Bio-optical Data Research Articles

Characterization of Phytoplankton Composition in Lake Maggiore: Integrated Chemotaxonomy for Enhanced Cyanobacteria Detection

Cyanobacterial blooms in lakes have increased in frequency and intensity over the past two decades, negatively affecting ecological and biogeochemical processes. This study focuses on the phytoplankton composition of Lake Maggiore, with a special emphasis on cyanobacteria detection through pigment composition. While microscopy is the standard method for phytoplankton identification, pigment-based methods provide broader spatiotemporal coverage. Between May and September 2023, five measurement campaigns were conducted in Lake Maggiore, collecting bio-geochemical and bio-optical data at 27 stations. The total Chlorophyll-a (TChl a) was measured, with concentrations ranging from 1.13 to 6.9 mg/m3. Phytoplankton pigment composition was analyzed using High-Performance Liquid Chromatography (HPLC) and the CHEMTAX approach was applied for phytoplankton classification. The results were cross-validated using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and microscopic counts. Cyanobacteria were identified based on unique pigment markers, such as carotenoids. The HPLC-derived pigment classification results aligned well with both PCA and HCA and microscopic counts verified the accuracy of the pigment-based chemotaxonomy. The study demonstrates that pigment-based classification methods, when combined with statistical analyses, offer a reliable alternative for identifying cyanobacteria and other phytoplankton groups, with potential applications in support of remote sensing algorithm development.

Interannual variability of surface bio-optical characteristics in the frontal zones of the Indian sector of the Southern Ocean during austral summer

Surface bio-optical constituents (Chl-a, light-absorption coefficient of phytoplankton (aph (443)), detritus (ad(443)), coloured dissolved organic matter (aCDOM(443))), and hydrographic variables were studied across the fronts along 57.5°E during 2011, 2013 and 2015 in the Indian sector of Southern Ocean to understand their interannual variability and phytoplankton pigment package effect. Latitudinal increases of Chl-a, aph(443), and aCDOM(443) were observed in 2011 and 2013. Mean Chl-a was higher in 2013 than in 2015 and 2011. The Chl-a specific absorption (a*ph(443)) latitudinally decreased implying ‘pigment package effect’ with increasing latitude. Interannually increase of aph blue (443)/red (675) ratio suggested higher non-photosynthetic absorption during 2013 and 2015 than in 2011. The relative contributions of bio-optical constituents to total absorption (at) showed interannual decrease in aph(443) and increase in aCDOM(443) and ad(443) inferring phytoplankton growth and decay phases. The robust aph(443)-Chl-a relationship during 2011 was used to derive aph(443) for other years, which holds good for in situ and satellite-derived counterparts. aCDOM(443) positively correlated with Chl-a and negatively with CDOM-spectral slope (S300-500), suggesting autochthonous sources. Satellite-retrieved Chl-a, aph(443) showed significant correlation (p < 0.001) with in situ data. This study provides baseline bio-optical data for understanding phytoplankton size-class, and improvement of bio-optical algorithms in this relatively inaccessible polar region.

Two-term scattering phase function for photon transport to model subdiffuse reflectance in superficial tissues.

For Monte Carlo simulations of light transport in a variety of diffuse scattering applications, a single-scattering two-term phase function with five adjustable parameters is sufficiently flexible to separately control the forward and backward components of scattering. The forward component dominates light penetration into a tissue and the resulting diffuse reflectance. The backward component controls early subdiffuse scatter from superficial tissues. The phase function consists of a linear combination of two phase functions [Reynolds and McCormick, J. Opt. Soc. Am.70, 1206 (1980)10.1364/JOSA.70.001206] that were derived from the generating function for Gegenbauer polynomials. The two-term phase function (TT) accommodates strongly-forward anisotropic scattering with enhanced backscattering and is a generalization of the two-term, three-parameter Henyey-Greenstein phase function. An analytical inverse of the cumulative distribution function for scattering is provided for implementation in Monte Carlo simulations. Explicit TT equations are given for the single-scattering metrics g 1, g 2, γ, and δ. Scattering data from previously published bio-optical data are shown to fit better with the TT than other phase function models. Example Monte Carlo simulations illustrate the use of the TT and its independent control of subdiffuse scatter.

