Abstract. A global in situ dataset for validation of satellite products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, with data starting in 1996, extends to 2025, which is important for the validation of recent algorithms and satellite products. The dataset comprises in situ observations of the following variables: spectral remote-sensing reflectance (rrs), chlorophyll-a concentration ("chla"; HPLC and fluorometric), spectral inherent optical properties (IOPs: algal pigment absorption "aph", detritus plus gelbstoff absorption "adg", and particle backscattering "bbp"), spectral diffuse attenuation coefficient (kd) and total suspended matter (tsm). Data were obtained from multiple archives acquired via open internet services, or from individual projects, acquired directly from data providers. Compared to the previous version (Valente et al., 2022) this release incorporates updates from existing sources and introduces data from 13 additional sources. Notably, AERONET-OC coverage has been significantly expanded (39 sites compared to 11 in v2022) and MOBY Platinum data have been included from 2024. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made to the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The harmonisation procedures also include the application of a bidirectional reflectance distribution function (BRDF) correction to "rrs". The result is a merged table available in text format. Overall, the dataset increased by ~115 %, reaching 319,183 rows, with each row representing a unique station in space and time (cf 148,432 rows in Valente et al., 2022). Records of chlaovera increased by ~31 % (82,543 to 107,922); "rrs" observations nearly doubled +94 % (68,641 → 133,325); IOPs showed substantial expansion, with aph increasing by ~150 % (4,265 to 10,655) and adg by 78 % (1,654 to 2,948). The most pronounced change was for bbp, which increased by ~10,258 % (792 to 82,033), while moderate increases were observed for kd (+30 %; 2,454 to 3,197) and "tsm" (+51 %; 1,546 to 2,334). Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at (Salem et al., 2026).

The Arctic Ocean is undergoing drastic changes in its sea ice cover, but is also optically complex. Observations from summer 2022 across the western Eurasian Basin, show increased colored dissolved organic matter (CDOM) absorption from terrestrial-derived runoff within the Transpolar Drift (TPD) in the Amundsen Basin, extending down to a depth of 100 m. This is in stark contrast to the low-CDOM waters of the Atlantic-influenced Nansen Basin. The higher CDOM absorption increases the diffuse attenuation coefficient by around 35% relative to Atlantic-influenced waters, and changes the spectral quality of light at depth. An irradiance model demonstrates that the decreased light availability can delay or inhibit under-ice phytoplankton blooms, affect zooplankton behavior, and cause enhanced solar heating in the upper 10 m. These findings show that simplified representations of light attenuation in present Earth System Models can hinder accurate assessments of light limitations on Arctic marine ecosystems.

The planktonic copepod, Calanus finmarchicus, plays a pivotal role in the Gulf of Maine (GoM) pelagic food web as a primary food source for many species, including the critically endangered North Atlantic right whale (NARW). Thus, observing C. finmarchicus on a Gulf-wide scale via satellite could be beneficial for understanding changes in the migration patterns of the NARW. This study investigated the application of ocean color remote sensing to detect the surface population levels of C. finmarchicus in the GoM. Using remote sensing reflectance data from the MODIS Aqua sensor, we processed enhanced RGB (eRGB) imagery to detect and quantify the presence of C. finmarchicus, which is identifiable by its red astaxanthin pigment. This study employed a refined approach from the method originally developed off the coast of Norway, which integrates eRGB imagery and radiative transfer modeling to generate optical anomaly maps that are used for quantifying surface C. finmarchicus concentrations in the GoM. We detected surface swarms of C. finmarchicus in the ocean color imagery and estimated their concentrations. However, due to the method’s reliance on astaxanthin/red pigment-based detection, other astaxanthin-rich red/brown plankton were misidentified as C. finmarchicus. While the approach presented is effective for identifying astaxanthin anomalies in ocean color and holds potential for quantifying the surface populations of C. finmarchicus, it requires local knowledge to accurately quantify the C. finmarchicus abundances.

