some form of depuration of Hg in mesozooplankton. Also, planktonic organisms from fjords had significantly higher THg concentrations than those from the open sea. The 72 hour LC50 of Hg and CH3HgCl decreased with decreasing size and lipid content of dominant zooplankton species in the rank order of C. glacialis > C. finmarchicus > Pseudocalanus sp. > Oithona sp. > nauplii, with lower LC50 values for CH3HgCl than for Hg. Our results demonstrate the presence of mercury in the lower trophic levels of the Arctic marine environment, and species-specific responses of zooplankton on Hg pollution. Since concentrations in higher trophic levels (e.g., fish) are likely to be dependent on the concentrations at the base of the marine food web, it is necessary to understand the potential and mechanisms of bioaccumulation.

In their latest report, the IPCC expressed low confidence in satellite-based estimates of trends in marine primary production, citing the insufficient length of the time series as well as the lack of independent validation methods. Independent validation of basin-scale primary production estimates is compromised since all available in situ data from photosynthesis-irradiance measurements and all remotely-sensed data on chlorophyll concentration and available light are used for the modelling of primary production. Independent, concurrent, in situ, daily, water-column primary production measurements are not sufficient in numbers or in geographic distribution, for a global validation. Moreover, indirect methods of validation, such as the comparison with bulk property estimates, are compromised by incompatibility of time scales and representation of different components of primary production. In this study, we address the uncertainty in satellite-based primary production estimates by assessing the errors inherent to the calculation, in which each element of the calculation is considered separately. This method closely follows the validation approach described in the Guide to the expression of Uncertainty in Measurement (GUM). We assess the error in each of the input quantities to the primary production model (biomass, photosynthetic parameters and light) and propagate the errors through the model to obtain the uncertainty in primary production. By doing this on a pixel-by-pixel basis, we can address the uncertainties in primary production at regional scales and pinpoint regions where more in situ and remote-sensing data are needed to improve the confidence in satellite-based estimates of trends in marine primary production.

The Great Agulhas current is one of the most

intense western boundary current in the world. During a Sea

campaign on board the Staatsraad Lehmkulh we record all the

AIS messages surrounding the vessel and calculate the associated

current from the drift of the vessels. Most of the time, the

gridded Oceanic current product lacks of sufficient resolution

and the resulting gradient are often under-estimated. Here we

show that these new observations could help to enhance the

resolution and the gradient of velocity which plays a crucial

role in oceanography.

This study aims to apply drone technology for high-resolution mapping of intertidal seagrass meadows. Most of the green macrophytes growing on the intertidal area share the same pigmentary composition, making the discrimination between them challenging using remote sensing. The spectral signature of these vegetation types (e.g. Ulva sp. and Zostera noltei) are very similar within visible wavelengths, but slight differences can be observed in the near-infrared. High spectral resolution tools, like field spectroradiometers, used to collect hyperspectral signatures, efficiently distinguish seagrass from green macroalgae. However, the spatial resolution of satellite mounted hyperspectral sensors is too coarse (~30 m) to accurately map small patches of intertidal vegetation. This is especially true in complex or heterogeneous areas, while hyperspectral drone-mounted sensors are resource and time intensive.

To overcome these limitations, we investigated the potential of multispectral data acquired by drones to map intertidal vegetation at a very high spatial resolution. Our results showed that degrading hyperspectral signatures to the spectral resolution of a MicaSense Dual Sensor camera (10 spectral bands ranging from 444nm to 840nm) allows the discrimination of macrophytes. Our study demonstrates that combining drones, multispectral data, and neural networks is a promising approach for mapping intertidal vegetation, notably discriminating seagrass meadows and macroalgae. Some confusion arises between seagrass and xanthophytes (Vaucheria sp.). This method provides a cost-effective and efficient way to map the macrophyte biodiversity at the class taxonomic resolution, and it can potentially inform management strategies of these ecosystems.

Marine Heat Waves (MHWs) are extreme climatic events that may affect marine organisms directly impacting their optimal-thermal ranges and indirectly via ocean biogeochemistry changes. As a result, phytoplankton communities may modify their structure and functions, and their resilience is not assured. Combined with the synergistic space-borne observations, robotic autonomous observations from Biogeochemical (BGC)-Argo floats offer a novel tool for assessing MHW impacts on phytoplankton communities and marine ecosystems in general, from the ocean surface down to the interior.

