Bio-optical Properties Research Articles

Influence of the Kara Sea Surface Layer Parameters on the Accuracy of Chlorophyll-a Concentration Estimation by the Bio-optical Algorithms

The results of the hydro-optical model setup in the HydroLight software for three stations in the Kara Sea characterized by different vertical distributions of the values of bio-optical properties are presented. The selected distributions are typical for the areas of the Kara Sea influenced by the river runoff. The stations are located inside, outside and at the boundary of the surface desalinated layer. The model reproduces the spectra of remote sensing reflectance, vertical profiles of light attenuation coefficient of sea water and photosynthetically available radiation measured in expeditions with good accuracy (average relative error less than 10 %). The results obtained made it possible to evaluate the accuracy of different algorithms for determining chlorophyll-a concentration in the range of values typical for the Kara Sea. For the same purpose, similar calculations were performed for different values of the absorption coefficient of colored dissolved organic matter. It is shown that the IO RAS regional algorithm allows estimation of chlorophyll-a concentration in the range of values exceeding 0.5 mg/m3 with a significantly smaller relative error (less than 50 %) than the semi-analytical GIOP algorithm (more than 100 %). At the same time, variations in the yellow matter absorption coefficient have a significantly smaller impact on the results of the regional algorithm. The significant influence of the initial approximation of chlorophyll-a concentration values on the results of the semi-analytical GIOP algorithm makes it unsuitable for use in the area of influence of river runoff in the Kara Sea. A numerical method for determining the thickness of the layer forming 90 % of the water-leaving radiance was implemented, which will allow a more detailed assessment of the influence of surface layer parameters on the accuracy of chlorophyll-a concentration estimation in the Kara Sea based on satellite ocean color data in the future.

Bio-optical variability of particulate matter in the Southern Ocean

The composition and size distribution of particles in the ocean control their optical (scattering and absorption) properties, as well as a range of biogeochemical and ecological processes. Therefore, they provide important information about the pelagic ocean ecosystem’s structure and functioning, which can be used to assess primary production, particle sinking, and carbon sequestration. Due to its harsh environment and remoteness, the particulate bio-optical properties of the Southern Ocean (SO) remain poorly observed and understood. Here, we combined field measurements from hydrographic casts from two research voyages and from autonomous profiling floats (BGC-Argo) to examine particulate bio-optical properties and relationships among several ecologically and optically important variables, namely the phytoplankton chlorophyll a concentration (Chl), the particulate absorption coefficient (ap), the particulate backscattering coefficient (bbp), and the particulate organic carbon (POC) concentration. In the clearest waters of the SO (Chl < 0.2 mg m−3), we found a significant contribution to absorption by non-algal particles (NAP) at 442 nm, which was up to 10 times greater than the absorption by phytoplankton. This makes the particulate bio-optical properties there remarkably different from typical oceanic case 1 water. A matchup analysis confirms the impact of this larger NAP absorption on the retrieval of Chl from satellite ocean colour observations. For waters with Chl > 0.2 mg m−3, no significant differences are observed between the SO and temperate waters. Our findings also demonstrate consistency in predicting phytoplankton carbon from either Chl or bbp, suggesting that both methods are applicable in the SO.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and neural network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03–4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Biological and optical properties of Baltic surface waters and sea-land interaction – searching for interdependencies

In June 2015, aboard the r/v Akademik Ioffe, a study was conducted on the surface zooplankton community in selected Baltic Sea basins along the Arkona Basin - Gulf of Gdansk route. Samples were collected using a 100 μm mesh plankton net at depths of 2–0 m. Additionally, optical and physical properties were assessed through remote (lidar) and in-situ (CTD and surface microlayer seawater sampling) measurements to provide a foundation for biological analysis.The zooplankton included 19 taxa from holoplanktonic Copepoda, Diplostraca, Rotifera, and meroplanktonic stages of Bivalvia, Gastropoda, Polychaeta, Cirripedia, and Pisces. Zooplankton abundance ranged from 37399 ind. m−3 (Slupsk Furrow) to 267744 ind. m−3 (Bornholm Basin), with copepods being the most numerous groups. While the zooplankton community composition was relatively stable across the study area, their distribution varied. The most diverse station was Slupsk Furrow, with Copepoda, Diplostraca, Rotifera, and meroplankton making up approximately 40%, 25%, 20%, and 5%, respectively. In contrast, Bornholm Basin had the highest zooplankton numbers, mainly dominated by copepods (90%) with minor contributions from other groups.Fluorescence properties of surface microlayer organic matter were assessed by measuring the intensity ratio of the primary fluorophores (A, C, M, and T) of dissolved organic matter molecules, represented as (M + T)/(A + C). This indicated a mixed marine-terrestrial nature of organic matter in stations west of the Slupsk Furrow. Stations at the Arkona Basin and the Slupsk Furrow had the highest ratio values, suggesting a significant marine organic matter source. In contrast, stations east of the Slupsk Furrow exhibited lower (M + T)/(A + C) ratios, indicating a dominant terrestrial origin for organic matter. Lidar results further supported the division of the study area into two regions based on bio-optical properties: a western region (Arkona Basin, Bornholm Basin, and Slupsk Furrow) and an eastern region (Gotland Basin and Gdansk Basin). Moreover, the variability in zooplankton community structure and distribution is closely correlated with the water hydrographic and optical characteristics. We can therefore conclude that all the water properties that we have studied are a derivative of the interaction of sea and land.