Spatiotemporal variability in bio-optical characteristics of the southwestern tropical Indian Ocean during boreal summer: Biophysical influences

The bio-optical characteristics of the surface ocean play a pivotal role in radiative transfer and photosynthetic carbon fixation. To examine spatiotemporal variability of bio-optical properties in the tropical Indian Ocean, a 10-day time-series comparative study was made at one fixed and 3 variable stations in the southwestern tropical Indian Ocean (SWTIO), and 4-stations in the Equatorial Indian Ocean (EIO) during June 2014. A total of 24 hyperspectral radiometer profiles were collected along with physicochemical parameters and phytoplankton pigments analysis. A negative sea level anomaly (SLA), higher precipitation, a more stable mixed layer, and higher buoyancy frequency (N2) were observed in SWTIO compared to EIO. The deep chlorophyll maxima (DCM) in SWTIO were characterised by deep biomass maxima (DBM) whereas in the EIO it was due to deep photoacclimation maxima (DAM). The photosynthetically available radiation (PAR) profiles were obtained by integrating downwelling irradiance from 400 to 700 nm suggesting that the SWTIO had a greater abundance of optically active substances (OAS) than the EIO. Surface chlorophyll a (Chl-a) was found to have significant relationships with phytoplankton absorption coefficient (aph(443)). Chl-a-specific phytoplankton absorption coefficients (a*ph(443)), total absorption coefficient (at(443)), and particulate backscattering (bbp(555)) were also related to Chl-a implying that the phytoplankton was in growth/active phase. The time-series variability of the phytoplankton and changes in the photosynthetic pigments (PSP) to photoprotective pigments (PPP) ratio at SWTIO suggested phytoplankton community shift during 5th to 7th days. Microplankton percent-abundances showed distinct linear relationships with Chl-a and bbp(555) implying that carbon fixation per unit Chl-a was higher in microplankton than in nano and picoplankton at SWTIO. The in situ Chl-a, aph(443), adg(443), Kd(490) and C¯pd were used to validate the various empirical algorithms available as well as the satellite-retrieved data. The MODIS and VIIRS data correlated moderately. This study would help towards improving regional optimized algorithms for OAS and provide a baseline understanding of the scale of variability of primary production and carbon flux from space. In addition, this would contribute valuable bio-optical data from an under-sampled geographical area.

Genesis and Evolution of NASA’s Satellite Ocean Color Program

We recount, based on our involvements in NASA ocean color flight projects, the chronology of technical challenges, lessons learned, and key developments over the past 40 + years of NASA satellite ocean color, beginning with the Nimbus-7/Coastal Zone Color Scanner, that have led to the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Topics include the evolution of 1) satellite ocean color and field bio-optical data access, 2) satellite sensor capabilities, i.e., CZCS to PACE’s hyperspectral Ocean Color Imager, OCI, 3) atmospheric corrections, 4) pre- and post-launch sensor characterization and calibration, 5) bio-optical algorithms, 6) in situ-derived radiometry and photosynthetic pigment data measurement quality, and 7) applications of hyperspectral satellite observations.

Artificial Intelligence Meets Marine Ecotoxicology: Applying Deep Learning to Bio-Optical Data from Marine Diatoms Exposed to Legacy and Emerging Contaminants.

