Conference Agenda

In support of efforts to protect eelgrass beds, halt biodiversity loss, and promote recovery, the SwedCoast-BlueCarb project applies satellite Earth Observation (EO) to Swedish coastal waters. Funded by the Swedish and UK Space Agencies, the project combines EO and in situ data to assess the impacts of climate-change mitigation in contrasting test areas: the CDOM-dominated Baltic Sea around Kalmar, and the Swedish west coast that's strongly influenced by Baltic outflow.

Initial activities established collaborations with academic partners and monitoring programmes. The EO processing has focused on generating consistent datasets with atmospheric correction methods tested, and a modelling approach developed to retrieve both water optical properties and submerged vegetation from surface reflectance. Copernicus Sentinel-2 imagery (20 m) is used to map eelgrass (Zostera marina) and bladderwrack (Fucus vesiculosus) extent, together with uncertainty estimates, where vegetation occurs within detectable depths. In addition, laboratory analyses support the optical modelling by characterising the absorption and reflectance spectra of submerged vegetation.

Now that the initial modelling approach is working, the ongoing work utilises the benefits of a machine learning model to accelerate the modelling process, allowing for faster processing and, consequently, systematic monitoring across large spatial and temporal scales. Sentinel-3 data (300 m) provide complementary information on water optical status, a key factor in determining light availability and ecosystem health. In parallel, commercial WorldView-2 imagery (2 m) is being evaluated to demonstrate the potential of very high-resolution mapping.

Ultimately, an automated processing chain will deliver EO-based products openly through a GIS-style portal. These products will enable local authorities and conservation groups to track the condition of eelgrass, identify restoration priorities, and assess the effectiveness of management measures. By quantifying vegetation extent and water optical properties, the project supports blue-carbon conservation goals and strengthens the evidence base for climate-change mitigation in coastal ecosystems.

Seagrass ecosystems store a disproportionately large amount of the ocean’s total carbon, but the synergistic impact of climate and anthropogenic interactions has led to severe habitat loss and carbon storage capability of the ecosystem. High dynamicity of the seagrass ecosystem and associated blue carbon stock necessitates timely monitoring of blue carbon stock to determine variability in the source dynamics and budget of these vulnerable ecosystems. Conventional field measurements of seagrass biomass and sediment organic carbon are laborious and economically non-viable, necessitating the need to develop regional algorithms for monitoring seagrass biomass and organic carbon using satellite data. This study assessed the spatio-temporal variability of seagrass biomass and sediment organic carbon stock along the Palk Bay, South-east coast of India, through a combination of in-situ surveys and satellite-based modelling for the period January 2022 -December 2023. Two permanent monitoring sites—Chinnapalam and Mandapam were selected following pilot surveys, with additional sampling conducted from Thondi to Thangachimadam to validate satellite-derived estimates. Field data on seagrass biomass, sediment, and water quality were collected and analysed, while Sentinel-2 imagery (2015–2023) was processed to map annual and seasonal variability in seagrass cover. NDVI- based seagrass above ground biomass was also obtained from Sentinel data, and validated with field observations. Results revealed significant seasonal variability in total seagrass biomass, with higher values during the wet season (778.29 ± 227.06 g dwt m⁻²) compared to the dry season, whereas the sediment organic carbon showed higher concentrations in the dry season (1.03 ± 0.23%) compared to the wet season (0.72 ± 0.06%). Among species, Cymodocea serrulata showed the highest biomass, while Halophila ovalis and Halodule pinifolia had comparatively lower values. Above-ground (AGB) and below-ground biomass (BGB) also exhibited significant species-level and seasonal variability, contributing to seasonal differences in total organic carbon stock. Empirical models linking NDVI with in-situ AGB (R² = 0.80) enabled satellite-based estimation of biomass and carbon stock, based on AGB-carbon stock relationship for the Palk Bay. Validation with independent field data showed strong agreement with seagrass AGB (R² = 0.67) and carbon stock (R² = 0.72). This integrated field–satellite approach provides a robust framework for mapping blue carbon resources at regional scales, reducing economic and human efforts. The outputs were then incorporated in a WebGIS application, that offers valuable decision-support tools for identifying priority areas for seagrass management and restoration to enhance climate mitigation potential.

