Search Results (103)

Phytoplankton are primary producers in the aquatic ecosystems whose pigments, cell size, and physiological state affect how they absorb light and fix carbon. The phytoplankton absorption coefficient (ɑ_ph(λ)) in the visible spectrum is a fundamental cellular optical property that determines phytoplankton–light interactions in the marine environment. This property links biological processes to ocean color remote sensing reflectance (Rrs), enabling an assessment of environmental and biogeochemical conditions in the ocean using ocean color satellites. This study presents a multi-stage systematic review of six decades (1965–2025) of ɑ_ph(λ) research, with a focused synthesis of developments in the past decade. A bibliometric analysis empirically examines the research growth of the field and its thematic convergence into methodological divergence across six decades. Cluster analysis was used to compile influential research topics as well as emerging trends, to determine the scope and design of the systematic review. A focused systematic review of studies in the past decade (2015–2025) has been carried out to identify conceptual and theoretical advances, major observational and algorithmic improvements, and ongoing challenges. The data analyses highlight the accuracy achieved by various studies, the complexity of applications of algorithms, and product-focused developments. The ongoing challenges identified include resolving optical degeneracy, vertical structure acquisition, and scaling methods for operational use. This review concludes the centrality of ɑ_ph(λ) as a key parameter to next-generation ocean color science, biogeochemical modeling, and climate-related ecosystem monitoring. Full article

Phytoplankton size classes (PSCs) fundamentally regulate ocean productivity, biogeochemical cycling, and carbon export, yet their distribution and optical variability across the Arabian Sea remain poorly constrained. This study develops and validates a regionally tuned absorption-based approach for phytoplankton size class estimation using in situ phytoplankton absorption spectra (a_ph(λ)) collected during six research cruises between 2016 and 2024. A significant power-law relationship between a_ph(443) and the spectral slope (S_443–510) (R² = 0.963, p < 0.001) provided a consistent optical basis for distinguishing PSCs. Co-located HPLC pigment data were used to derive empirical a_ph(443) thresholds for pico- (≤0.011 m⁻¹), nano- (0.011–0.059 m⁻¹), and micro-phytoplankton (>0.059 m⁻¹). Class-specific mean spectra showed clear optical distinctions consistent with size-dependent pigment packaging. Model evaluation showed reduced error and improved regression agreement relative to existing a_ph- and chl-a-based models when applied to the Arabian Sea dataset, with regression slopes close to unity (0.78–0.81) across all PSCs. This regional model also improved representation of transitional nano communities, which are commonly associated with higher uncertainties in global models. The empirical relationships developed in this study were applied to VIIRS Level 3 a_ph(443) data for 2024 to generate PSC distributions. Satellite-derived PSC fields revealed pronounced spatial gradients and regional contrasts across the Arabian Sea, including micro-phytoplankton blooms in the northern Arabian Sea and mixed nano-dominated communities along the western Arabian Sea (Somali coast). Pico-phytoplankton dominated the low-absorption oligotrophic offshore waters, while nano-phytoplankton were most common in transitional regions influenced by moderate nutrient inputs. Taken together, these results demonstrate that the combined a_ph(443)-S_443–510 framework provides a practical, regionally optimized method for retrieving PSCs at synoptic scales across the Arabian Sea, supporting improved bio-optical modelling and satellite-based monitoring of phytoplankton community structure in this region. Full article

Dredging activities regularly occurring in near-shore and coastal waters generate turbid waters within the surface layer with high concentrations of suspended particulate matter collected in bottom sediments. The potential impact of these dredge plumes on natural ecosystems must be monitored using cost-effective methods and observations. Here, we investigate the biogeochemical and optical properties of dredge plumes selected mainly in European and African coastal waters. Laboratory analyses realized on numerous water samples collected in dredge plumes reveal (extremely) high water turbidity and high concentrations of inorganic particles in suspension, sometimes mixed with high concentrations of phytoplankton particles. The most peculiar optical property of these particles is a spectral light absorption coefficient significantly flatter than that of suspended particles in natural turbid waters (e.g., river plumes or estuarine maximum turbidity zones). This peculiar optical property is also detected on ocean color satellite data corrected for atmospheric effects, with a water reflectance signal higher than natural turbid waters at short visible wavebands (400–550 nm). Such an atypical spectral signature, which can be detected and mapped from space, makes the operational monitoring of dredge plumes in coastal waters using high-spatial-resolution (e.g., Sentinel2-MSI) satellite data possible. Full article

