Search Results (163)

The Sentinel-3 Ocean and Land Colour Instrument (OLCI) is designed for water monitoring. Its 21-spectral bands serve as the basis for the precise retrieval of water quality parameters. However, its coarse resolution restricts the depiction of the spatial distribution of water quality parameters in small inland water bodies. Spatial–spectral fusion is a common method to address the inherent constraints between the spatial and spectral resolutions of sensors. Central to the popular methods is the deep learning-based method. Nonetheless, deep-learning-based models still face challenges in fusing Sentinel-2 Multi-Spectral Instrument (MSI) and Sentinel-3 OLCI data. Here, we propose a Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN), which effectively integrates OLCI’s spectral advantage and MSI’s spatial resolution. Quantitative evaluation was conducted against five benchmark methods, including traditional approaches (GS, SFIM, MTF-GLP) and deep learning models (SRCNN, UCGAN). The results show that MSA-UGAN achieves the best overall performance: QNR (0.9709) and SSIM (0.9087) are the highest, while SAM (1.1331), spatial distortion (D_S = 0.0389), and spectral distortion (D_λ = 0.0252) are the lowest. This shows that MSA-UGAN can better preserve the spatial details of S2 MSI and the spectral features of S3 OLCI data. Moreover, ERGAS (2.2734) also performs excellently in the comparative experiments. The experiment of Chlorophyll-a inversion using the fused image in Chen Lake revealed a spatial gradient ranging from 3.25 to 19.33 µg/L, with the highest concentrations in the southwestern nearshore waters, likely associated with aquaculture. These results jointly indicate that MSA-UGAN can generate high-spatial-resolution multispectral images, and the fused images can be effectively utilized for water quality monitoring, thereby providing essential data support for the precision management and scientific decision-making regarding inland lakes. Full article

Carbon fluxes from the atmosphere to the ocean and from the ocean surface to the deep ocean are a key pathway in the long-term sequestration of anthropogenic CO₂. Particulate Organic Carbon (POC), which comprises living plankton, detritus and other microscopic organisms, is a very dynamic carbon pool in surface waters, so an ability to assess POC reliably from satellites and autonomous profilers is fundamental to the quantification of the reservoirs and fluxes of carbon within the ocean, and to assess their response to climate change. In situ records from sample filtration during dedicated hydrographic surveys are limited both in terms of spatial coverage and time, so reliable algorithms are required that make use of readily available autonomously collected data that provide much better spatial and temporal coverage. In this paper, algorithms that use ocean colour data from satellites to estimate POC are re-assessed, and then the satellite-derived products are compared with near-surface in situ observations from biogeochemical (BGC) Argo profilers. The satellites and in situ BGC-Argo records match each other to within 30%, but a regional bias persists that may be related to the BGC-Argo fluorometers overestimating the chlorophyll concentration in the Southern Ocean. A simple coarse-resolution regional correction to the observed chlorophyll-a concentration and backscatter coefficient, plus the removal of clear outliers, improves the agreement to approximately 15%. The association of POC with the surface chlorophyll value is so strong that an algorithm based on chlorophyll-a alone provides an almost equally good estimate of POC compared with more complex algorithms that incorporate additional bio-optical variables such as the backscattering coefficient. Full article

Classifying optical water types (OWTs), particularly concerning different phytoplankton bloom types, is critically important because dominant phytoplankton groups govern key marine ecosystem functions and biogeochemical processes, including nutrient cycling and carbon export. This study refines a recent OWT classification method developed for the Second-Generation Global Imager (SGLI), which was originally proposed to discriminate dinoflagellate and diatom blooms. By employing binary logistic regression (bLR) with independent in situ data from Karenia selliformis (dinoflagellate) blooms off the Kamchatka Peninsula and Skeletonema spp. (diatom) blooms in Tokyo Bay, this study establishes more robust and statistically meaningful boundaries between OWTs. The analysis confirms the diagnostic spectral shapes from SGLI data: a trough at 490 nm for K. selliformis blooms and a peak at 490 nm for diatom blooms, validating the consistency of this spectral criterion. The updated method reliably identifies waters dominated by coloured dissolved organic matter and different phytoplankton functional types in mesotrophic waters, and successfully detected a Karenia mikimotoi bloom in the Gulf St. Vincent, South Australia, demonstrating its potential for the global monitoring of red tides. By providing a reliable, satellite-based tool to distinguish between ecologically distinct phytoplankton groups, this refined OWT classification offers a valuable data product to improve the accuracy of marine ecosystem and carbon cycle models, moving beyond bulk chlorophyll-a parameterizations. Full article