The ongoing rise in ocean acidification (OA) presents a significant threat to coral reef
ecosystems, particularly affecting the vulnerability of coral species to environmental stressors.
This research focuses on quantifying the impact of OA on coral reefs surrounding Saint Martin
Island, Bangladesh—an area recognized for its coral biodiversity. Through systematic ecological
surveys, water quality analyses, and predictive modeling using machine learning (ML), we aim
to illustrate the changing conditions of coral growth rates and resilience linked to OA over time.
Coral growth is negatively impacted by OA due to reduced skeletal density, making these
ecosystems more susceptible to bioerosion and storm damage, thereby impairing their structural
integrity and resilience.
Historical data shows that live coral cover surrounding Saint Martin Island has decreased
dramatically, from 20–25% in the 1980s to below 10% today. This decline has been exacerbated
by both global climate change and localized stressors, such as coastal pollution and overfishing.
Our analysis will specifically explore how these stressors interact with OA to further decrease
coral resilience, supporting findings that highlight the severe implications of combined threats
faced by coral ecosystems. Implementation of modern ML techniques allows us to predict future
risks and develop proactive management strategies for coral conservation and restoration,
focusing on promoting resilient coral species and effectively managing local impacts.
Furthermore, the findings of this research will be pivotal in guiding stakeholders towards
adaptive solutions, such as the establishment of locally managed marine protected areas and the
restoration of protective habitats like mangroves and seagrass beds, which can help mitigate the
effects of acidification. This comprehensive understanding is crucial for sustaining not only the
coral reefs themselves but also the myriad of ecosystem services they provide, vital for local
communities reliant on fisheries and tourism. This research aims to fortify the resilience of Saint
Martin Island’s coral reefs against the backdrop of ongoing climate change, ensuring the
longevity of their ecological and economic contributions.

The coastal ocean is a region of socio-economic and ecological importance. Yet, this system is under immense pressure from global climate change and other anthropogenic hazards, which threaten ecosystem services and increase the vulnerability of the growing coastal population and infrastructure. Whilst the coastal ocean is under pressure, it can also be part of the solution to manage and adapt to changes, with the phytoplankton ecosystem as an example of this. Primary production by phytoplankton plays an important role in the global carbon cycle through the conversion of inorganic carbon in the water to organic carbon via photosynthesis. This process is not only important for global climate regulation, but also essential for supporting all coastal ecosystems and the services they provide. In this study, we explored changes in phytoplankton primary production in the global coastal ocean from 1998-2022 within Longhurst's ecological provinces. We address three key questions: (1) In which coastal provinces does primary production undergo significant changes? (2) What are the underlying causes of these changes? And (3) Is the aggregation of data into large areas (such as the ecological provinces) suitable for investigating the underlying causes of any observed change in the global coastal ocean?
To address these questions, we have undertaken trend analysis of primary production model outputs based on the Ocean-Color Climate Change Initiative (OC-CCI) data, and applied a linear regression analysis using a seasonal decomposition of the time series and autoregressive integrated moving average (ARIMA) method. We further explored the impact of sea surface temperature (from SST CCI), phytoplankton chlorophyll-a concentration from OC-CCI (while recognising that chlorophyll-a concentration was an input to the primary production calculations, and that, therefore the two are not independent) and upwelling (Bakun Index) on primary production. We also studied the impacts of missing data (with a systematic bias) and of data aggregation methods at individual pixel level on apparent or artificial trends. Results showed that there were five (out of 23) coastal provinces with statistically significant increasing or decreasing linear trends in primary productivity, and that all regions with significant trends are also upwelling regions. Through this work we contribute to developing an understanding of the changes experienced in the global coastal ocean, which is essential knowledge for management of coastal challenges and pressures globally. This is a contribution to the UKRI NERC funded FOCUS Project (NE/X006271/1), the ESA SCOPE Project and the Simons Foundation CBIOMES Project.

Monitoring marine carbon pools and fluxes is critical for understanding the ocean's role in the global carbon cycle. Satellite ocean color data provide a unique tool for assessing key biogeochemical parameters, but their accuracy relies on robust regional algorithms. Since 2002, the Ocean Optics Laboratory at the Shirshov Institute of Oceanology has developed an electronic Atlas for the Russian seas based on satellite data and validated regional algorithms (http://optics.ocean.ru). The Atlas offers enhanced accuracy for bio-optical characteristics, which are fundamental proxies for quantifying carbon-related processes, primarily chlorophyll-a and coccolithophore concentrations. This study leverages over than two decades (1998–2024) of satellite observations processed with these regional algorithms to analyze interannual variability and trends in key parameters linked to the ocean carbon cycle in the Barents, Kara, Laptev, White, Baltic, Black, and Caspian seas. We present analyzed trends in these parameters, which provide valuable insights for future carbon cycle studies. Notably, a significant positive trend in coccolithophore concentration in the northeastern Black Sea confirms the intensification of blooms, which has implications for calcium carbonate production and export fluxes. Similarly, a positive trend in chlorophyll-a concentration in the northern Barents Sea, likely associated with regional sea ice decline, points to increased primary production and potential carbon drawdown. Such long-term observations are crucial for validating and improving climate models that represent high-latitude and marginal seas.