Determination of biogeochemical properties in sea waters using the inversion of the three-stream irradiance model

Inversion models, in the context of oceanography, relate the observed ocean color to the concentrations of the different biogeochemical components present in the water of the ocean. However, building accurate inversion models can be quite complex due to the many factors that can influence the observed ocean color, such as variations in the composition or the optical properties of biogeochemical products. Here we assess the feasibility of the inversion approach, by implementing the three-stream light inversion model in a one-dimensional water column configuration, represented at the BOUSSOLE site in the northwestern Mediterranean Sea. Moreover, we provide a comprehensive sensitivity analysis of the model’s skill by perturbing the parameters of the bio-optical properties and phytoplankton physiology. Analysis of the inversion indicates that the model is able to reconstruct the variability of the optical constituents. Results indicate that chlorophyll-a and coloured dissolved organic matter play a major role in light modulation. The sensitivity analysis shows that the parameterization of the ratio of chlorophyll-a to carbon is important for the performance of the inversion model. A coherent inversion model, as presented, can be used as an observational operator to assimilate remote sensing reflectance.

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

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.

Short-term time-series observations of phytoplankton light-absorption and productivity in Prydz Bay, coastal Antarctica

The optical characteristics of coastal Antarctic waters exhibit complexity due to the dynamic hydrography influenced by meltwater intrusion, which alters nutrient levels, thermohaline structure, and optically active substances (OAS) regimes. Studies on bio-optical variability and its implications on phytoplankton productivity (PP) are scanty in coastal polar regions. On this backdrop, time-series measurements (72 h at 6 h intervals) of bio-optical properties such as phytoplankton biomass (chlorophyll-a), absorption (aph), and total suspended matter (TSM) concurrently with PP were measured to understand their interplay and variability in relation to the ambient physicochemical settings in the under-sampled Prydz Bay, coastal Antarctica. Our findings revealed thermohaline stratification within the bay, likely attributed to the inflow of less saline meltwater from nearby glaciers and minimal wind activity. The consistent presence of sub-surface chlorophyll maximum (SCM) beneath the stratified layer underscored the light-acclimatization response of shade-adapted phytoplankton. Surface waters exhibited higher TSM compared to deeper layers, indicating glacial melt influence, while the depth of the sunlit layer remained relatively stable, suggesting limited water mass movement and/or variability in OAS at the study site. An inverse relation between chlorophyll-a and chlorophyll-a-specific phytoplankton light absorption (a*ph(λ)) manifested ‘pigment package effect’ within the prevailing phytoplankton community, implying reduced light-absorption efficiency and consequent lower PP. Compared to chlorophyll-a, the phytoplankton light absorption (aph(λ)) emerged as a better proxy for explaining PP variability. Nutrient availability was not limiting, which was conducive to micro (large) phytoplankton growth. Classification of phytoplankton size classes (micro, nano, and pico) based on the B/R ratio (aph at Blue (443 nm)/Red (676 nm) region) confirmed the dominance of larger (micro) phytoplankton that are more susceptible to package effect, thus have implications on reduced PP potential of this polar marine ecosystem.