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health. Full article

Accurate estimates of Chlorophyll-a (Chl) concentration from satellite observations are critical for understanding large-scale phytoplankton variations, particularly in the context of climate change. However, existing operational Chl retrieval algorithms have been shown to perform poorly in the Southern Ocean (SO). To address this issue, this study proposed improved Chl algorithms tailored to the SO. To this end, three Chl satellite products (MODIS, OC-CCI, and GlobColour) were evaluated against high-precision (high-performance liquid chromatography-derived, HPLC), long-term (1997–2021), and spatially widespread (south of 40°S) in situ Chl observations. Subsequently, OC3M-based empirical algorithms were improved using remote sensing reflectance (R_rs) data. Among the original products, OC-CCI exhibited the best overall performance (R² = 0.36, Slope = 0.36), followed by GlobColour-AVW (R² = 0.27, Slope = 0.21), whereas Aqua-MODIS showed the worst agreement (R² = 0.18, Slope = 0.18) with in situ observations. All three products systematically underestimated Chl concentrations, with average biases of 43% (Aqua-MODIS), 24% (OC-CCI), and 36% (GlobColour-AVW), particularly at high Chl concentrations (>0.2 mg/m³ for Aqua-MODIS and GlobColour-AVW; >0.3 mg/m³ for OC-CCI). The parameter-tuned algorithms significantly reduced these biases to 1% (OC-CCI), 3% (GlobColour-AVW), and a slight overestimation of 2% (Aqua-MODIS). All products showed marked improvements in performance, with R² increasing to 0.68–0.91, slopes approaching 1.0 (0.62–0.92), and notable reductions in MAE (1.39–1.42) and RMSE (1.49–1.51). These results offer enhanced capabilities for Chl retrieval in the data-sparse and optically complex waters of the SO. Full article

Understanding the composition of phytoplankton assemblages and monitoring changes in their diversity is a key factor in the comprehension of global biogeochemical cycles, climate regulation and marine ecosystem health, especially in the context of increasing global warming. Regional empirical algorithms for phytoplankton satellite estimates of size classes (PSCs) and functional types (PFTs) in the Mediterranean Sea have been developed and implemented in the EU Copernicus Marine Service since 2019. Here, we present an update of the PSC and PFT algorithms operational in the Copernicus catalogue since the end of 2024. Results show an overall improvement in the model performance, in line with Copernicus Marine Service requirements focused on the continuous enhancement of the accuracy of distributed biogeochemical variables. Finally, the new algorithms were applied to a time series of over 25 years of satellite data (1998–2024), enabling the identification of key changes in phytoplankton composition at both monthly and basin scales. These insights were made possible by an algorithm re-calibration based on updated and more comprehensive regional pigment ratios. Full article