Simple SummaryOur work is motivated by the increasing production of chemicals with environmentally harmful effects to our aquatic ecosystems. We show that it is possible to detect and distinguish the presence of several different emerging contaminants, using the photochemical responses of a microalgae species, which is among the most abundant phytoplankton group in the oceans. We use several machine learning and deep learning models that operate on chlorophyll fluorescence induction curves, which are composed of fluorescence values taken at different time steps from the microalgae exposure trials, achieving up to 97.65% accuracy when predicting the type of contaminant, and up to 100% in several cases when predicting the exposure concentration. Our results show the combination of these models with the fluorescence induction curves creates a powerful tool for ecotoxicity assessment, capable of classifying model organisms for their contaminant exposure, both in terms of type and concentration, opening new doors for toxicophenomics developments.Over recent decades, the world has experienced the adverse consequences of uncontrolled development of multiple human activities. In recent years, the total production of chemicals has been composed of environmentally harmful compounds, the majority of which have significant environmental impacts. These emerging contaminants (ECs) include a wide range of man-made chemicals (such as pesticides, cosmetics, personal and household care products, pharmaceuticals), which are of worldwide use. Among these, several ECs raised concerns regarding their ecotoxicological effects and how to assess them efficiently. This is of particular interest if marine diatoms are considered as potential target species, due to their widespread distribution, being the most abundant phytoplankton group in the oceans, and also being responsible for key ecological roles. Bio-optical ecotoxicity methods appear as reliable, fast, and high-throughput screening (HTS) techniques, providing large datasets with biological relevance on the mode of action of these ECs in phototrophic organisms, such as diatoms. However, from the large datasets produced, only a small amount of data are normally extracted for physiological evaluation, leaving out a large amount of information on the ECs exposure. In the present paper, we use all the available information and evaluate the application of several machine learning and deep learning algorithms to predict the exposure of model organisms to different ECs under different doses, using a model marine diatom (Phaeodactylum tricornutum) as a test organism. The results show that 2D convolutional neural networks are the best method to predict the type of EC to which the cultures were exposed, achieving a median accuracy of 97.65%, while Rocket is the best at predicting which concentration the cultures were subjected to, achieving a median accuracy of 100%.

A Semi-Analytical Optical Remote Sensing Model to Estimate Suspended Sediment and Dissolved Organic Carbon in Tropical Coastal Waters Influenced by Peatland-Draining River Discharges off Sarawak, Borneo

Coastal water quality degradation is a global challenge. Marine pollution due to suspended sediments and dissolved matter impacts water colour, biogeochemistry, benthic habitats and eventually human populations that depend on marine resources. In Sarawak (Malaysian Borneo), peatland-draining river discharges containing suspended sediments and dissolved organic carbon influence coastal water quality at multiple locations along the coast. Optical remote sensing is an effective tool to monitor coastal waters over large areas and across remote geographic locations. However, the lack of regional optical measurements and inversion models limits the use of remote sensing observations for water quality monitoring in Sarawak. To overcome this limitation, we have (1) compiled a regional spectral optical library for Sarawak coastal waters, (2) developed a new semi-analytical remote sensing model to estimate suspended sediment and dissolved organic carbon in coastal waters, and (3) demonstrated the application of our remote sensing inversion model on satellite data over Sarawak. Bio-optical data analysis revealed that there is a clear spatial variability in the inherent optical properties of particulate and dissolved matter in Sarawak. Our optical inversion model coupled with the Sarawak spectral optical library performed well in retrieving suspended sediment (bias = 3% and MAE = 5%) and dissolved organic carbon (bias = 3% and MAE = 8%) concentrations. Demonstration products using MODIS Aqua data clearly showed the influence of large rivers such as the Rajang and Lupar in discharging suspended sediments and dissolved organic carbon into coastal waters. The bio-optical parameterisation, optical model, and remote sensing inversion approach detailed here can now help improve monitoring and management of coastal water quality in Sarawak.

Sensitivity of a Satellite Algorithm for Harmful Algal Bloom Discrimination to the Use of Laboratory Bio-optical Data for Training

Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical data from the culture and a bio-optical model were used to construct a training dataset for an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive rates of detection of K. mikimotoi between using an algorithm trained with purely manually selected areas on satellite images and using laboratory data as training was overall &amp;lt;1%. The difference was higher, &amp;lt;15%, when using modeled optical data rather than laboratory data, with potential for improvement if local average chlorophyll concentrations are used. Using a laboratory-derived training dataset improved the ability of the algorithm to distinguish high turbidity from high chlorophyll concentrations. However, additional in situ observations of non-harmful high chlorophyll blooms in the area would improve testing of the ability to distinguish harmful from non-harmful high chlorophyll blooms. This approach can be expanded to use additional wavelengths, different satellite sensors and different phytoplankton genera.