Marine net primary production is a cornerstone of the global carbon cycle and a foundation of marine ecosystems. As one of the largest carbon fluxes on the planet, it sustains marine biodiversity, underpins global ocean ecosystems and supports critical ecosystem services. Climate change is disrupting marine primary production in complex and poorly understood ways, with major implications for the carbon cycle, food security and climate feedbacks. Yet there remains little consensus on either the direction or magnitude of projected change. To address this uncertainty, we analyse remote sensing net primary production trends using six different algorithms, and benchmark them against fifteen divergent model projections. Our results suggest that future declines in production are more likely than current models predict, and that even the best-performing models still underestimate the magnitude of ongoing declines. However, large uncertainties remain, as trend estimates and model rankings depend strongly on the choice of remote sensing algorithm. To address this knowledge gap, we applied a subset of these algorithms to biogeochemical-Argo measurements. Although this offers less spatial and temporal coverage than satellites, it provides critical information at depth. Most notably, the disagreement in trend direction across ocean biomes disappears in this framework, and estimated changes are often much larger than those inferred from remote sensing alone. Together, these findings highlight both the promise and the limitations of current approaches to quantifying ocean productivity and its role in the carbon cycle. They underscore the urgent need for an integrated strategy that brings together satellite observations, autonomous platforms and biogeochemical models. Such integration is essential not only to constrain projections, but also to generate new mechanistic understanding of the drivers of ocean productivity change, knowledge critical for improving models and informing climate policy.

Responses of the Earth system to climate change may not be gradual, but abrupt in the form of tipping points. A new ESA project ‘TIME’ on Tipping points and abrupt changes In Marine Ecosystems, is designed to investigate eight vulnerable elements of the marine ecosystems for evidence of loss of resilience or for signs of abrupt changes, based on satellite data. For one of these elements: coccolithophores, a type of phytoplankton covered with highly reflective calcium plates, is unique in its ability to be studied globally even using coarse visible satellite data.

Previous studies have shown that blooms of coccolithophores are starting to appear in new areas such as sub-polar waters, and are becoming more prominent at mid-latitudes. In TIME we have extended our dataset to generate a consistent 45-year time series of AVHRR data for analysis of coccolithophores, which will be used to investigate the drivers of change using AI tools. In this presentation, we will present preliminary results of this unique dataset.

pim@pml.ac.uk

The strong control that carbon dioxide (CO₂) emissions have over Earth's climate requires their accurate quantification and study. The ocean annually absorbs more than a quarter of all CO₂ emissions and observation-based estimates of this ocean carbon uptake (sink) have now become a key component within annual global carbon budget assessments used to guide policy. And these ocean observations form one of only two, key observational pillars and constraints within annual carbon assessments, and their uncertainties directly impact the closure of the total budget. These assessments are fairly unique in the area of Essential Climate Variables (ECVs), as data synergy approaches are fundamentally required in their generation, whereas most other ECVs require only single parameters or measurements. These ocean assessments rely heavily on multiple satellite, in situ and re-analysis datasets, but uncertainties and errors within these datasets are often unknown or poorly constrained with unknown consequences. To address these issues, the Ocean Carbon for Climate (OC4C) project are now developing the first ocean carbon assessment that focusses on using climate data records. The first version (for 1980 to 2024) uses five climate data records, and this has enabled the regional and global uncertainties to be comprehensively characterised. This presentation will present the new dataset along with example case studies to illustrate some of the many advances. This will include i) an example erroneous signal that appears when climate records are not used, ii) how inclusion of biological signals can reduce the uncertainties, iii) the power of community led experiments to investigate underlying signals, iv) how an enhanced ocean budget can be used to interrogate the global carbon budget and the land component within this, and v) how the dataset has already been used for the 2025 Planetary Health Check assessment – an effort that charts humanity’s impact on the Earth System to guide government policy.