We present a gradient boosting framework for reconstructing hyperspectral signatures in the visible spectrum (400–700 nm) of satellite-based ocean scenes from limited multispectral inputs. Hyperspectral data is composed of many, typically greater than 100, narrow wavelength bands across the electromagnetic spectrum. While hyperspectral data can offer reflectance values at every nanometer, multispectral sensors typically provide only 3 to 11 discrete bands, undersampling the visible color space. Our approach is applied to remote sensing reflectance ($R_{rs}$) measurements from a set of ocean color sensors, including Suomi-National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), the Ocean and Land Colour Instrument (OLCI), Hyperspectral Imager for the Coastal Ocean (HICO), and NASA’s Plankton, Aerosol, Cloud, Ocean Ecosystem Ocean Color Instrument (PACE OCI), as well as in situ $R_{rs}$ data from National Oceanic and Atmospheric Administration (NOAA) calibration and validation cruises. By leveraging these datasets, we demonstrate the feasibility of transforming low-spectral-resolution imagery into high-fidelity hyperspectral products. This capability is particularly valuable given the increasing availability of low-cost platforms equipped with RGB or multispectral imaging systems. Our results underscore the potential of hyperspectral enhancement for advancing ocean color monitoring and enabling broader access to high-resolution spectral data for scientific and environmental applications. Full article

As part of the Copernicus program of the European Union (EU), the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) are currently operating the Sentinel-3 mission that consists of a constellation of two unites A and B (S3A, S3B). Each unit carries on board an Ocean and Land Colour Instrument (OLCI) that is acquiring moderate-spatial-resolution optical imagery. This article provides a description of the Level-1B radiometry product validation activities of the constellation Sentinel-3A and Sentinel-3B after six years in orbit. Several vicarious calibration methods have been applied independently by three expert groups and the results are compared over different surface target types. All methods agree on the good radiometric performance of both instruments. Although OLCI-A shows brighter Top-of-Atmosphere (TOA) radiance than OLCI-B by about 1–2%, both sensors exhibit very good stability and good image quality. The results are analyzed and discussed to propose a set of vicarious gain coefficients that could be used to align OLCI-A with OLCI-B radiometry time-series. Finally, recommendations for future missions are suggested. Full article

To examine the bio-optical response to coastal upwelling, we measured inherent optical properties (IOPs) and biogeochemical parameters simultaneously off Hainan Island in the northwest part of the South China Sea (SCS) during late summer 2013. Bio-optical relationships between IOPs and phytoplankton were used for calculating vertical profiles of the total chlorophyll a concentration (Chl-a) and the absorption by coloured detrital matter (CDM). These bio-optical properties, which showed distinct horizontal and vertical distributions across the continental shelf, were strongly influenced by upwelling processes, as well as the shelf topography. Phytoplankton biomass and CDM absorption in surface waters showed much higher values along the coast, with their spatial distributions related to topographic variability. Vertical distributions of phytoplankton were characterised by a subsurface chlorophyll maximum (SCM) layer. The strongest SCM (Chl-a = 4.22 mg m⁻³) was observed at 24 m depth in coastal waters near the northeast cape of Hainan Island. The depth of the SCM varied between 16 and 60 m at different stations, appearing to coincide with the isotherm of 22 °C. The SCM depth was inversely correlated with the magnitude of the SCM. Different shapes of Chl-a profiles were observed, which suggested that the vertical distributions of phytoplankton biomass were driven by different environmental factors. Elevated concentrations of CDM were mainly observed near the bottom, which suggest that the benthic nepheloid layer may be an important source of detrital material. The relationship between the absorption coefficient of CDM at 443 nm, a_CDM(443), and Chl-a exhibited distinct differences between waters in upper ocean and in bottom layers, with the threshold depth being modulated by shelf topography. Our results highlight the utility of bio-optical observations with high resolution for better understanding the coupling between physical forcing and biogeochemical variability. Full article

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m⁻³). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. Full article