Accurate detection of chlorophyll-a (Chl-a) is critical for monitoring water quality in inland waters, where high concentrations of coloured dissolved organic matter (CDOM) complicate retrieval process. Reliable Chl-a estimation depends on the precise determination of the phytoplankton absorption coefficient (a_ph). This study evaluates Chl-a detection from in situ a_ph measurements and assesses the accuracy of phytoplankton absorption retrieval from Sentinel-2/MSI (S2) and Sentinel-3/OLCI (S3) using the Case-2-Regional-Coast-Colour (C2RCC) processor across diverse optical water types (OWTs) in boreal lakes. OWTs were classified based on remote sensing reflectance features, representing Clear, Moderate, Turbid, Very Turbid, and Brown conditions. CDOM absorption strongly influenced the underwater light field, particularly in Brown and Turbid waters. Linear relationships between a_ph and Chl-a were generally strong across OWTs, with improved relationships in the red spectral region (670 nm). Satellite-derived a_pig estimates showed a weak relationship with in situ data (R² = 0.26–0.45). Both sensors overestimated small a_ph values, while S3 underestimated larger ones. S2 underestimated a_ph in Clear and Brown OWTs, with median absolute percentage differences near 100% for all OWTs. These findings emphasize the challenges posed by bio-optical complexity in boreal lakes and highlight the need for OWT-specific algorithms to improve satellite-based absorption and Chl-a retrieval accuracy. Full article

As an important component of waters constituent that affects ocean color and the underwater ecological environment, the accurate assessment of Chromophoric Dissolved Organic Matter (CDOM) is crucial for observing the continuous changes in the marine ecosystem. However, remote sensing estimation of CDOM remains challenging for both coastal and oceanic waters due to its weak optical signals and complex optical conditions. Therefore, the development of efficient, practical, and robust models for estimating the CDOM absorption coefficient in both coastal and oceanic waters remains an active research focus. This study presents a novel algorithm (denoted as DQAAG) that incorporates ultraviolet bands into the inversion model. The design leverages the distinct spectral absorption characteristics of phytoplankton versus detrital particles in the ultraviolet (UV) region, enabling improved discrimination of water color parameters. Furthermore, the algorithm replaces empirical formulas commonly used in semi-analytical approaches with an artificial intelligence model (deep learning) to achieve enhanced inversion accuracy. Using IOCCG hyperspectral simulation data and NOMAD dataset to evaluates Shanmugam (2011) (S2011), Aurin et al. (2018) (A2018), Zhu et al. (2011) (QAA-CDOM), DQAAG, the results indicate that the a_g(443) derived from the DQAAG exhibit good agreement with the validation data, with root mean square deviation (RMSD) < 0.3 m⁻¹, mean absolute relative difference (MARD) < 0.30, mean bias (bias) < 0.028 m⁻¹, coefficient of determination (R²) > 0.78. The DQAAG algorithm was applied to SeaWiFS remote sensing data, and validation was performed through match-up analysis with the NOMAD dataset. The results show the RMSD = 0.14 m⁻¹, MARD = 0.39, and R² = 0.62. Through a sensitivity analysis of the algorithm, the study reveals that R_rs(670) and R_rs(380) exhibit more significant characteristics. These results demonstrate that UV bands play a crucial role in enhancing the retrieval accuracy of ocean color parameters. In addition, DQAAG, which integrates semi-analytical algorithms with artificial intelligence, presents an encouraging approach for processing ocean color imagery to retrieve a_g(443). Full article

