This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August-September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi. Measurements were assimilated into a very high resolution (1.5 km) numerical forecast model, with an implementation of biogeochemistry data assimilation for this purpose. The model forecast was then used by a probabilistic uncertainty model to plan a series of waypoints to navigate the glider fleet towards features of interest. By utilising a continuous feedback loop of measurement, prediction, guidance, and refinement a system with real time coupling between the real ocean environment and its digital counterpart has been established.Building upon a prior pilot study of , this work improves every element of the system to address several limitations of the prior configuration. Whilst a bloom was present in the wider area, measurements and modeling suggest it didn't enter the glider operation zone. Despite this and other operational challenges the mission clearly demonstrates the benefits of such a system. The ability to simultaneously track multiple features of interest, namely chlorophyll maxima and oxygen minima, would not have been possible with a single glider resulting in significant benefits to the system. Furthermore, the improvement to biogeochemical forecasting has been demonstrated through a series of post mission experiments, highlighting the advantages of high temporal resolution observations and increased spatial resolution of the model.

The UN General Assembly (2015) emphasizes sustainable pathways to enhance resilience for people and nature, with future development driven by data and evidence. Sustainable development frameworks (e.g., the UN 2030 Agenda and the 2016 Paris Climate Agreement) highlight the importance of data and evidence in assessment and decision-making that respects national policies and priorities. Global advances in Earth observation (EO) data provision and digital solutions that increase efficiencies, timeliness, and affordability are making major contributions. However, many existing platforms rely on externally hosted cloud infrastructures and generic global classifications of environments that may not align with domestic statutory definitions, limiting national control over data governance, methodological standards, and regulatory reporting. These constraints have raised growing concerns regarding data and technological sovereignty for countries seeking authoritative, policy-ready environmental information. Using Wales (United Kingdom; UK) as an exemplar, this study showcases the design and implementation of a flexible, sovereign National Digital Infrastructure (NDI) that uses the Open Data Cube (ODC) to apply Living Earth, a novel and customizable approach for EO-focused environmental monitoring. Outputs are time series of land cover and habitat maps and change products, including post-event (e.g., fire, flood) management, which address key policy requirements and support land and water resource management (from freshwater to marine environments), while ensuring public dissemination. Major advantages include the sharing of consistent datasets across governments and partner organizations, minimizing duplication of effort, improving transparency, traceability, and reproducibility, fostering collaboration between diverse stakeholders and communities, promoting inclusivity in environmental management decision-making, and supporting sustainable outcomes.

Antarctic sea ice showed a profound, step-wise reduction around 2016-2017, but the scarcity of modern time series means that we know very little about how Southern Ocean biota have responded. Given the paucity of direct field data, we combined satellite data with KRILLBASE, a large historical salp and krill database, to examine how the new, low-ice era has changed the feeding habitat of these key plankton species. Most (69%) of the Southern Ocean area has experienced an increase in mean summer chlorophyll a (chl a) values since the step-change in sea ice. We went beyond bulk chl a indices to classify the Southern Ocean into 14 optical water types based on their spectral reflectance. At this finer resolution, the 2 species showed more habitat differentiation than discernible from chl a alone, with salps being strongly correlated with those optical water types that increased in area after the 2017 reduction in sea ice. These water types have moderate phytoplankton concentrations (similar to 0.4 mg chl a m(-3)), and large expanses of the Indian-Pacific sector improved from being too oligotrophic into better feeding habitats for salps. We also show fundamental differences in habitat requirements of both species between the Atlantic and the Indian-Pacific sectors, but overall, our feeding habitat indices suggested that the modern low-ice era has become more favourable for salps. Antarctic sea ice is a crucial part of the climate system, and the recent era of extreme variability and record lows has major ramifications for food webs and biogeochemical cycles.