Rapid Arctic warming and permafrost thaw are mobilizing large pools of terrestrial organic carbon (OC) into rivers, deltas, and coastal seas. Quantifying these land–ocean fluxes is essential for constraining regional and global carbon budgets, predicting carbon cycle feedbacks, and assessing impacts on vulnerable Arctic shelf ecosystems. Yet major uncertainties remain: most satellite retrievals were designed for large water bodies, while Arctic rivers and nearshore zones are characterized by narrow channels, strong salinity and optical gradients, low sun angles, and adjacency effects that challenge algorithm performance. Here, we assess the potential of optical remote sensing to trace organic carbon across Arctic land–ocean compartments. Building on high-frequency river monitoring and multi-platform campaigns in the Mackenzie–Beaufort region, we evaluate Sentinel-2 MSI and Sentinel-3 OLCI algorithms, including atmospheric correction approaches (Acolite, Polymer, C2RCC) and inherent optical property retrieval schemes (band ratios, semi-analytical, neural networks). Results show that while absolute reflectances differ across atmospheric corrections, consistent spectral shapes and newly derived bio-optical relationships enable robust retrieval of dissolved and particulate OC across riverine, deltaic, and shelf waters. Remote sensing captures the strong seasonality of Arctic rivers, including spring freshet and rain-driven pulses, and reveals the lateral spread and variability of river plumes across the shelf. Our findings demonstrate that remote sensing can capture both the seasonal dynamics of OC export and the spatial heterogeneity of carbon pathways across compartments and salinity gradients. Comparisons with in situ data reveal limitations of ocean colour algorithms, guiding their applicability in Arctic environments. Satellite monitoring offers overlooked potential in Arctic rivers and coastal transition zones, providing synoptic coverage that complements sparse and costly field data. Through regional validation, performance assessment, and application of the most reliable combinations of atmospheric correction and IOP retrieval algorithms, we demonstrate how carbon remote sensing from space can reduce uncertainties in Arctic land–ocean flux estimates.

Quantifying the ocean carbon budget and understanding how it is responding to anthropogenic forcing is a major goal in climate research. It is widely accepted that the ocean has absorbed around a quarter of CO₂ emissions released anthropogenically, and that the ocean uptake of carbon has increased in proportion to increasing CO₂ emissions. Yet, our understanding of the pools of carbon in the ocean, the processes that modulate them, and how they interact with the land and atmosphere, is not satisfactory enough to make confident predictions of how the ocean carbon budget is changing. Improving our understanding requires a holistic and integrated approach to ocean carbon cycle research, with monitoring systems capable of filling the gaps in our understanding. Satellite observations can play a major role in this. The ESA-funded ‘Satellite-based observations of Carbon in the Ocean: Pools, fluxes and Exchanges’ (SCOPE) project aims to provide the best possible characterisation of the ocean carbon budget from satellite observations and further the understanding of its variability in space and time. Here, we present the development of an internally consistent dataset of the carbon pools, fluxes and exchanges that are observable from space, including dissolved inorganic and organic carbon, particulate inorganic and organic carbon, phytoplankton carbon, primary and export production, and air-sea CO₂ and land-sea exchange. This satellite-based ocean carbon dataset is harmonised in space and time, based on climate-quality input data from the ESA’s Climate Change Initiative and fully error-characterised. This allows us, for the first time, to address both the physico-chemical and biological processes that drive the ocean carbon cycle in a consistent manner. We use the newly developed satellite-based ocean carbon dataset to analyse trends in each component of the ocean carbon cycle since the start of the ocean-colour data record in 1997. This will provide insight into how satellite observations can aid in the assessment of the ocean carbon budget in a climate context and provide useful information to evaluate and improve climate models.

Satellite-derived models of Primary Production (PP) is a critical tool to constrain the global carbon system and to better understand the mechanism of marine ecosystems with with unprecedented spatial resolution and coverage. While arguably one of the most significant developments in biological oceanography over the last 30 years, there is still questions about what the models predict (the output lies somewhere between Net and Gross PP) or how to validate the models with in situ observations. PP is furthermore only a part of the cycling of carbon in the surface ocean. To address these challenges, we have developed purely data-driven models to better constrain upper-ocean fluxes including export production (EP).

We use decision-tree models to predict primary and export production from satellite-derived properties. The approach is expanded by including depth as one input feature, allowing for depth-resolved predictions. Our approach allows us to generate purely data-driven global models with high skill showing that depth resolved PP and EP estimates are feasible. We are able to predict well constrained vertical relationships for PP and EP closely analogous to earlier analytical work. The models can in conjunction also estimate community respiration, remineralization attenuation (the Martin relationship), and export efficiency.