Coral reefs exposed to chronically turbid conditions challenge conventional assumptions about the optical environments required for reef persistence and productivity. This study investigates the utility of light absorption coefficients as indicators of optical water quality in Varadero Reef, an extreme coral ecosystem located in Cartagena Bay, Colombia. Field campaigns were conducted across three seasons (rainy, dry, and transitional) along a transect from fluvial to marine influence. Absorption coefficients at 440 nm were derived for particulate (a_p(440)) and chromophoric dissolved organic matter (a_CDOM(440)) to assess their contribution to underwater light attenuation. Average values across seasons show that a_p(440) reached 0.466 m⁻¹ in the rainy season (September 2021), 0.285 m⁻¹ in the dry season (February 2022), and 0.944 m⁻¹ in the transitional rainy season (June 2022). Meanwhile, mean a_CDOM(440) values were 0.368, 0.111, and 0.552 m⁻¹, respectively. These coefficients reflect the dominant influence of particulate absorption under turbid conditions and increasing a_CDOM(440) relevance during lower turbidity periods. Mean Secchi Disk Depth (ZSD) ranged from 0.6 m in the rainy season to 3.0 m in the dry season, aligning with variations in Kd PAR, which averaged 2.63 m⁻¹, 1.13 m⁻¹, and 1.08 m⁻¹ for the three campaigns. Chlorophyll-a concentrations at 1 m depth also varied significantly, with average values of 2.3, 2.7, and 6.2 μg L⁻¹, indicating phytoplankton biomass peaks associated with seasonal freshwater inputs. While particulate absorption limits light penetration, CDOM plays a potentially photoprotective role by attenuating UV radiation. The observed variability in these optical constituents reflects complex hydrodynamic and environmental gradients, providing insight into the mechanisms that sustain coral functionality under suboptimal light conditions. The absorption-based approach applied here, using standardized spectrophotometric methods, proved to be a reliable and reproducible tool for characterizing the spatial and temporal variability of IOPs. We propose integrating these indicators into monitoring frameworks as cost-effective, component-resolving tool for evaluating light regimes and ecological resilience in optically dynamic coastal systems. Full article

Water treatment systems in swimming ponds support the natural self-cleaning capabilities of water based on the functions of repository macrophytes in their regeneration zone and the regulation of the internal metabolism of the reservoirs. As part of the project, a functional modular filtration chamber with system multiplication capabilities was designed and created. This element is dedicated to water treatment systems in natural swimming ponds. The prototype system consisted of modular filtration chambers and pump sections, as well as equipment adapted to the conditions prevailing in the eco-pool. An innovative solution for selective shutdown of the filtration chamber without closing the circulation circuit was also used, which forms the basis of a patent application. A verified high-performance adsorbent, Rockfos^® modified limestone, was used in the filtration chamber. In order to determine the effective filtration rate for three small test ponds with different flow rates (5 m/h, 10 m/h and 15 m/h), the selected physicochemical parameters of water (temperature, pH, electrolytical conductivity, oxygen saturation, total hardness, nitrites, nitrates, and total phosphorus, including adsorption efficiency and bed absorption capacity) were researched before and after filtration. Tests were also carried out on the composition of fecal bacteria and phyto- and zooplankton. Based on high effective phosphorus filtration efficiency of 32.65% during the operation of the bed, the following were determined: no exceedances of the standards for the tested parameters in relation to the German standards for eco-pools (FLL—Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau e. V., 2011); lower number of fecal pathogens (on average 393—coliform bacteria; 74—Escherichia coli; 34—fecal enterococci, most probably number/100 mL); the lowest share of problematic cyanobacteria in phytoplankton (<250,000 individuals/dm³ in number and <0.05 µg/dm³—biomass); low chlorophyll a content (2.2 µg/dm³—oligotrophy) and the presence of more favorable smaller forms of zooplankton, an effective filtration speed of 5 m/h. This velocity was recommended in the FLL standards for swimming ponds, which were adopted in this study as a reference for rapid filters. In testing the functional efficiency of a dedicated filtration system for a Type II test pond (50 m²—area and 33 m³—capacity), at a filtration rate of 5 m/h, an average effective phosphorus adsorption efficiency of 18.28–53.98% was observed under the bed work-in-progress conditions. Analyses of other physicochemical water parameters, with appropriate calculations and statistical tests, indicated progressive functional efficiency of the system under bathing conditions. Full article

This study presents the living coccolithophore communities and the morphological variability of Emiliania huxleyi in the South Aegean Sea from three sampling regions during winter-early spring (March 2017, March 2019) and summer (August 2019). Emphasis is given to March 2017 to monitor the variations in coccolithophore assemblages after an exceptionally cold event in December 2016, which resulted in newly produced dense waters that ventilated the Aegean deep basins. The assemblages displayed distinct seasonality with the predominance of E. huxleyi and Syracosphaera molischii during winter-early spring, associated with the water column mixing. By contrast, summer assemblages were featured by holococcolithophores and typical taxa of warm, oligotrophic upper waters. It seems that the phytoplanktonic succession as well as the nutrient supply to the upper euphotic layers were affected by the water column perturbation during the extreme winter of 2016–2017, which led to strong convective mixing and dense water formation. The decreased coccosphere densities during March 2017, accompanied by the notable presence of diatoms, were most probably associated with a prolonged diatom bloom, causing delay in the development of the coccolithophore community and resulting in a nitrogen-limited setting. Emiliania huxleyi morphometry showed the characteristic seasonal calcification trend of the Aegean, with the dominance of smaller coccoliths in the summer and increased coccolith length and width during the cold season. The intense cold conditions and wind-induced mixing during the winter of 2016–2017 possibly increased the absorption of atmospheric CO₂ in surface waters, causing increased acidity and the subsequent presence of etched/undercalcified E. huxleyi coccoliths and other taxa, most probably implying in situ calcite dissolution. Full article

