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Ocean colour (OC) remote sensing benefits society by providing continuous biological and ecological parameters relevant to sustainable marine resource exploitation. It enhances our understanding of climate change and allows us to monitor oceanographic phenomena over various scales of variability. However, significant data gaps occur daily due to cloud cover, atmospheric correction failures, sun-glint contamination, and satellite coverage limitations. Level 4 (L4) gap-free images are generally created by averaging over specific periods (e.g., weekly, monthly, seasonal) or re-gridding data with coarser resolution to overcome these limitations. These approaches, however, often fail to capture anomalous events or fine-scale resolution processes, calling for more advanced methods. The Data Interpolating Empirical Orthogonal Function (DINEOF) method has proved effective in reconstructing missing OC data and capturing smaller-scale features in noisy fields. To the best of authors knowledge, DINEOF is here used for the first time to interpolate multispectral Remote Sensing Reflectance (Rrs) to produce a consistent and gap-free L4 Rrs dataset, minimizing errors in inferred ocean products, such as Chlorophyll-a (Chl), the most widely used proxy for phytoplankton biomass. Specifically, using a multivariate approach, we assessed the DINEOF technique’s capability to reconstruct Rrs, focusing on six bands (412, 443, 490, 510, 555, and 670 nm) and validating the results using extensive in situ datasets. Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. We anticipate further improvements in L4 Rrs accuracy using richer spectral information from upcoming hyperspectral satellite missions. This study highlights the effectiveness of using Rrs as a standalone dataset for DINEOF interpolation. Operationally, it can derivate various gap-free and consistent biogeochemical parameters with reduced uncertainty, thus providing a more reliable and versatile method.

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.

The Ocean Colour Thematic Assembly Centre (OCTAC) of the Copernicus Marine Service delivers state-of-the-art Ocean Colour core products for both global oceans and European seas, derived from multiple satellite missions. Since 2015, the OCTAC has provided global and regional high-level merged products that offer value-added information not directly available from space agencies. This is achieved by integrating observations from various missions, resulting in homogenized, inter-calibrated datasets with broader spatial coverage than single-sensor data streams. OCTAC enhanced continuously the basin-level accuracy of essential ocean variables (EOVs) across the global ocean and European regional seas, including the Atlantic, Arctic, Baltic, Mediterranean, and Black seas. From 2019 onwards, new EOVs have been introduced, focusing on phytoplankton functional groups, community structure, and primary production. This paper provides an overview of the evolution of the OCTAC catalogue from 2015 to date, evaluates the accuracy of global and regional products, and outlines plans for future product development.

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m2. The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales.

Abstract Taking into account trophic relationships in seagrass meadows is crucial to explain and predict seagrass temporal trajectories, as well as for implementing and evaluating seagrass conservation policies. However, this type of interaction has been rarely investigated over the long term and at the scale of the whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass species, Zostera noltei, and a herbivorous bird feeding on this seagrass species, the migratory goose Branta bernicla bernicla, were investigated using an original combination of long‐term Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs) such as seagrass abundance and phenology were measured from 1985 to 2020 using high‐resolution satellite remote sensing over Bourgneuf Bay (France), and cross‐analysed with in situ measurements of bird population size during the goose wintering season. Our results showed a mutual relationship between seagrass and Brent geese over the four last decades, suggesting that the relationship between the two species extends beyond a simple grass—herbivore consumptive effect. We provided evidence of two types of interactions: (i) a bottom‐up control where the late‐summer seagrass abundance drives the wintering population of herbivorous geese and (ii) an indirect top‐down effect of Brent goose on seagrass habitat, where seagrass development is positively influenced by the bird population during the previous wintering season. Such a mutualistic relationship has strong implications for biodiversity conservation because protecting one species is beneficial to the other one, as demonstrated here by the positive trajectories observed from 1985 to 2020 in both seagrass and bird populations. Importantly, we also demonstrated here that exploring the synergy between EO and in situ bird data can benefit seagrass ecology and ecosystem management.