The Barents Sea region is a hotspot for global climate change, with larger‐than‐average temperature increase and sea ice loss. This has led to massive changes in the underwater light environment, which is a key environmental driver for polar marine ecosystems given the extreme seasonal variability. The ecosystem consequences of a changing underwater lightscape remains poorly characterized. Here, we model the changes in availability of photosynthetically available radiation in the Barents Sea from 1979 to 2022, using the underwater irradiance model HEIMDALL, which is developed and validated for use in the Barents Sea and has recently been expanded for use in ice‐covered or ice‐influenced waters. Our results show that the potential growth season for phytoplankton has become more than 1 month longer in large parts of the northern Barents Sea. The largest changes have been in the northeastern part of the region with up to 60 days. The primary environmental driver for light availability is the sea ice concentration (SIC), particularly in spring, with a Kendall rank correlation coefficient = −0.69. Cloud cover and phytoplankton are important second‐order factors ( = −0.4 after controlling for SIC), whereas snow and ice thickness have a negligible effect after controlling for SIC due to strong interdependencies. We observe a partially stepwise change in the 2000s, but sea ice driven interannual variability has been large throughout the period. Sea ice and snow have a limited impact on the spectral quality of light, omitting any impurities or sea ice algae.

The push broom design of an underwater hyperspectral imaging (UHI) instrument makes it possible to measure angle-resolved spectral radiance L(lambda) in a plane. We describe the characterization of a commercial UHI instrument (UHI-4, Ecotone AS, Norway) and the spectral, geometric, and radiometric calibration transfer for measuring L(lambda) in absolute units [mu Wcm(-2 )nm(-1 )sr(-1)]. We present a low-cost instrument characterization approach that is intended to be easily replicated for other users to perform their own calibration transfer. Cross-calibration with a RAMSES-ARC spectroradiometer (TriOS, Germany) in air and in water shows good linear correlation across the observed spectral range and over four orders of magnitude in dynamic range, with the UHI instrument providing higher sensitivity overall.

On Arctic shelves, benthic food-webs are tightly linked to overlying primary production. In the seasonal ice zone, sympagic (ice-associated) primary production can be a major source of carbon for the benthos on productive inflow shelves. However, the role of sympagic organic matter is less well-understood in food webs of heavily ice-covered, less- productive outflow shelves, such as the northeast Greenland shelf. Highly branched isoprenoid biomarkers (HBIs) were used to track the relative distribution of sympagic and pelagic organic matter in the water column, sediments, and benthic fauna of the northeast Greenland shelf and fjords. Low pelagic HBI presence throughout the study area indicated a generally low production by pelagic diatoms (at the time of sampling). This was reflected in the benthos, as ~90% of their assimilated carbon was estimated to come from sympagic sources, indicating a benthic food-web highly reliant on sympagic production. This reliance was higher in coastal areas than on the open shelf, where the potentially higher pelagic productivity and shallower water on banks likely increased contributions of pelagic organic matter. As declining ice cover and reduced production of fast-sinking ice algae projected for Arctic shelves will likely result in weaker coupling between ice algae and the benthos, with possible consequences for future benthic-community structure and function.

Zooplankton are keystone organisms that provide a critical link between primary production and higher-order predators in the marine food web, as well as facilitating the sequestration of carbon within the ocean. In this context, there is considerable interest in the detection of zooplankton swarms from satellite ocean color signals. However, for this to be possible, accurate inherent optical property characterization of key zooplankton groups is first required. In this study, spectral absorption properties of six epipelagic zooplankton groups have been measured using what we believe to be a novel serial addition technique carried out with a Point Source Integrating Cavity Absorption Meter. The measured absorption spectra were used to model the impact of each group on remote sensing reflectance signals and determine a concentration threshold that would generate a distinguishable signal from ocean color data. Results indicate that the spectral shape of absorption did not vary much between species, with most organisms showing a peak at around 480 nm, characteristic of the pigment astaxanthin. Conversely, the magnitude of absorption did vary considerably between species, with larger organisms typically producing stronger absorption signals than smaller species. Thus, detection thresholds also varied for each group measured and were additionally influenced by background constituents within the water column. The calculated concentration thresholds indicate the feasibility of identifying zooplankton from ocean color, but owing to the spectral similarity in absorption properties, knowledge of in situ populations would be required to determine species abundances from satellite signals.