Glyphosate-Based Herbicide Toxicophenomics in Marine Diatoms: Impacts on Primary Production and Physiological Fitness

Glyphosate is the main active component of the commercial formulation Roundup®, the most widely used chemical herbicide worldwide. However, its potential high toxicity to the environment and throughout trophic webs has come under increasing scrutiny. The present study aims to investigate the application of bio-optical techniques and their correlation to physiological and biochemical processes, including primary productivity, oxidative stress, energy balance, and alterations in pigment and lipid composition in Phaeodactylum tricornutum, a representative species of marine diatoms, using the case study of its response to the herbicide glyphosate-based Roundup® formulation, at environmentally relevant concentrations. Cultures were exposed to the herbicide formulation representing effective glyphosate concentrations of 0, 10, 50, 100, 250, and 500 μg L−1. Results showed that high concentrations decreased cell density; furthermore, the inhibition of photosynthetic activity was not only caused by the impairment of electron transport in the thylakoids, but also by a decrease of antioxidant capacity and increased lipid peroxidation. Nevertheless, concentrations of one of the plastidial marker fatty acids had a positive correlation with the highest concentration as well as an increase in total protein. Cell energy allocation also increased with concentration, relative to control and the lowest concentration, although culture growth was inhibited. Pigment composition and fatty acid profiles proved to be efficient biomarkers for the highest glyphosate-based herbicide concentrations, while bio-optical data separated controls from intermediate concentrations and high concentrations.

Classification of Australian Waterbodies across a Wide Range of Optical Water Types

Baseline determination and operational continental scale monitoring of water quality are required for reporting on marine and inland water progress to Sustainable Development Goals (SDG). This study aims to improve our knowledge of the optical complexity of Australian waters. A workflow was developed to cluster the modelled spectral response of a range of in situ bio-optical observations collected in Australian coastal and continental waters into distinct optical water types (OWTs). Following clustering and merging, most of the modelled spectra and modelled specific inherent optical properties (SIOP) sets were clustered in 11 OWTs, ranging from clear blue coastal waters to very turbid inland lakes. The resulting OWTs were used to classify Sentinel-2 MSI surface reflectance observations extracted over relatively permanent water bodies in three drainage regions in Eastern Australia. The satellite data classification demonstrated clear limnological and seasonal differences in water types within and between the drainage divisions congruent with general limnological, topographical, and climatological factors. Locations of unclassified observations can be used to inform where in situ bio-optical data acquisition may be targeted to capture a more comprehensive characterization of all Australian waters. This can contribute to global initiatives like the SDGs and increases the diversity of natural water in global databases.

Controls on Ocean Color Spectra Observed During the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES)

Satellite ocean color remote sensing is the primary method to retrieve synoptic measurements of the optical properties of the ocean on large spatial and regular time scales.Through bio-optical modelling, changes in ocean color spectra can be linked to changes in marine 2 ecosystem and biogeochemical properties. Bio-optical algorithms rely on assumptions about the covariance of marine constituents as well as the relationships among their inherent and apparent optical properties. Validation with in situ measurements of in-water constituents and their optical properties is required to extrapolate local knowledge about ocean color variations to global scales.Here, we evaluate seasonal and spatial relationships between optical constituents and their inherent and apparent optical properties throughout the annual cycle of the North Atlantic plankton bloom using bio-optical data from four cruises conducted as part of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). Our results show ocean color variability, quantified using field observations of the remote sensing reflectance spectrum at each NAAMES station, is driven by colored dissolved organic matter (CDOM) absorption in the ultraviolet wavelengths, phytoplankton absorption in the blue wavelengths, and total particulate backscattering in the green wavelengths. Results from a recently storm-mixed station at the height of the spring bloom demonstrate that significant changes in bio-optical properties can occur on daily scales. By testing the effects of variations in lighting conditions and solar geometries, we also demonstrate that, for this data set, remote sensing reflectance should be considered a quasi-inherent optical property.We find that the temporal and spatial chlorophyll concentrations and the magnitudes of inherent optical properties can be accurately assessed using previously published ocean color algorithms.However, changes in the spectral slopes of the inherent optical properties are often poorly retrieved, indicating the need for improvements in the retrieval of optical constituent composition.The characterization of such a dynamic environment provides beneficial insights for future biooptical algorithms.