Effective management to conserve marine environments requires up-to-date information on the location, distribution, and extent of major benthic habitats. Remote sensing is a key tool for such assessments, enabling consistent, repeated measurements over large areas. There is particular interest in using freely available satellite images such as from the Copernicus Sentinel-2 series for accessible repeat assessments. In this study, an area of 438 km2 of the northern Red Sea coastline, adjacent to the NEOM development was mapped using Sentinel-2 imagery. A hierarchical Random Forest classification method was used, where the initial level classified pixels into a geomorphological class, followed by a second level of benthic cover classification. Uncrewed Aerial Vehicle (UAV) surveys were carried out in 12 locations in the NEOM area to collect field data on benthic cover for training and validation. The overall accuracy of the geomorphic and benthic classifications was 84.15% and 72.97%, respectively. Approximately 12% (26.26 km2) of the shallow Red Sea study area was classified as coral or dense algae and 16% (36.12 km2) was classified as rubble. These reef environments offer crucial ecosystem services and are believed to be internationally important as a global warming refugium. Seagrass meadows, covering an estimated 29.17 km2 of the study area, play a regionally significant role in carbon sequestration and are estimated to store 200 tonnes of carbon annually, emphasising the importance of their conservation for meeting the environmental goals of the NEOM megaproject. This is the first map of this region generated using Sentinel-2 data and demonstrates the feasibility of using an open source and reproducible methodology for monitoring coastal habitats in the region. The use of training data derived from UAV imagery provides a low-cost and time-efficient alternative to traditional methods of boat or snorkel surveys for covering large areas in remote sites.

National maps are essential to support the conservation, restoration, and sustainable management of blue carbon ecosystems (BCE). This is particularly important for nations in the Indo-Pacific region, including Papua New Guinea (PNG), that aspire to integrate these ecosystems into their nationally determined contributions (NDCs) for ecosystem accounting. This study focussed on mapping the extent of BCEs in PNG using Earth observation data for the year 2020 and reporting on biomass and carbon storage services. Land cover categories were generated using the Living Earth framework for the 15 coastal provinces of PNG. The total BCE area in PNG (14,353 km²) comprised 30% mangrove, 65% lowland peat swamp forest, 3% saltmarsh, and 2% seagrass. Lowland peat swamp forests contribute the greatest biomass (137.94 ± 67.10 Tg) followed by mangroves (71.79 ± 27.16 Tg), with a total biomass of 212.99 ± 95.89 Tg. Across PNG, a total of 710.46 ± 362.75 Tg C were estimated for belowground carbon of BCEs (reporting to 1 m depth), almost seven times more than that of aboveground carbon (102.14 ± 45.97 Tg C). This study highlights the need for a consistent and standardised framework for mapping BCEs, which can support coordinated management of coastal landscapes across provinces that contribute to national policies and NDC reporting. This case study can be used as a demonstration for other nations where similar opportunities and challenges may exist for mapping BCE using Earth observations, with a framework that can be compared and adapted to user requirements.

Introductions of non-indigenous species such as seaweeds to new ecosystems are one of the major threats to biodiversity and resilience of coastal and marine habitats. Across the Atlantic into the Caribbean and Gulf of Mexico, recurring invasions of Sargassum seaweeds are currently monitored using satellite sensors such as OLCI, VIIRS and MODIS. These optical sensors are, however, limited by cloud, and their coarse spatial resolution prevents detection of seaweed patches smaller than approximately 2000 m 2 in size. Synthetic Aperture Radar (SAR) imagers, such as the sensor carried aboard Sentinel-1, may overcome both these limitations; detecting Sargassum through cloud at higher spatial resolutions.With a focus on open waters east of Barbados, we demonstrate the utility of Sentinel-1 GRDH SAR data for the detection of incoming Sargassum invasions. Application of a SARgassum index, which makes use of SAR data in predominantly VH-polarisation, improved contrast between Sargassum and background seawater. Downstream application of a Constant False Alarm Rate (CFAR) detector allowed for automated detection of floating Sargassum patches as small as 400 m 2 in size. This is a five-fold improvement on MODIS, proving the utility of Sentinel-1 for monitoring of Sargassum in coastal to open waters that may also be impacted by cloud. Though SAR data collected by Sentinel-1 is not currently included in existing Sargassum monitoring efforts over the Caribbean and Gulf of Mexico, our results suggest that it is a valuable but overlooked resource.