The Galician estuaries are Europe’s foremost supplier of mussels, generating millions of euros annually and offering substantial employment opportunities for its population. One of the most critical threats to shellfish production is the occurrence of harmful algal blooms (HABs), contaminating bivalves with phytotoxins. To successfully tackle this problem, there needs to be a collaborative effort between the scientific community and decision-makers to establish a dynamic and effective monitoring system. This could enable early warnings and preventive actions to avert the loss of millions of tons of shellfish. Remote sensing, despite its limitations, requires commitment and effort by experts to devise effective methods for detecting target optical constituents mixed with other undesired target but that exhibit strong signals. Therefore, the essential necessity arises to identify approaches for mitigating the shortcomings of the undertaken efforts. The objective of this study is to assess the main environmental drivers of potential harmful genera (Pseudo-nitzschia, Dinophysis, Alexandrium, and Gymnodinium) in the Rías Baixas from 2015 to 2022, developing regression-based models and customizing Generalized Additive Models (GAMs) to investigate their spatial-temporal dynamics. Risk-susceptible bloom zones were identified in the river mouth of the Ría Pontevedra and from the center to offshore of the Ría Vigo. Early upwelling events triggered peaks in chlorophyll-a (Chl-a), driving Dinophysis grazing on phytoplankton communities mainly dominated by diatoms, flagellates, and ciliates. Subsequently, the upwelling intensity favoring Pseudo-nitzschia or Alexandrium minutum growth as larger diatoms declined. A. minutum exhibited elevated incidence over the past five years. Gymnodinium catenatum presented a rare occurrence across the three studied estuaries, which limited the assessment of its spatial dynamics in the region. This study emphasizes the need to integrate remote sensing evaluation of high-risk bloom areas (July to September), in-situ cell count collection, and enhanced efforts for forecasting future critical occurrences of HABs.

The Abrolhos Bank harbors the richest coral reef ecosystem in the South Atlantic Ocean. It exhibits unique geomorphologic structures, is localized in shallow depths, and is divided into two reef regions with an inner arc close to the coast (3–20 m depth) and an outer deeper arc (5–30 m depth). This study aims to describe some bio-optical properties of the Abrolhos Bank waters and to evaluate the performance of the inversion Hyperspectral Optimization Processing Exemplar (HOPE) model, developed to retrieve optical properties in shallow waters, in the region. To this end, measurements at 75 stations during two field campaigns conducted during the 2013 and 2016 wet seasons were analyzed, and the HOPE model was applied to both in situ remote sensing reflectance (Rrs) spectra and PRecursore IperSpettrale della Missione Applicativa (PRISMA) imagery. Significant differences in optical and biological properties were found between the two arcs. The empirical relationships between chlorophyll-a concentration (Chl-a) and absorption coefficient of phytoplankton at 440 nm (aphy(440)) diverged from Bricaud’s models, suggesting differences in phytoplankton diversity and cell size. In both arcs, total non-water absorption coefficient at 440 nm (aT-w(440)) was dominated by colored dissolved organic matter (CDOM) by ∼60%. Absorption coefficient by CDOM (acdom) presented a higher variability within the outer arc, with the lowest contribution from non-algal particles (NAPs), and the spectral slopes of aCDOM resembled those of the inner arc. The spectral slopes of the NAP absorption coefficient suggested a dominance by organic rather than mineral particles that probably originated from biological production. The HOPE model applied to in situ Rrs performed satisfactorily for depth in the Abrolhos Bank waters, although retrievals of aphy(440), CDOM plus NAP (adg(440)) and aT-w(440) were underestimated with a relative bias of −27.9%, −32.1% and −45.8%, respectively. The HOPE model retrievals from the PRISMA image exhibited low aphy(440) values over the whole scene and the highest adg(440) values in the Caravelas river plume. Very shallow depths (≤3 m), bottom substrate reflectance used as input in the HOPE model, model parametrization associated with the water complexity in the study site, and uncertainties associated to Rrs measurements used as input might be responsible for differences found when comparing HOPE retrievals with in situ measurements.

Rugulopteryx okamurae is a species of brown macroalgae belonging to the Dictyotaceae family and native to the north-western Pacific. As an Invasive Alien Species (IAS), it was first detected in the Strait of Gibraltar in 2015. Since then, R. okamurae has been spreading rapidly through the submerged euphotic zone, colonizing from 0 to 50 m depth and generating substantial economic and environmental impacts on the Andalusian coasts (southern Spain). More than 40% of marine IAS in the European Union (EU) are macroalgae, representing one of the main threats to biodiversity and ecosystem functioning in coastal habitats. This study presents a monitoring pilot of beached R. okamurae and fresh R. okamurae down to 5 m depth in Tarifa (Cadiz, Spain), combining multispectral remote sensing data collected by sensors on-board Unmanned Aerial Vehicles (UAVs) and satellites, and how this information can be used to support decision-making and policy. We used an UAV flight carried out at Bolonia beach (Tarifa, Spain) on 1st July 2021 and Sentinel-2 (S2) and Landsat-8 (L8) image acquisitions close to the drone flight date. In situ data were also measured on the same date of the flight, and they were used to train the supervised classification Super Vector Machine (SVM) method based on the spectral information obtained for each substrate cover. The results obtained show how multispectral images allow the detection of beached R. okamurae, and the classification accuracy for water, land vegetation, sand and R. okamurae depending on the image resolution (8.3 cm/pixel for UAV flight, 10 m/pixel for S2 and 30 m/pixel for L8). While the UAV imagery precisely delimited the area occupied by this macroalgae, satellite data were capable of detecting its presence, and able to generate early warnings. This study demonstrates the usefulness of multispectral remote sensing techniques to be incorporated in continuous monitoring programmes of the marine IAS R. okamurae in coastal areas. This information is also key to supporting regional, national and European policies in order to adapt strategic management of invasive marine macrophytes.