Aquopts: A multisource processing system for multidimensional bio-optical data integration and correction

Field surveys are an important source of data for several scientific studies. Hydrological optics investigations require a large amount of optical data acquired using sensors from distinct manufacturers built with specific technologies. Consequently, rapid and integrated processing can be difficult due to several challenges, such as non-standard outputs, multidimensional resolutions of data (spatial, temporal, and spectral), mismatched sampling, sensor-specific corrections, and manufacturer proprietary software. In this context, we propose a procedure to overcome the drawbacks of processing multisource and multidimensional datasets. Our main goal is to provide a platform that integrates and analyzes datasets acquired from five sensors commonly used by the hydrological optics community. We can summarize the contribution of this work with three resources: a management model for datasets from field surveys, a processing workflow that describes all the correction steps by grouping protocols to integrate data, and an online system, Aquopts, which is a resources for storage, integration, correction, processing, and analysis. Aquopts has provided resources that have been used in several published studies in recognized geoscience journals supported by our rapid delivering system and integrated dataset processing.

Evaluating semi-analytical algorithms for estimating inherent optical properties in the South China Sea.

Using large amounts of bio-optical data collected in the South China Sea (SCS) from 2003 to 2016, this study checks the consistency between well-known semi-analytical algorithms (SAAs)-the quasi-analytical algorithm (QAA) and the default generalized inherent optical property (GIOP-DC)-in retrieving the non-water absorption coefficient (anw(λ)), phytoplankton absorption coefficient (aph(λ)) and particulate backscattering coefficient (bbp(λ)) from remote-sensing reflectance (Rrs(λ)) data at 412, 443, 490, 531, and 555 nm. The samples from the SCS are further separated into oligotrophic and mesotrophic water types for the comparison of the SAAs. Several findings are made: First, the values of anw(λ) derived from the two SAAs deliver similar performance, with R2 values ranging from 0.74 to 0.85 and 0.74 to 0.87, implying absolute percent error differences (APDs) from 37.93% to 74.88% and from 32.32% to 71.75% for the QAA and GIOP-DC, respectively. The QAA shows a value of R2 between 0.64 and 0.91 and APDs between 43.57% to 83.53%, while the GIOP-DC yields R2 between 0.76 to 0.89 and APDs between 44.65% to 79.46% when estimating aph(λ). The values of bbp(λ) derived from the QAA are closer to the in-situ bbp(λ) values, as indicated by the low values of the normalized centered root-mean-square deviation and normalized standard deviation, which are close to one. Second, a regionally tuned estimation of aph(λ) is proposed and recommended for the SCS. This consistency check of inherent optical properties products from SAAs can serve as reference for algorithm selection for further applications, including primary production, carbon, and biogeochemical models of the SCS, and can provide guidance for improving aph(λ) estimation.

Spatio-temporal variability of red-green chlorophyll-a index from MODIS data – Case study: Chabahar Bay, SE of Iran

Chabahar Bay is a strategic and productive estuary in the south-east of Iran (north of Oman Sea). It is an optically complex bay, and its Chl-a content variations never studied. In this study, Red-Green Chlorophyll Index (RGCI) algorithm for estimating accurate Chl-a content was tested, validated and applied to Moderate Resolution Imaging Spectrometer (MODIS) data, using in situ bio-optical data collected seasonally from 2007 to 2015 in 35 stations. Chl-a concentrations varied from 7.7 to 31.3 mg m−3 with a mean value of 16.4 ± 5.9 mg m−3. The mean absorption at 443 nm and 555 nm data showed that the detrital particles and CDOM contents is relatively low and the mean absorption is dominated by phytoplankton and non-living particles. The RGCI algorithm was tuned for green band center position of 555 nm, and showed improvement over the traditional blue-green band ratio algorithms (e.g. OC3) with mean relative error of 37.4% and RMSE of 73.2% for Chl-a ranging between 2 and 80 mg m−3. The Wavelet Transform (WT) techniques were utilized to analyze the spatio-temporal stability and abnormality of MODIS Chl-a extracted using the tuned RGCI algorithm. Significant variability in time and space was observed, with higher Chl-a in the eastern segment and lower Chl-a in the middle of bay. The highest and lowest Chl-a concentrations were observed in summer and winter, respectively. WT components analysis and anomaly detection revealed the strong correlation of Chl-a concentrations and patterns with turbidity contents and adjacent river discharge. This study showed that the accuracy of RGCI algorithm depends on water body constituents and optimized green waveband position, and therefore the algorithm must be tuned regionally with in situ Chl-a data.

Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry

Abstract. New autonomous robotic platforms for observing the ocean, i.e. Biogeochemical-Argo (BGC-Argo) floats, have drastically increased the number of vertical profiles of irradiance, photosynthetically available radiation (PAR), and algal chlorophyll concentrations around the globe independent of the season. Such data may therefore be a fruitful resource to improve performances of numerical models for marine biogeochemistry. Here we present a work that integrates 1314 vertical profiles of PAR acquired by 31 BGC-Argo floats operated in the Mediterranean Sea between 2012 and 2016 into a one-dimensional model to simulate the vertical and temporal variability of algal chlorophyll concentrations. The model was initially forced with PAR measurements to assess its skill when using quality-controlled light profiles, and subsequently with a number of alternative bio-optical models to analyse the model capability when light observations are not available. Model outputs were evaluated against co-located chlorophyll profiles measured by BGC-Argo floats. Results highlight that the data-driven model is able to reproduce the spatial and temporal variability of deep chlorophyll maxima depth observed at a number of Mediterranean sites well. Further, we illustrate the key role of PAR and vertical mixing in shaping the vertical dynamics of primary producers in the Mediterranean Sea. The comparison of alternative bio-optical models identifies the best simple one to be used, and suggests that model simulations benefit from considering the diel cycle.

Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine

The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (at-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from at-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r2 between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea.

Diurnal regulation of photosynthetic light absorption, electron transport and carbon fixation in two contrasting oceanic environments

Abstract. Understanding the dynamics of marine phytoplankton productivity requires mechanistic insight into the non-linear coupling of light absorption, photosynthetic electron transport and carbon fixation in response to environmental variability. In the present study, we examined the variability of phytoplankton light absorption characteristics, light-dependent electron transport and 14C-uptake rates over a 48 h period in the coastal subarctic north-east (NE) Pacific. We observed an intricately coordinated response of the different components of the photosynthetic process to diurnal irradiance cycles, which acted to maximize carbon fixation, while simultaneously preventing damage by excess absorbed light energy. In particular, we found diurnal adjustments in pigment ratios, excitation energy transfer to reaction centre II (RCII), the capacity for non-photochemical quenching (NPQ), and the light efficiency (α) and maximum rates (Pmax) of RCII electron transport (ETRRCII) and 14C uptake. Comparison of these results from coastal waters to previous observations in offshore waters of the subarctic NE Pacific provides insight into the effects of iron limitation on the optimization of photosynthesis. Under iron-limited, low-biomass conditions, there was a significant reduction of iron-rich photosynthetic units per chlorophyll a, which was partly offset by higher light absorption and electron transport per photosystem II (PSII). Iron deficiency limited the capacity of phytoplankton to utilize peak midday irradiance for carbon fixation and caused an upregulation of photoprotective mechanisms, including NPQ, and the decoupling of light absorption, electron transport and carbon fixation. Such decoupling resulted in an increased electron requirement (Φe,C) and decreased quantum efficiency (ΦC) of carbon fixation at the iron-limited station. In both coastal and offshore waters, Φe,C and ΦC correlated strongly to NPQ, albeit with a significantly different slope. We discuss the implications of our results for the interpretation of bio-optical data and the parameterization of numerical productivity models, both of which are vital tools in monitoring marine photosynthesis over large temporal and spatial scales.

Bio‐Optical Data Assimilation With Observational Error Covariance Derived From an Ensemble of Satellite Images

AbstractAn ensemble‐based approach to specify observational error covariance in the data assimilation of satellite bio‐optical properties is proposed. The observational error covariance is derived from statistical properties of the generated ensemble of satellite MODIS‐Aqua chlorophyll (Chl) images. The proposed observational error covariance is used in the Optimal Interpolation scheme for the assimilation of MODIS‐Aqua Chl observations. The forecast error covariance is specified in the subspace of the multivariate (bio‐optical, physical) empirical orthogonal functions (EOFs) estimated from a month‐long model run. The assimilation of surface MODIS‐Aqua Chl improved surface and subsurface model Chl predictions. Comparisons with surface and subsurface water samples demonstrate that data assimilation run with the proposed observational error covariance has higher RMSE than the data assimilation run with “optimistic” assumption about observational errors (10% of the ensemble mean), but has smaller or comparable RMSE than data assimilation run with an assumption that observational errors equal to 35% of the ensemble mean (the target error for satellite data product for chlorophyll). Also, with the assimilation of the MODIS‐Aqua Chl data, the RMSE between observed and model‐predicted fractions of diatoms to the total phytoplankton is reduced by a factor of two in comparison to the nonassimilative run.