Bio-Optical Properties near a Coastal Convergence Zone Derived from Aircraft Remote Sensing Imagery and Modeling

Bio-optical and physical measurements were collected in the Mississippi Sound (Northern Gulf of Mexico) during the spring of 2018 as part of the Integrated Coastal Bio-Optical Dynamics project. The goal was to examine the impact of atmospheric and tidal fronts on fine-scale physical and bio-optical property distributions in a shallow, dynamic, coastal environment. During a 25-day experiment, eight moorings were deployed in the vicinity of a frontal zone. For a one-week period in the middle of the mooring deployment, focused ship sampling was conducted with aircraft and unmanned aerial vehicle overflights, acquiring hyperspectral optical and thermal data. The personnel in the aircraft located visible color fronts indicating the convergence of two water masses and directed the ship to the front. Dye releases were performed on opposite sides of a front, and coincident aircraft and unmanned aerial vehicle overflights were collected to facilitate visualization of advection/mixing/dispersion processes. Radiometric calibration of the optical hyperspectral sensor was performed. Empirical Line Calibration was also performed to atmospherically correct the aircraft imagery using in situ remote sensing reflectance measurements as calibration sources. Bio-optical properties were subsequently derived from the atmospherically corrected aircraft and unmanned aerial vehicle imagery using the Naval Research Laboratory Automated Processing System.

Evaluation of Ocean Color Algorithms to Retrieve Chlorophyll-a Concentration in the Mexican Pacific Ocean off the Baja California Peninsula, Mexico

Mathematical algorithms relate satellite data of ocean color with the surface Chlorophyll-a concentration (Chl-a), a proxy of phytoplankton biomass. These mathematical tools work best when they are adapted to the unique bio-optical properties of a particular oceanic province. Ocean color algorithms should also consider that there are significant differences between datasets derived from different sensors. Common solutions are to provide different parameters for each sensor or use merged satellite data. In this paper, we use satellite data from the Copernicus merged product suite and in situ data from the southernmost part of the California Current System to test two widely used global algorithms, OCx and CI, and a regional algorithm, CalCOFI2. The OCx algorithm yielded the most favorable results. Consequently, we regionalized it and conducted further testing, leading to significant improvements, especially in eutrophic and oligotrophic waters. The database was then separated according to (a) dynamic boundaries in the area, (b) bio-optical properties, and (c) climatic conditions (El Niño/La Niña). Regional algorithms were obtained and tested for each partition. The Chl-a retrievals for each model were tested and compared. The best fit for the data was for the regional algorithms that considered the climatic conditions (El Niño/La Niña). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Niño/La Niña events on the bio-optical properties of the area.

Assessment of global detection capability of oceanographic lidar

The profiling of marine bio-optical properties by oceanographic lidar is of great significance to the monitoring of the marine environment and the study of the global carbon cycle. In this study, the global detection capability of oceanographic lidar is analyzed with different specifications. To evaluate the detection performance, a simulator on multi-platform is developed. Based on the quasi-single elastic scattering approximation lidar equation and the bio-optical models, the simulator can generate different kinds of outputs, such as lidar signal profiles, signal-to-noise ratio profiles, maximum detectable depth distributions. The optical properties profiles of the global ocean could be derived from the global chlorophyll-a concentration profile data set, which is used as the input to the simulator. The simulation results are compared with the measurements from airborne lidar in the South China Sea, which verifies the reliability of the simulator. The global detection capability of spaceborne lidar with different power aperture products is simulated, and the influence of laser wavelength is analyzed. The results show that, for the simulated spaceborne lidar with typical specifications at 486 nm, there are probabilities ranging from 0.66 to 0.85 that the upper boundary of the thermocline can be detected on a global scale. In the cleanest open ocean, the lidar has a good capability to penetrate the upper boundary of the thermocline, while it cannot penetrate the subsurface chlorophyll maximum layers, in which the global penetration ratio is 0.93 under the simulation specifications at 486 nm. Finally, the influence of repetition frequency is analyzed, and the results show that reducing the pulse repetition frequency at the same average power can effectively reduce the background light of cumulative profiles and improve the signal-to-noise ratio.

Cross-Satellite Atmospheric Correction for Consistent Remote Sensing Reflectance from Multiple Ocean Color Satellites: Concept and Demonstrations

Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites, thereby enhancing spatial coverage and extending temporal spans. However, due to factors such as sensor specifics and separate data processing algorithms, bio-optical properties (e.g., remote sensing reflectance, R rs ) from different ocean color missions exhibit varying discrepancies in oceanic, coastal, and inland waters. Here, we introduce a cross-satellite atmospheric correction (CSAC) scheme, which could greatly improve the consistency of R rs products between MODIS-Aqua and other satellite ocean color missions. Specifically, using an inclusive high-quality R rs dataset of oceanic waters obtained from MODIS-Aqua as the reference, and as an example, top-of-atmosphere reflectance from SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) is directly processed to MODIS-Aqua-equivalent R rs ( R rs MA − eqv ) via CSAC. As a demonstration, for independent space-time matched measurements between MODIS-Aqua and SeaWiFS, the mean absolute percent difference ( MAPD ) between R rs MA − eqv and MODIS-Aqua R rs ranges from 5.9% to 22.2% across wavelengths from 412 to 667 nm. In contrast, the MAPD values between the NASA standard SeaWiFS and MODIS-Aqua R rs products range from 10.1% to 55.1% for the same spectral bands. These results highlight the potential of CSAC in obtaining consistent R rs products and, subsequently, R rs -derived bio-optical properties, from various ocean color satellites, facilitating extensive and long-term ocean color observations of the global ocean.

Links between Land Cover and In-Water Optical Properties in Four Optically Contrasting Swedish Bays

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) in the respective catchment of each bay. The optical properties were measured in situ over the last decade by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8/OLI data. The absorption coefficient of colored dissolve organic matter (CDOM) at 440 nm, aCDOM (440), was significantly correlated to Wetlands which may act as sources of CDOM, while Developed areas (Agricultural and Urban classes) were negatively correlated. The Agriculture class was also negatively related to suspended particulate organic matter (SPOM), whilst Coniferous Forests and Mixed Forests as well as Meadows were positively correlated. SPOM seems thus to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land use and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale.

Ocean Colour Atmospheric Correction for Optically Complex Waters under High Solar Zenith Angles: Facilitating Frequent Diurnal Monitoring and Management

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρrc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R2 ≥ 0.90, RMSD ≤ 0.0034 sr−1); (ii) the model had a high validation accuracy with an independent dataset (R2 = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day.

Vertically Resolved Global Ocean Light Models Using Machine Learning

The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., Rrs, PAR, and kd(490)) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m−2 s−1 for PAR and 0.04, 0.08, and 0.09 W m−2 nm−1 for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications.

Impact of subsurface chlorophyll maxima on satellite-based Arctic spring primary production estimates

In recent years several regional and pan-Arctic ocean color algorithms, which consider the unique bio-optical properties of the Arctic Ocean, have been developed to accurately extract surface chlorophyll a (Chl a), a key input into net primary production (NPP) algorithms, from spectral remote sensing reflectance (Rrs). However, most satellite derived NPP (NPPSAT) algorithms used in the Arctic do not account for production at deep subsurface chlorophyll maxima (SCMs), a prominent feature in the Arctic Ocean during the summer months, leading to underestimations of NPP during the post-bloom period. In this study, a shallow SCM was observed in early summer (June/July 2016) in Baffin Bay at the time of the sea ice edge bloom, raising the question to what extent this SCM contributes to Rrs and how it is captured in NPPSAT estimates. Radiative transfer simulations based on in situ data of inherent and apparent optical properties and Chl a concentration in the water column were used to examine the effects of heterogeneous vertical Chl a profiles of various strength and depth on spectral reflectance, algorithm-derived surface Chl aSAT and NPPSAT. NPPSAT estimates for varying Chl a profile shapes were then compared to NPPSAT estimates of reference simulations for homogenous Chl a profiles, and to measured NPP calculated using observed Chl a profiles. Results show a significant impact of shallow SCMs on Rrs with the depth of maximum Chl a < 30 m, causing an increase in Chl aSAT by 17 ± 6% relative to a vertically homogenous ocean. Interestingly, SCMs significantly influencing Rrs were found down to the fifth light penetration depth. The increase in Chl aSAT reduced the difference between predicted and measured NPP estimates to −25 ± 12% at stations with a shallow SCM. Furthermore, due to the significant contribution of these shallow SCM stations to regional NPP, regional predicted NPPSAT was only 16% lower than measured NPP. Our results demonstrate that the partial representation of a shallow and very productive SCM in Chl aSAT minimizes errors in satellite-based NPPSAT estimates of the Arctic spring bloom during the sea ice melt period.