Intertidal areas, which emerge during low tide, form a vital link between terrestrial and marine environments. Seagrasses, a well-studied intertidal habitat, provide a multitude of different ecosystem goods and services. However, owing to their relatively high exposure to anthropogenic impacts, seagrasss meadows and other intertidal habitats have seen extensive declines. Remote sensing methods that can capture the spatial and temporal variation of marine habitats are essential to best assess the trajectories of seagrass ecosystems. An advanced machine learning method has been developed to map intertidal vegetation from satellite-derived surface reflectance at a 12-band multispectral resolution and distinguish between similarly pigmented intertidal macrophytes, such as seagrass and green algae. The Intertidal Classification of Europe: Categorising Reflectance of Emerged Areas of Marine vegetation with Sentinel-2 (ICE CREAMS v1.0), a neural network model trained on over 300,000 Sentinel-2 pixels to identify different intertidal habitats, was applied to the open-access long term archive of systematically collected Sentinel-2 imagery to provide 7 years (2017-2023) worth of intertidal seagrass dynamics in 6 sites across Western Europe (471 Sentinel-2 Images). A combination of independently collected visually inspected Uncrewed Aerial Vehicle imagery and in situ quadrat images were used to validate ICE CREAMS. Having achieved a high seagrass classification accuracy (0.82 over 12,000 pixels) and consistent conversion into cover (19% RMSD), the ICE CREAMS model outputs provided evidence of site specific variation in trajectories of seagrass extent, when appropriate consideration of intra-annual variation has been considered. Inter-annual dynamics of sites showed some instances of consistent change, some indicated stability, while others indicated instability over time, characterised by increases and decreases across the time-series in seagrass coverage. This methological pipeline has helped to create up-to-date monitoring data that, with the planned continuation of the Sentinel missions, will allow almost real-time monitoring of these habitats into the future. This process, and the data it provides, could aid management practitioners from regional to international levels, with the ability to monitor intertidal seagrass meadows at both high spatial and temporal resolution, over continental scales. The implementation of Earth Observation for high-resolution monitoring of intertidal seagrasses could therefore allow for gap-filling seagrass datasets, and sustain specific and rapid management measures.

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5–9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11–12 cm and bias of 2.7–6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Abstract Seagrasses are marine flowering plants that form extensive meadows from the inter-tidal zone up to ~50 m depth. As biological and ecological Essential Biodiversity Variables, seagrass cover and composition provide a wide range of ecosystem services. Inter-tidal seagrass meadows provide services to many ecosystems, so monitoring their occurrence, extent, condition and diversity can be used to indicate the biodiversity and health of local ecosystems. Current global estimates of seagrass extent and recent reviews either do not mention inter-tidal seagrasses and their seasonal variation, or combine them with sub-tidal seagrasses. Here, using high-spatial and high-temporal resolution satellite data (Sentinel-2), we demonstrate a method for consistently mapping inter-tidal seagrass meadows and their phenology at a continental scale. We were able to highlight varying seasonal patterns that are observable across a 23° latitudinal range. Timings of peaks in seagrass extent varied by up to 5 months, rather than the previously assumed marginal to non-existent variation in peak timing. These results will aid management by providing high-resolution spatio-temporal monitoring data to better inform seagrass conservation and restoration. They also highlight the high level of seasonal variability in inter-tidal seagrass, meaning combination with sub-tidal seagrass for global assessments will likely produce misleading or incorrect estimates.

Abstract Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open‐source software.

The Atlantic Meridional Transect (AMT) program (www.amt-uk.org) provides the perfect opportunity to observe the phytoplankton community size structure over a long latitudinal transect 50oN to 50oS, thereby covering the most important latitude-related basin-scale environmental gradients of the Atlantic Ocean. This work presents cell abundance data of phytoplankton taxa recently collected during cruises AMT28 and 29 (in 2018 and 2019, respectively) using flow cytometer and microscope observations, as well as the pigment composition of the community, to assess the abundance and spatial distribution of taxonomic groups across the Atlantic. The community size structure showed a clear consistency between cruises at large spatial scale, with a dominance of picoplanktonic Cyanobacteria in oceanic gyres, an increase in all groups in the equatorial upwelling region, and high biomass of microplankton size class at higher latitudes. Phytoplankton carbon biomass for oceanographic provinces, ranged from median values of 10 to 47 mg Carbon m-3, for the oligotrophic gyres, and South Atlantic (45°S-50oS), respectively. Satellite images of total chlorophyll a (as a proxy for phytoplankton biomass) as well as the relative contribution of the three phytoplankton size classes were produced for both cruises, and despite the small number of matchups, statistically agreed well with in situ size classes estimated as carbon biomass, constituting the first attempt in the literature to match satellite size classes with in situ data derived from cell abundance. The comparison of community structure between recent cruises (2019, 2018, 2015) and earlier ones (1995-1998) indicates a decrease in the number of diatom-bloom forming species, and an increase in Dinoflagellates, whereas nitrogen-fixing Trichodesmium abundance in tropical Atlantic remains constant. Within the recent period, a relative increase in the median values of picoplankton fraction was seen in SATL region, counterbalanced by a decrease in both nano- and microplankton fractions. Additionally, this study includes a database of species identified by microscopy, which had been interrupted for 20 years, providing a basis for long-term series of phytoplankton species.