Temporal variability of chlorophyll distribution in the Gulf of Mexico: bio-optical data from profiling floats

Abstract. Chlorophyll concentration is a key oceanic biogeochemical variable. In the Gulf of Mexico (GOM), its distribution, which is mainly obtained from satellite surface observations and scarce in situ experiments, is still poorly understood. In 2011–2012, eight profiling floats equipped with biogeochemical sensors were deployed for the first time in the GOM and generated an unprecedented dataset that significantly increased the number of chlorophyll vertical distribution measurements in the region. The analysis of these data, once calibrated, permits us to reconsider the spatial and temporal variability of the chlorophyll concentration in the water column. At a seasonal scale, results confirm the surface signal seen by satellites, presenting maximum concentrations in winter and low values in summer. It is shown that the deepening of the mixed layer is the primary factor triggering the chlorophyll surface increase in winter. In the GOM, a possible interpretation is that this surface increase corresponds to a biomass increase. However, the present dataset suggests that the basin-scale climatological surface increase in chlorophyll content results from a vertical redistribution of subsurface chlorophyll and/or photoacclimation processes, rather than a net increase of biomass. One plausible explanation for this is the decoupling between the mixed-layer depth and the deep nutrient reservoir since mixed-layer depth only reaches the nitracline in sporadic events in the observations. Float measurements also provide evidence that the depth and the magnitude of the deep chlorophyll maximum is strongly controlled by the mesoscale variability, with higher chlorophyll biomass generally observed in cyclones rather than anticyclones.

Seasonal variability in bio-optical properties along the coastal waters off Cochin

Strong seasonal upwelling, downwelling, changes in current patterns and the volume of freshwater discharge from Cochin Estuary defines the coastal waters off Cochin. These coastal waters were investigated through monthly sampling efforts during March 2015 to February 2016 to study the seasonal and spatial variability in bio-optical properties for the four different seasons mainly Spring Inter Monsoon (SIM), South West Monsoon (SWM), Fall Inter Monsoon (FIM) and Winter Monsoon (WM). The Barmouth region is the meeting place where freshwater from Cochin Estuary directly enters to the sea through a single narrow outlet, was dominated by highly turbid waters during the entire period of study. Among the four seasons, chlorophyll a (Chl_a) concentration showed a high value during SWM, ranged from 2.90 to 11.66 mg m−3 with an average value of 6.56 ± 3.51 mg m−3. During SIM the distribution of coloured dissolved organic matter (CDOM) is controlled by decomposition of phytoplankton biomass and the river discharge, whereas during SWM the temporal distribution of CDOM is controlled only by river discharge. The highest value for CDOM spectral slope (SCDOM) was observed during SWM, ranged from 0.013 to 0.020 nm−1 with an average value of 0.015 ± 0.002 nm−1. During WM, the high SCDOM with lower aCDOM (443) indicates the photo-degradation affects the absorption characteristics of CDOM. The observed nonlinearity between Chl_a and the ratio of phytoplankton absorption aph (443)/aph (670) indicating the packaging effect and changes in the intercellular composition of pigments. During the study period, aph (670) was strongly correlated with Chl_a than aph (443), which explains the accessory pigment absorption dominating more than Chl_a in the blue part of the spectrum. Similarly, the results obtained from seasonal bio-optical data indicating that Chl_a significantly contributes light attenuation of the water column during SIM, whereas detritus (ad) significantly contributes light attenuation during SIM and WM. During the study period, the relative absorption of detritus materials dominates the relative absorption of phytoplankton and CDOM at 443, 555 and 670 nm wavelengths.