This work, supported by the “deteCtion and threAts of maRinE Heat waves (CAREHeat)” project funded by ESA, aims to assess changes and resilience of phytoplankton communities and biogeochemistry under the influence of MHW events. Our focus is on the northwestern (NW) region of the Mediterranean Sea, where phytoplankton biomass shows a large seasonal variation and MHWs have been frequent in the last decades. To achieve this, we leverage BGC-Argo optical observations in conjunction with ocean colour satellite measurements, and MHW events identified using a 2D MHW Global Atlas.

Preliminary research findings indicate that MHWs have a significant impact on the occurrence of the annual phytoplankton surface bloom expected in late winter-early spring. In the NW Mediterranean Sea, during winter and spring, the mixed layer depth (MLD) is reduced by over 40%, reaching a maximum decrease of approximately 70% in April due to MHW events. During this period, the concentration of phytoplankton carbon (Cphyto), and thus of phytoplankton biomass, is reduced by more than 80%. Concurrently, chlorophyll-a concentration (Chl-a), net primary productivity (NPP), particulate organic carbon (POC), and dissolved oxygen (DO) are all significantly lower than climatological levels. In summer, the particle-associated backscattering coefficient (bbp) decreases, while the bbp-to-Chl-a ratio significantly increases by over 120% compared to normal values in August. Furthermore, the ESA OC-CCI normalized remote sensing reflectance (Rrs) spectra from January to April, also show a trend towards increasing water clarity in the surface waters under the influenced by MHW events.

We speculate that, in the NW Mediterranean Sea, MHW events occurring in winter seem to reduce the vertical mixing of the water column which hinders the refurbishment of new nutrients and thus phytoplankton from blooming. The intensification of such extreme events may impact on the quantity of organic carbon that is produced and then exported or consumed by higher trophic levels.

Long-term observations of the flow of dense waters from their area of formation to the abyss of the World Ocean, and the return flow of warm waters, are central to climate research. For the Weddell Sea, an important component of such a system entail monitoring the formation of High Salinity Shelf Water (HSSW) on the continental shelf north of Ronne Ice Front, the transformation to Ice Shelf Water (ISW) beneath the floating Filchner-Ronne ice shelf (FRIS), and the flux of ISW overflowing the shelf break to the deep Weddell Sea. Equally important is the inflow of warm water toward the FRIS cavity. This flow of warm water is predicted to increase during the coming decades. Tipping the continental shelf sea from a cold to a warm state and dramatical increase the basal melting of the FRIS. This ocean tipping point will in turn create an Antarctic ice sheet tipping point resulting in increased flow of inland ice into the ocean.

In 1968, Norwegian scientists deployed the first instrumented moorings to obtain yearlong time series of oceanic current and properties. Since than Norway have operated several observatories in Weddell Sea and during the recent years Alfred Wegener Institute (AWI) and British Antarctic Survey (BAS), Sorbonne Université (LOCEAN), University of Bergen (UiB) and the Norwegian Research Centre (NORCE) have built an extensive observing system for the oceanic regime of the Filchner-Ronne Ice Shelf system, Weddell Watch. The unique data from the Weddell Watch system is contribute to the ongoing international effort to study the ocean induced melting of the Antarctic Ice Shelves.

Measurements of sea CO2 partial pressure (pCO2), temperature, salinity, pH, and wind have been carried out at the Lampedusa Oceanographic Observatory (OO) in the Central Mediterranean, since November 2021. These measurements, coupled with high-quality atmospheric CO2 data from the Lampedusa Atmospheric Observatory, located approximately 15 km to the northeast, part of the Integrated Carbon Observation System (ICOS), enable the computation of air-sea CO2 fluxes.

The computed fluxes reveal a distinct seasonal cycle, with the ocean acting as a carbon sink during colder months (November to May) and as source during warmer months. The net annual CO2 flux is negative, with a net ocean absorption.

Significant differences emerge between two successive November-May periods, with the second year (November 2022-May 2023) exhibiting a substantially smaller integrated CO2 flux than the first year (November 2021-May 2022): (-4.82 + 0.04)E-5 versus (-2.78 + 0.02)E-5 tCO2/m2. A prolonged and intense marine heatwave occurred from May 2022 to March 2023, and possible influences on the carbon cycle are being investigated.