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. Full article

The Quaternary Mercure basin is a complex fault structure located in the Pollino region of the southern Apennines (Italy). A persistent seismic gap makes the Mercure basin structure one of Italy’s highest seismic risk zones. The southernmost termination of the Mercure basin is the Timpa della Manca fault. The fault’s mirror is characterised by distinctive, lineated, black-coloured striae decorating a cataclasite made of carbonate clasts. These black-coloured striae consist of a mixture of Mn phases, including hollandite, todorokite, birnessite, and orientite, which are associated with goethite and hematite along with minor amounts of phyllosilicates (chlorite, muscovite), quartz, and sursassite. This mineral association and their phase stability suggest that hydrothermal circulating fluids may have mobilised and re-precipitated low-temperature Mn hydrous phases within the shear zone, leaving remnants of higher-temperature minerals. Oceanic crust remnant blocks within the Frido Unit appear to be the most likely source of the Mn. The uniqueness of the Mn striae on the Timpa della Manca fault offers intriguing insights into fluid circulation within the Mercure basin tectonic system, with potential implications for the seismotectonic characteristics of the Pollino region. Full article

Atmospheric correction, the removal of the atmospheric signal from a satellite image, still poses a challenge over optically complex coastal water. Here, we present the first atmospheric correction validation study performed in optically complex Norwegian fjords. We compare in situ reflectance measurements and chlorophyll-a concentrations from Western Norwegian fjords with atmospherically corrected Sentinel-3 Ocean and Land Colour Instrument observations and chlorophyll-a retrievals. Measurements were taken in Hardangerfjord, Bjørnafjord and Møkstrafjord during a bright green coccolithophore bloom in May 2022, and during a period of no apparent discoloration in April 2023. Coccolithophore blooms generally peak in the blue region (490 nm), but spectra measured in this bloom peaked in the green region (559 nm), possibly due to absorption by colored dissolved organic matter (a_CDOM(440) = 0.18 ± 0.01 m⁻¹) or due to high cell counts (up to 15 million cells/L). We tested a wide range of atmospheric correction algorithms, including ACOLITE, BAC, C2RCC, iCOR, L2gen, POLYMER and the SNAP Rayleigh correction. Surprisingly, atmospheric correction algorithms generally performed better during the bloom (average MAE = 1.25) rather than in the less scattering water in the following year (average MAE = 4.67), possibly because the high water-leaving radiances due to the high backscattering by coccolithophores outweighed the adjacency effect. However, atmospheric correction algorithms consistently underestimated water-leaving reflectance in the bloom. In non-bloom matchups, most atmospheric correction algorithms overestimated the water-leaving reflectance. POLYMER appears unsuitable for use over coccolithophore blooms but performed well in non-bloom matchups. Neither BAC, used in the official Level-2 OLCI products, nor C2RCC performed well in the bloom. Nine chlorophyll-a retrieval algorithms, including two algorithms based on neural nets, four based on red and near-infrared bands and three maximum band-ratio algorithms, were also tested. Most chlorophyll-a retrieval algorithms did not perform well in either year, although several did perform within the 70% accuracy threshold for case-2 waters. A red-edge algorithm performed best in the coccolithophore blooms, while a maximum band-ratio algorithm performed best in the following year. Full article

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data. Full article

Water quality indicator algorithms often separate marine and freshwater systems, introducing artificial boundaries and artifacts in the freshwater to ocean continuum. Building upon the Ocean Colour- (OC) and Lakes Climate Change Initiative (CCI) projects, we propose an improved tool to assess the interactions across river–sea transition zones. Fuzzy clustering methods are used to generate optical water types (OWT) representing spectrally distinct water reflectance classes, occurring within a given region and period (here 2016–2021), which are then utilized to assign membership values to every OWT class for each pixel and seamlessly blend optimal in-water algorithms across the region. This allows a more flexible representation of water provinces across transition zones than classic hard clustering techniques. Improvements deal with expanded sensor spectral band-sets, such as Sentinel-3 OLCI, and increased spatial resolution with Sentinel-2 MSI high-resolution data. Regional clustering was found to be necessary to capture site-specific characteristics, and a method was developed to compare and merge regional cluster sets into a pan-regional representative OWT set. Fuzzy clustering OWT timeseries data allow unique insights into optical regime changes within a lagoon, estuary, or delta system, and can be used as a basis to improve WQ algorithm performance. Full article