As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient ($a_{phy}$), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA’s hyperspectral missions, such as EMIT and PACE, have generated an urgent need for hyperspectral algorithms for studying phytoplankton. Retrieving $a_{phy}$ from ocean color remote sensing in coastal waters has been extremely challenging due to complex optical properties. Traditional methods often fail under these circumstances, while improved machine-learning approaches are hindered by data scarcity, heterogeneity, and noise from data collection. In response, this study introduces a novel machine learning framework for hyperspectral retrievals of $a_{phy}$ based on the mixture-of-experts (MOEs), named PhA-MOE. Various preprocessing methods for hyperspectral training data are explored, with the combination of robust and logarithmic scalers identified as optimal. The proposed PhA-MOE for $a_{phy}$ prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. Extensive experiments reveal the importance of data preprocessing and improved performance of PhA-MOE in estimating $a_{phy}$ as well as in handling data heterogeneity. Notably, this study marks the first application of a machine learning–based MOE model to real PACE-OCI hyperspectral imagery, validated using match-up field data. This application enables the exploration of spatiotemporal variations in $a_{phy}$ within an optically complex estuarine environment. Full article

An oceanographic cruise with 34 stations was conducted in the central-southwestern region of the Gulf of Mexico from February 19 to 10 March 2013. This study included the measurement of hydrographic and phytoplankton bio-optical parameters, and pigment samples were collected at two depth levels (10 and 50 m). Our results showed a warm and nutrient-depleted water column associated with low chlorophyll a (<1 mg Chla m⁻³) and average values of a_ph440 (0.01 ± 0.008, m⁻¹) and a_d350 (0.04 ± 0.02, m⁻¹). In addition, nano-microphytoplankton abundance and pigments were analyzed using a light microscope and HPLC, respectively. Overall, the Gulf of Mexico exhibited oligotrophic characteristics, with Chla (0.17 ± 0.11 mg m⁻³) and NO₃⁻ (0.03 ± 0.001 µM), except at 50 m depth in some stations north of Yucatán and in Campeche Bay and at surface level off the Tamaulipas shelf. In these three regions, values of a_ph(440), a_d(350), (Chla) and phytoplankton abundance (>12 × 10³ cells L⁻¹) were observed near river mouths and under seasonal oceanographic forcings, which increased the growth and diversity of phytoplankton. The most relevant pigments found were DVchla (0.06 ± 0.13 mg m⁻³), Chlb (0.16 ± 0.21 mg m⁻³), Zea (0.06 ± 0.03 mg m⁻³), and Hex-fuco (0.02 ± 0.02 mg m⁻³); these are associated with the presence of Prochlorococcus, chlorophytes, Synechococcus, prymnesiophytes, and diatoms. Through the bio-optical variability, we determined the ecological geography of phytoplankton in four different spectral shapes, where M1 and M2 represent the group of cyanobacteria (Prochlorococcus and Synechococcus) and M3 and M4 represent a mixture of diatoms, dinoflagellates, and chlorophytes. In conclusion, we consider that oceanographic processes such as cyclonic and anticyclonic structures and permanent rivers determine the favorable changes in phytoplankton (>nutrients, Chla, a_ph440) and an increment in the number of phytoplankton spectral shapes). Full article