To extend the knowledge from the local to the regional scale and derive flux estimates from satellite data, regression and machine learning algorithms were applied to in-situ data for pCO2 estimation. Different quantities among those obtained from satellite sensors, were considered in the regressions.

Preliminary results show that some of the adopted regression models perform well, achieving a determination coefficient (R2) of 0.96, a root mean square difference of 4.96 μatm, and a bias of -2.00 μatm with respect to measured pCO2 values. The best-performing model includes temperature, chlorophyll, photosynthetically active radiation and wind speed as independent variables.

Machine learning-based models exhibit inferior performance, likely attributable to the dataset's limited size. We are currently applying the regression model to satellite data.

Satellite-derived estimates of instantaneous photosynthetically active radiation (PAR) over the sea, obtained from the Ocean and Land Color Instrument (OLCI) on Sentinel-3, are compared to ground-based measurements conducted on the island of Lampedusa in the central Mediterranean Sea. The ground-based dataset is generated through high-temporal resolution observations on the island using multi-filter rotating shadowband radiometers and Li-190R PAR sensors.

Radiative transfer calculations demonstrate that differences between PAR measurements taken on the island and those over the ocean are negligible. This allows us to assume that the recorded values accurately represent open ocean conditions. Satellite data exhibit good agreement with in-situ measurements, displaying a bias of +5.2% (OLCI overestimates ground-based observations) and a high correlation (R2=0.97). The agreement somewhat weakens during the summer months, when somewhat larger differences are found during specific dates.

To investigate potential factors affecting PAR estimation, we have examined aerosol optical depth (AOD) values measured at Lampedusa and those derived from OLCI data. AOD in Lampedusa exhibits a robust annual cycle, characterized by larger values in spring and summer, often linked to the transport of Saharan dust.

Interestingly, the relative difference between OLCI and in-situ PAR measurements shows a negative correlation with the relative difference between OLCI and ground-based AOD. This suggests that a more accurate determination of AOD may lead to enhanced satellite-based PAR estimates.

Furthermore, it is observed that the difference between OLCI and ground-based PAR determinations are are larger with moderate/high aerosol loads (AOD > 0.2). These elevated aerosol levels in Lampedusa are predominantly associated with Saharan dust events.

Intermittent wind-driven coastal upwelling and downwelling are ubiquitous processes that drive a large part of the high frequency variability of coastal hydrography, with implications for ecosystems and socio-economic activities. However little synoptic information exists on these processes, especially in regions characterized by rapidly changing atmospheric forcing and complex shorelines. In addition, recent work suggested that remotely sensed L4 Sea Surface Temperature largely underestimates the thermal fingerprint of coastal upwelling due to artificial flagging procedure.

Combining multi-annual hourly in-situ observations of nearshore temperatures with a long-term archive of Sea Surface Winds (SSW from ERA5 reanalysis), we investigate the statistical occurrence of wind-driven upwelling and downwelling events and their associated thermal responses along the northwestern Mediterranean coastlines. After validating the gridded SSW product with in-situ wind measurements, a Wind-based Upwelling and Downwelling Index (WUDI) is calculated at 20 km spatial resolution and validated against time-series of surface and subsurface in-situ temperatures at 11 coastal locations.

We find that the WUDI index allows monitoring robustly all year round both up- and downwelling events that effectively cause coastal cooling/warming. On average, significant thermal responses to favorable winds appear after short delays (spanning 6-54h for upwelling, 12-66h for downwelling, depending on the site considered) with intensities 5 to 10 times stronger in stratified as compared to nonstratified conditions. Maximum near-surface cooling (subsurface warming, respectively) recorded after the most extreme events can reach up to -12°C (+9.5°C, respectively) during the period of seasonal stratification.

A climatological database of wind-driven events that can be associated with typical thermal responses is constructed for the Northwestern Mediterranean shorelines over the last four decades. It shows that up/downwelling events are favored along certain portions of coastline, called "cells", and are characterized by specific magnitudes, frequencies of occurrence and durations with respect to seasonality. Despite differential responses, wind-driven events are more frequent during winter-spring than during summer-autumn. More generally, our conclusions suggest that future integrated analyses of the next generation SSW and SST products, along with high-frequency in-situ observations, show great promises in deciphering the full variability of the coastal ocean.