Light Absorption by Optically Active Components in the Arctic Region (August 2020) and the Possibility of Application to Satellite Products for Water Quality Assessment

In August 2020, during the 80th cruise of the R/V “Akademik Mstislav Keldysh”, the chlorophyll a concentration (Chl-a) and spectral coefficients of light absorption by phytoplankton pigments, non-algal particles (NAP) and colored dissolved organic matter (CDOM) were measured in the Norwegian Sea, the Barents Sea and the adjacent area of the Arctic Ocean. It was shown that the spatial distribution of the three light-absorbing components in the explored Arctic region was non-homogenous. It was revealed that CDOM contributed largely to the total non-water light absorption (atot(λ) = aph(λ) + aNAP(λ) + aCDOM(λ)) in the blue spectral range in the Arctic Ocean and the Barents Sea. The fraction of NAP in the total non-water absorption was low (less than 20%). The depth of the euphotic zone depended on atot(λ) in the surface water layer, which was described by a power equation. The Arctic Ocean, the Norwegian Sea and the Barents Sea did not differ in the Chl-a-specific light absorption coefficients of phytoplankton. In the blue maximum of phytoplankton absorption spectra, Chl-a-specific light absorption coefficients of phytoplankton in the upper mixed layer (UML) were higher than those below the UML. Relationships between phytoplankton absorption coefficients and Chl-a were derived by least squares fitting to power functions for the whole visible domain with a 1 nm interval. The OCI, OC3 and GIOP algorithms were validated using a database of co-located results (day-to-day) of in situ measurements (n = 63) and the ocean color scanner data: the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra (EOS AM) and Aqua (EOS PM) satellites, the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and JPSS-1 satellites (also known as NOAA-20), and the Ocean and the Land Color Imager (OLCI) onboard the Sentinel-3A and Sentinel-3B satellites. The comparison showed that despite the technological progress in optical scanners and the algorithms refinement, the considered standard products (chlor_a, chl_ocx, aph_443, adg_443) carried little information about inherent optical properties in Arctic waters. Based on the statistic metrics (Bias, MdAD, MAE and RMSE), it was concluded that refinement of the algorithm for retrieval of water bio-optical properties based on remote sensing data was required for the Arctic region.

Phytoplanktonic community and bio-optical properties in coastal waters of an Argentinian Patagonian gulf

The influence of the phytoplankton community in the light absorption budget was quantified in coastal waters of the North region of the San Jorge Gulf (Argentinian Patagonia). The phytoplanktonic composition and their absorption spectra were determined. Nanoflagellates and diatoms were the dominant groups. The toxigenic dinoflagellate Dinophysis acuminata was recorded in all the sampling sites. The optical characterization of the particulate material showed that 60 % of the absorption at 443 nm and 88 % of absorption at 675 nm was due to phytoplankton. The contributions of phytoplankton to total absorption at 443 nm wavelengths reached 50 %. The absorption by chromophoric dissolved organic matter (CDOM) and non-algal particles (NAP) was predominant in turbulent waters (>60 %). This study shows the influence of submesoscale physical-biological interactions in the light absorption budget. The field absorption spectra of active optical components are of interest in the assessment and development of regional ocean color satellite algorithms.

Seasonal and spatial variability in bio-optical properties of the Persian Gulf: Implications for ocean color remote sensing

In this study, the absorption coefficients of phytoplankton community (aPh(λ)), Colored Dissolved Organic Matter (CDOM) [aCDOM(λ)], and Non-Algal Particles (NAP) [aNap(λ)] were analyzed in the northern Persian Gulf, a Mediterranean-type semi-enclosed marginal sea, through seven cruise expeditions conducted from May 2018 to February 2022. The highest and lowest aPh(443) values were observed in the shallow area (depth ≤20 m) during the wet season (November–April) (0.043 ± 0.004 m-1) and within the deep area (depth >20 m) through the dry season (July–September) (0.024 ± 0.003 m-1), respectively. The chlorophyll-specific absorption (a*Ph(λ)) values increased from the deep to shallow areas as well as the wet to dry seasons. A significant difference was further detected in a*Ph(λ) magnitude at the blue (443 nm) and red (675 nm) wavelengths, with the mean values of 0.0124/0.0223 m2 mg−1 and 0.0069/0.0135 m2 mg−1 in the wet/dry seasons, respectively. The mean values of aCDOM(443) in the shallow area during the wet season (0.10 ± 0.001 m-1) were significantly larger than those in the deep area during the dry season (0.06 ± 0.002 m-1). Similarly, aNAP(443) decreased from the shallow (0.056 ± 0.021 m-1) to deep areas (0.019 ± 0.011 m-1), and from the wet (0.035 ± 0.020 m-1) to dry (0.029 ± 0.015 m-1) seasons. The statistical analysis revealed that the differences in aCDOM(λ) and aNAP(λ) spectral distribution were not statistically significant for the duration of the wet and dry seasons, whereas significant differences were observed in the shallow and deep samples. During both wet and dry seasons, the magnitude of the blue-green absorption spectra was dominated by CDOM, but the red bands were associated with phytoplankton dominated samples. The ratio of aCDOM(λ)/a*Ph(λ) was utilized as an index for uncertainty assessment to estimate Chl-a concentrations by the National Aeronautics and Space Administration (NASA) standard Chl-a algorithms for the Moderate Resolution Imaging Spectrometer (MODIS; OC3M) and Sentinel-3 Ocean and Land Color Instrument (OLCI) (OLCI; OC4v6). The results additionally demonstrated that the satellite-derived Chl-a was overestimated due to higher aCDOM(λ) and underestimated owing to lower a*Ph(λ) at the low (<0.5 mg m−3) and high (>3 mg m−3) concentrations of Chl-a, respectively. The study findings established that the red bands should be considered for tuning or developing new Chl-a algorithms in the Persian Gulf.