Offshore wind farms are becoming an increasingly common feature in the marine environment as a renewable energy source. There is a growing body of evidence on the effects of wind farms on the seabed and its organisms. However, an important and understudied aspect of site development is the interaction of turbine foundations on the surrounding marine environment. Structures exert significant disturbance on tides, waves and currents; these are visible as optically-distinct, elongate wakes at the sea surface with elevated suspended particulate matter. Despite this, there is uncertainty on the mechanisms that lead to the visible manifestation of wakes at turbine foundations, primarily due to a lack of direct measurements. Here, in situ measurements along with a 15-year time series of satellite images of the Thanet offshore wind farm, located within the Thames Estuary, were used to investigate the formation of visible monopile wakes, and the effects these have on the surrounding water column. We show the optically distinct wakes are near-constant at Thanet; visible in >90% of all satellite images, yet no regional change in sea surface turbidity could be attributed to wind farm construction or operation. Monopile wake in situ water samples and acoustic Doppler current profiler (ADCP) backscatter measurements demonstrated colour change related to elevated sea surface sediment concentration. However, averaged water column measurements of suspended sediment within wakes, and upstream of monopiles, remained consistent. These measurements demonstrate that sediment was redistributed towards surface waters, rather than additional sediment becoming suspended in the wake. ADCP velocity measurements supported a mechanism of sediment lofting towards the surface, with enhanced vertically upwards flow recorded in wakes.

The ArcLight observatory provides an hourly continuous time series of all-sky images providing light climate data (intensity, spectral composition, and photoperiod) from the Arctic (Svalbard at 79°N). Until recently, no complete annual time series of light climate relevant for biological processes has been provided from the high Arctic because of insufficient sensitivity of commercial light sensors during the Polar Night. The ArcLight set up is unique, as it provides both all-sky images and the corresponding integrated spectral irradiance in the visible part of the solar electromagnetic spectrum (E P A R ). Here we present a further development providing hourly diel-annual dynamics from 2020 of the irradiance partitioned into the red, green, and blue parts of the solar spectrum and illustrate their relation to weather conditions, and sun and moon trajectories. We show that there is variation between the RGB proportions of irradiance throughout the year, with the blue part of the spectrum showing the greatest variation, which is dependent on weather conditions (i.e., cloud cover). We further provide an example of the biological impact of these spectral variations in the light climate using in vivo Chl a-specific absorption coefficients of diatoms (mean of six low light acclimated northern-Arctic bloom-forming species) to model total algal light absorption (AQ t o t a l ) and the corresponding fraction of quanta used by Photosystem II (AQPSII) (O 2 production) in RGB bands and the potential impacts on the photoreceptor response, suggesting periods where repair and maintenance functions dominate activity in the absence of appreciable levels of red or green light. The method used here can be applied to light climate data and spectral response data worldwide to give localized ecological models of AQ.

Calanus finmarchicus is a keystone zooplankton species that is commercially harvested and is critical in sustaining many important fisheries in the North Atlantic. However, due to their patchy population distributions, they are notoriously difficult to map using traditional ship-based techniques. This study involves the use of a combined approach of standardized ocean colour imagery and radiative transfer modelling to identify reflectance anomalies potentially caused by surface swarms of C. finmarchicus in the northern Norwegian Sea. Here, we have standardized satellite eRGB imagery that depicts a distinct ‘red’ patch, which coincides with in situ measurements of high surface concentrations of C. finmarchicus. Anomaly mapping using a novel colour matching technique shows a high degree of anomaly within this patch compared to the surrounding waters, indicating the presence of something other than the standard bio-optical model constituents influencing the optics of the water column. Optical closure between modelled and satellite-derived reflectance signals is achieved (and the anomaly is significantly reduced) through the addition of C. finmarchicus absorption into the model. Estimations of the surface concentrations of C. finmarchicus suggest between 80,000 and 150,000 individuals m−3 within the extent of the identified red patch. Furthermore, analysis of the impact of C. finmarchicus absorption on the OC3M algorithm performance points to the potential for the algorithm to over-estimate chlorophyll concentrations if C. finmarchicus populations are present in the surface waters.