The global distribution of high Remote-sensing reflectance (Rrs) waters visible from satellite, likely associated with coccolithophore blooms, has changed markedly over the past 40 years. Over that period there has globally been an overall decrease in bloom area of 1.15 million km2 but with notable Rrs increases in the Barents Sea and the Antarctic Ocean. The primary drivers of these fundamental changes to ocean biogeochemistry have been investigated using Machine Learning techniques together with contemporaneous global multi-decadal time-series of sea-surface temperature (SST); wind speed and stress; sea level anomaly (SLA); photosynthetically available radiation (PAR) and; mixed layer depth (MLD). When split into ocean provinces different drivers of positive and negative trends in Rrs were found to dominate in different regions, but generally increases were found to coincide with changes to SST, PAR and reductions to wind-speed.

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5–7 cm relative to validation samples, and RMSEs of approximately 10–12 cm relative to in situ ALT measurements.

To comprehensively support national and international initiatives for sustainable development, land cover products need to be reliably and routinely generated within operational frameworks. Coupled with consistent semantics and taxonomies, ensuring confidence in mapping land cover for multiple time periods, facilitates informed decision-making at scales appropriate to multiple policy domains. The United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) provides a taxonomy that comparable at different scales, level of detail and geographic location. The Open Data Cube (ODC) initiative offers a framework for operational continental-scale land cover mapping using analysis-ready Earth Observation data. This study utilised the FAO LCCS framework and the Landsat sensor data through Digital Earth Australia (DEA; Australia's ODC instance) to generate consistent and continent-wide land cover mapping (DEA Land Cover) of the Australian continent. DEA Land Cover provides annual maps from 1988 to 2020 at 25 m resolution. Output maps were validated with ∼12,000 independent validation points, giving an overall map accuracy of 80%. DEA Land Cover provides Australia with a nationally consistent picture of land cover, with an open-source software package using readily available global coverage data and demonstrates a pathway of adoption for national implementations across the world.

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation 'impact (pressure)', with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into 'impact (pressure)' categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes-including land degradation, desertification and ecosystem restoration-the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions.

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution.

Global shipping accounts for 13% of global emissions of SO2, which, once oxidized to sulfate aerosol, acts to cool the planet both directly by scattering sunlight and indirectly by increasing the albedo of clouds. This cooling due to sulfate aerosol offsets some of the warming effect of greenhouse gasses and is the largest uncertainty in determining the change in the Earth's radiative balance by human activity. Ship tracks-the visible manifestation of the indirect of effect of ship emissions on clouds as quasi-linear features-have long provided an opportunity to quantify these effects. However, they have been arduous to catalog and typically studied only in particular regions for short periods of time. Using a machine-learning algorithm to automate their detection we catalog more than 1 million ship tracks to provide a global climatology. We use this to investigate the effect of stringent fuel regulations introduced by the International Maritime Organization in 2020 on their global prevalence since then, while accounting for the disruption in global commerce caused by COVID-19. We find a marked, but clearly nonlinear, decline in ship tracks globally: An 80% reduction in SO[Formula: see text] emissions causes only a 25% reduction in the number of tracks detected.

Ecosystem service assessments are increasingly used to support natural resource management, but there is a bias in their application towards terrestrial systems and higher income countries. Tropical marine applications are particularly scarce, especially in SE Asia. Given the growing coastal population and expansion in blue economy sectors in SE Asia, evidence to support effective marine planning, such as ecosystem service assessments, is urgently needed. Data deficiencies for marine systems, especially (but not only) in lower income countries is a significant obstacle for ecosystem service assessments. To overcome this, we develop an ecosystem service potential matrix which combines evidence taken from an extensive literature review together with expert opinion. The matrix includes both natural and modified habitats as the service providing units. The ecosystem service potential for habitats are scored at the macro level (e.g. mangrove) due to insufficient evidence to score micro-habitats (e.g. fringe, basin or riverine mangroves). The majority of evidence is available for biogenic habitats (mangroves, coral reefs and seagrass meadows) with comparatively little for sedimentary habitats. While provisioning, regulating and cultural services are scored, published evidence is more readily available for provisioning and regulating services. Confidence scores, indicating the uncertainty in the ecosystem service potential scores are included in the matrix. To our knowledge this is the first attempt to systematically capture the provision of ecosystem services from tropical marine habitats. Although initially developed for four marine biosphere reserves and protected areas in SE Asia, the generic nature of the evidence included suggests that the matrix constitutes a valuable baseline for marine ecosystem service assessments within SE Asia and provides a robust foundation for development in future work.