Antarctica and the Southern Ocean are important sinks in the global mercury (Hg) cycle, and in the marine environment, inorganic Hg can be converted by bacteria to monomethylmercury (MeHg), a highly bioavailable and toxic compound that biomagnifies along food webs. In the Southern Ocean, higher concentrations of Hg and MeHg have typically been reported in the coastal waters of the Ross and Amundsen Seas, where katabatic winds can transport Hg from the Antarctic Plateau and create coastal polynyas, which results in spring depletion events of atmospheric Hg. However, some studies on MeHg biomagnification in Antarctic marine food webs have reported higher Hg concentrations in penguins from sub-Antarctic waters and, unexpectedly, higher levels in juvenile krill than those in adult Antarctic krill. In light of recent estimates of the phytoplankton and zooplankton biomass and distribution in the Southern Ocean, this review suggests that although most studies on MeHg biomagnification refer to the short diatom–krill–vertebrate food chain, alternative and more complex pelagic food webs exist in the Southern Ocean. Thus, juvenile krill and micro- and mesozooplankton grazing on very small autotrophs and heterotrophs, which have high surface-to-volume ratios for MeHg ad-/absorption, may accumulate more Hg than consumers of large diatoms, such as adult krill. In addition, the increased availability of Hg and the different diet contribute to a greater metal accumulation in the feathers of Adélie penguins from the Ross Sea than that of those from the sub-Antarctic. Full article

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive a_ph(440), a_ph(555), a_ph(675), and a_ph(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R² all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology. Full article

Lake Kasumigaura, one of Japan’s largest lakes, presents significant challenges for remote sensing due to its eutrophic conditions and complex optical properties. Although the Global Change Observation Mission-Climate (GCOM-C)/Second-generation Global Imager (SGLI)-derived inherent optical properties (IOPs) offer water quality monitoring potential, their performance in such turbid inland waters remains inadequately validated. This study evaluated five established IOP retrieval algorithms, including the quasi-analytical algorithm (QAA_V6), Garver–Siegel–Maritorena (GSM), generalized IOP (GIOP-DC), Plymouth Marine Laboratory (PML), and linear matrix inversion (LMI), using measured remote sensing reflectance (Rrs) and corresponding IOPs between 2017–2018. The results demonstrated that the QAA had the highest performance for retrieving absorption of particles (a_p) with a Pearson correlation (r) = 0.98, phytoplankton (a_ph) with r = 0.97, and non-algal particles (a_nap) with r = 0.85. In contrast, the GSM algorithm exhibited the best accuracy for estimating absorption by colored dissolved organic matter (a_CDOM), with r = 0.87, along with the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE). Additionally, a strong correlation (r = 0.81) was observed between SGLI satellite-derived remote-sensing reflectance (R_rs) and in situ measurements. Notably, a high correlation was observed between the a_ph (443 nm) and the chlorophyll a (Chl-a) concentration (r = 0.84), as well as between the backscattering coefficient (b_bp) at 443 nm and inorganic suspended solids (r = 0.64), confirming that IOPs are reliable water quality assessment indicators. Furthermore, the use of IOPs as variables for estimating water quality parameters such as Chl-a and suspended solids showed better performance compared to empirical methods. Full article

Rapid and accurate dynamic monitoring and quantitative analysis of red tide disasters are of significant practical importance to national economic development. Remote sensing technology is an effective means for monitoring red tides. This paper utilizes GOCI satellite data and employs a quasi-analytical algorithm (QAA) to retrieve the spectral curves of phytoplankton absorption coefficients. On the basis of a detailed analysis of the differences in the spectral curves of the phytoplankton absorption coefficients between red tide and non-red tide waters, we establish a red tide identification algorithm for the East China Sea on the basis of phytoplankton absorption coefficients. The algorithm is applied to multiple red tide events in the East China Sea. The results indicate that this algorithm can effectively determine the occurrence locations of red tides and extract relevant information about them. Full article

Boreal lake depth, one of the most important parameters in numerical weather prediction and climate models through parametrization, helps in identifying notable environmental changes across the globe and in estimating its effect on the ecosystem in remote regions. However, there is no quantitative tool to effectively estimate lake depth from satellite images, leaving scientists to infer lake depth from extrapolation of statistics by relying on certain geological knowledge (such as those used in the Global Lake Database). The bottoms of boreal forest lakes are mainly composed of woody debris, and thus spectral imaging revealing contrast of woody debris can be used to estimate lake depth. Here, we use well-established Monte Carlo software to construct spectral images of boreal lakes that house woody debris, phytoplankton, and chlorophyll. This is accomplished by modeling the dynamic optical properties of selected boreal lakes and simulating the propagation of photons in the wavelength region of 400–800 nm. The results show that the spectral image contrast of boreal lakes is not only determined by the depth level and concentration level of phytoplankton and chlorophyll in water but is also affected by the spectral shape of background absorption, especially the contribution of pure water absorption in the total absorption of lake water. Full article