Seagrass meadows are monitored in the frame of several environmental programs worldwide, including the Water Framework Directive (WFD), to evaluate the ecological status of European coastal and transitional water bodies. The large size, spatial complexity, and interannual variability of seagrass ecosystems significantly challenge field monitoring. In this study, a multi-mission satellite time-series was used to estimate long-term changes in seagrass status in a macrotidal system dominated by Zostera noltei, at Bourgneuf Bay (French Atlantic coast). Metrics of seagrass extent and density were obtained from Earth Observation (EO) using validated and inter-calibrated Landsat, SPOT and Sentinel2 data from 1985 to 2020. The information provided by satellite data made it possible to compute and compare several seagrass indicators currently in use in several European countries (France, Portugal and UK) within the WFD. Both the seagrass extent and meadow-averaged density displayed increasing trends since 1985. A time-series of merged observations from various satellites revealed a high degree of interannual variability in seagrass extent, with abrupt losses (up to 50% within one year) alternating with periods of slow recovery (typically 4–6 years). The seagrass meadow which was in a moderate status (sensu the WFD) in the 1980s, achieved an overall recurrent good or high status since the mid-1990s. Altogether, the methods and results presented here demonstrated that EO is a reliable source of information for mapping and assessing the status of intertidal seagrass, complementing in situ measurements by providing long-term, spatial view and standardized observation framework. We recommend the systematic use of EO time-series in complement to traditional field measurements in seagrass monitoring programs such as the WFD.

Quantum yield of photosynthesis (ϕ) expresses the efficiency of phytoplankton carbon fixation given certain amount of absorbed light. This photophysiological parameter is key to obtaining reliable estimates of primary production (PPsat) in the ocean based on remote sensing information. Several works have shown that ϕ changes temporally, vertically, and horizontally in the ocean. One of the primary factors ruling its variability is light intensity and thereby, it can be modeled as a function of Photosynthetically Available Radiation (PAR). We estimated ϕ utilizing long time-series collected in the North Subtropical Oligotrophic Gyres, at HOT and BATS stations (Pacific and Atlantic oceans, respectively). Subsequently the maximum quantum yield (ϕm) and Kϕ (PAR value at half ϕm) were calculated. Median ϕm values were ~0.040 and 0.063 mol C mol photons−1 at HOT and BATS, respectively, with higher values in winter. Kϕ values were ~8.0 and 10.8 mol photons m−2 d−1 for HOT and BATS, respectively. Seasonal variability in Kϕ showed its peak in summer. Dynamical parameterizations for both regions are indicated by their temporal behaviors, where ϕm is related to temperature at BATS while Kϕ to PAR, in both stations. At HOT, ϕm was weakly related to temperature and its median annual value was used for the whole data series. Differences in the study areas, even though both belong to Subtropical Gyres, reinforced the demand for regional parameterizations in PPsat models. Such parameterizations were finally included in a PPsat model based on phytoplankton absorption (PPsat−aphy−based), where results showed that the PPsat−aphy−based model coupled with dynamical parameterization improved PPsat estimates. Compared with PPsat estimates from the widely used VGPM, a model based on chlorophyll concentration (PPsat−chl−based), PPsat−aphy−based reduced model-measurement differences from ~62.8 to ~8.3% at HOT, along with well-matched seasonal cycle of PP (R2 = 0.76). There is not significant reduction in model-measurement differences between PPsat−chl−based and PPsat−aphy−based PP at BATS though (37.8 vs. 36.4%), but much better agreement in seasonal cycles with PPsat−aphy−based (R2 increased from 0.34 to 0.71). Our results point to improved estimation of PPsat by parameterized quantum yield along with phytoplankton absorption coefficient at the core.

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.