Characterization of ocean color retrievals and ocean diurnal variations using the Geostationary Ocean Color Imager (GOCI)

Using measurements from the Geostationary Ocean Color Imager (GOCI) on the Communication, Ocean, and Meteorological Satellite (COMS), we characterize and quantify some advantages and applications of the satellite geostationary measurements, compared with those of the polar-orbiting satellites. With eight daily measurements in the western Pacific Ocean, the average GOCI daily coverage of ocean color retrievals reached ∼43.81%, compared to ∼21.77% from the Visible Infrared Imaging Radiometer Suite (VIIRS). The GOCI-measured ocean property data, such as normalized water-leaving radiance [nLw(λ)] and diffuse attenuation coefficient at 490 nm [Kd(490)], show significant diurnal variability over the region, particularly over highly turbid coastal regions. It is noted that GOCI-derived nLw(λ) spectra and Kd(490) in the region have been compared with those from VIIRS in 2012, 2016, and 2019, and shown consistent results (within ∼5–10%). Using the GOCI daily statistical results from its hourly data, i.e., standard deviations (STDs) and coefficient of variations (CVs) of nLw(λ) and Kd(490), the diurnal variability has been demonstrated and quantified. Over turbid coastal regions, STDs and CVs of nLw(λ) at GOCI 555 and 660 nm bands are important for characterizing diurnal variability, while for open oceans STDs and CVs of nLw(λ) at 412, 443, and 555 nm are useful. Specifically, using the four GOCI daily examples in 2019 on January 24, May 21, August 16, and November 15, we have carried out quantitative analyses for understanding regional diurnal variations. In addition, using the GOCI measurements from 2011 to 2020 in the region, hourly climatology Kd(490) data in four seasons and quantitative results are derived and presented. Results show important characteristics in ocean diurnal variabilities over this highly dynamic and complex western Pacific Ocean region. Indeed, the regional diurnal variability has strong spatial and temporal (seasonal) dependences, which are mostly related to regional ocean optical and bio-optical properties.

Coupling of hydrography and bio-optical constituents in a shallow optically complex region using ten years of in-situ data

Using ten years of in-situ data from a shallow optically complex coastal ecosystem, this study unravels the coupling between bio-optical variability and seasonally changing hydrography. The data presented is novel, as these are long-term simultaneous measurements of bio-optical properties and constituents covering a wide range of hydrographic conditions at different sampling stations in riverine, estuarine, and coastal shelf. The data represent all three seasons experienced in the region, i.e., Indian Summer Monsoon (ISM), post-ISM, and pre-ISM. It allowed us to conduct a baseline study to characterize the bio-optical variability (absorption or scattering dominated) coupled with stratification for remote sensing applications on ecological health. Four groups of sampling stations, representing varying hydrography, were identified as riverine, maximum stratification, maximum chlorophyll-a (Chl-a) concentration, and estuarine/shelf. The maximum concentrations of total suspended matter (TSM) were always collocated with the strongest stratification site, while maximum Chl-a was always located downstream of maximum stratification. Surprisingly, colored dissolved organic matter (CDOM) values were negligible in the ISM season and increased manifold in other seasons. The source of CDOM (either land-derived or in situ generated) inferred from the spectral slope revealed the dominance of land-derived CDOM in the post-ISM season. Combining the entire data into the same groups showed that groups with maximum TSM and samples from estuarine locations were scattering-dominated. In contrast, the group with maximum Chl-a was absorption dominated. The analysis revealed that the optical behavior of the sampling locations being either absorption or scattering dominated depended on the state of stratification and contribution of Chl-a to the absorption budget.