Abstract A spectral deconvolution model (SDM) for inversion of light absorption, a(λ) and backscattering, bb(λ), to estimate concentrations of chlorophyll (CHL), colored dissolved organic material (CDOM) and non‐biogenic mineral suspended solids (MSS) in offshore and shelf waters is presented. This approach exploits the spectral information embedded in the ratio bb(λ)/a(λ), without the need to know each parameter separately. The model has been applied to in situ inherent optical properties (IOPs), a(λ) and bb(λ), and to in situ remote sensing reflectance, rrs(λ). CHL, MSS, and CDOM estimates are provided by propagating uncertainties in input IOPs and material‐specific IOPs using a bootstrapping approach. Application of the SDM to a data set collected in the Ligurian Sea provides Mean Average Errors (MAE) of <0.7 mg m−3 for CHL, <0.02 m−1 for CDOM, and <0.2 g m−3 for MSS. The SDM is found to perform as well as, or in some cases better than, single parameter algorithms and other semi‐analytical algorithms (SAA) for each parameter for the Ligurian Sea data set. The SDM CHL product is tested using the NOMAD, Case 1 dominated, global data set and found to perform consistently with the quasi‐analytical algorithm (Lee et al., 2002, https://doi.org/10.1364/ao.41.005755) but with slightly poorer performance than standard OCx algorithms. However, the additional estimates of CDOM and MSS provided by the SDM suggest that the approach may be particularly useful for Case 2 waters. Successful retrieval of constituent concentrations with uncertainties suggests good potential to adapt this technique for satellite remote sensing.

Estimation of chlorophyll (CHL) using ocean colour remote sensing (OCRS) signals in coastal waters is difficult due to the presence of two other constituents altering the light signal: coloured dissolved organic material (CDOM) and mineral suspended sediments (MSS). Artificial neural networks (NNs) have the capacity to deal with signal complexity and are a potential solution to the problem. Here NNs are developed to operate on two datasets replicating MODIS Aqua bands simulated using Hydrolight 5.2. Artificial noise is added to the simulated signal to improve realism. Both datasets use the same ranges of in water constituent concentrations, and differ by the type of logarithmic concentration distributions. The first uses a Gaussian distribution to simulate samples from natural water conditions. The second uses a flat distribution and is intended to allow exploration of the impact of undersampling extremes at both high and low concentrations in the Gaussian distribution. The impact of the concentration distribution structure is assessed and no benefits were found by switching to a flat distribution. The normal distribution performs better because it reduces the number of low concentration samples that are relatively difficult to resolve against varying concentrations of other constituents. In this simulated environment NNs have the capacity to estimate CHL with outstanding performance compared to real in situ algorithms, except for low values when other constituents dominate the light signal in coastal waters. CDOM and MSS can also be predicted with very high accuracies using NNs. It is found that simultaneous retrieval of all three constituents using multitask learning (MTL) does not provide any advantage over single parameter retrievals. Finally it is found that increasing the number of wavebands generally improves NN performance, though there appear to be diminishing returns beyond ∼8 bands. It is also shown that a smaller number of carefully selected bands performs better than a uniformly distributed band set of the same size. These results provide useful insight into future performance for NNs using hyperspectral satellite sensors and highlight specific wavebands benefits.

The optical chain and logger (OptiCAL) is an autonomous ice-tethered observatory equipped with multiple light sensors for mapping the variation of light with depth. We describe the instrument and present an ensemble calibration for downwelling irradiance E P A R in [µm o l m -2 s -1]. Results from a long-term deployment in the Arctic Ocean demonstrate that the OptiCAL can cover the high dynamic range of under-ice light levels from July to November and produce realistic values in terms of magnitude when compared to modeled surface irradiance. Transient features of raised light levels at specific depths associated with nearby leads in the ice underline the importance of depth-resolved light measurements.

Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl algorithm performance. Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results for northwest European shelf seas over the 2002–2020 period are shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and are tested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleigh corrected TOA reflectances (RC). In each case, a NN architecture consisting of 3 layers of 15 neurons improved performance and data availability compared to current state-of-the-art algorithms used in the region. The NN operating on TOA reflectance outperformed BOA and RC versions. By operating on TOA reflectance data, the NN approach overcomes the common but difficult problem of atmospheric correction in coastal waters. Moreover, the NN provides data for regions which other algorithms often mask out for turbid water or low zenith angle flags. A distinguishing feature of the NN approach is generation of associated product uncertainties based on multiple resampling of the training data set to produce a distribution of values for each pixel, and an example is shown for a coastal time series in the North Sea. The final output of the NN approach consists of a best-estimate image based on medians for each pixel, and a second image representing uncertainty based on standard deviation for each pixel, providing pixel-specific estimates of uncertainty in the final product.

Arctic marine ecosystems are strongly influenced by the extreme seasonality of light in the region. Accurate determination of light is essential for building a comprehensive understanding of the dynamics of animal and aquatic algae populations. Current approaches to underwater light field parameterisations rely upon shortwave radiation (300–3000 nm) estimates from satellites or surface radiometry measurements to populate full radiative transfer software. Due to the inaccessibility of many regions in the Arctic, measured data is not widely available. This study presents a model of spectrally resolved underwater light in ice‐free conditions in the Barents Sea. Given a location and time, the model accounts for downwelling spectral irradiance in the photosynthetically active radiation (PAR, 400–700 nm) range ( E D PAR ) at the ocean surface from solar, lunar, and galactic light sources, modulated by local cloud cover. We demonstrate the ability to extend over the full year into the period of Polar Night, validated in both broadband PAR and spectral domains. Using a bio‐optical model of diffuse attenuation developed for the Barents Sea, we show accurate calculations to depth for inhomogeneous water columns over a spatial‐temporal range, validated against time series irradiance data from the ArcLight observatory in Ny‐Ålesund, Svalbard and in‐situ irradiance sensors deployed in the Barents Sea. Finally, in comparison to state‐of‐the‐art radiative transfer models, averaged over the water column we demonstrate a typical mean absolute error of <1 μmol m −2 s −1 in E D PAR for overcast conditions (<6 μmol m −2 s −1 for clear‐sky) and reduced execution time of factor 20.

Marine Scotland is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in the seawater quality monitoring. Currently, the Chl observation is mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper, we explore to implement advanced machine learning techniques to automatically estimate the Chl via the historic time series of ocean colour remote sensing data during from July 2002 to September 2019.

Half of globally significant megacities are situated near the coast, exposing urban marine ecosystems to multiple stressors such as waste-water discharge containing a host of organic and inorganic pollutants, air and noise pollution. In addition to these well recognized sources, artificial light at night (ALAN) pollution is inseparable from cities but poorly quantified in marine ecosystems to date. We have developed a time- and wavelength-resolving hydrological optical model that includes solar (daylight and twilight components), lunar and ALAN source terms and propagates these spectrally through a tidally varying water column using Beer’s Law. Our model shows that for 8 globally distributed cities surface ALAN dosages are up to a factor of 6 greater than moonlight, as ALAN intensities vary little throughout the night, over monthly or seasonal cycles. Moonlight only exceeds ALAN irradiances over the ±3-day period around full moon, and particularly during the brightest moons (mid-latitude winter, at zenith). Unlike the relatively stable surface ALAN, underwater ALAN varies spectrally and in magnitude throughout the night due to tidal cycles. The extent of ALAN in-water attenuation is location-specific, driven by the season, tidal range and cycle, and water clarity. This work highlights that marine ALAN ecosystem pollution is a particularly acute global change issue near some of the largest cities in the world.

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi: 10.1594/PANGAEA.854832 (Valente et al., 2015).