A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters

Modelling the upwelling radiance detected in a seawater column for oil-in-water emulsion tracking

This study concerns changes in the solar light transfer in seawater as a result of the appearance of oil substances in the form of oil-in-water emulsions. The expected effect of the studies is to gather knowledge that would be useful in designing an optical sensor for monitoring oil substances penetrating the seawater column. The paper presents the process of the Monte Carlo modelling of the upwelling radiance detected by a virtual underwater sensor. Moreover, this article discusses the predicted difference between the intensity of the signal from the upwelling radiance meter and the signal from the downwelling radiance meter. The modelling results suggest that in order to obtain a strong signal indicating the presence of oil substances, the radiance meter should receive light running upwards in the sea depth.

Water Turbidity and Suspended Particulate Matter Concentration at Dredged Material Dumping Sites in the Southern Baltic

Dredged material dumping is an activity that causes some of the greatest changes in coastal waters. It results in the need to regularly monitor the properties of seawater related to water quality. In this study, we present the first wide-ranging attempt to correlate seawater turbidity and suspended particulate matter (SPM) concentrations within dumping sites and adjacent waters on the basis of in situ measurements. In the years 2019-2020, we examined four dumping sites, namely Darłowo, Gdynia, Gdańsk, and DCT, located in Polish coastal waters of the Baltic Sea, in the course of four measurement campaigns conducted in the spring, summer, autumn, and winter. The measurements were conducted using a turbidity sensor to determine the turbidity, in formazin turbidity units (FTU), a spectrophotometer to determine the concentrations of nutrients (total phosphorus (P-tot), phosphate phosphorus (P-PO4-3), total nitrogen (N-tot), ammonium nitrogen (N-NH4+), and nitrate nitrogen (N-NO3-)), as well as glass microfiber filters to determine the concentrations of SPM. The analysis of the relationship between the turbidity and SPM within the dumping sites in comparison to reference points showed that the dumping sites are very complex waters and that the issue must be approached locally. The highest turbidity values were registered in the spring, and they correlated linearly with the SPM concentrations (R2 = 0.69). Moreover, we performed a statistical cluster analysis to illustrate the similarities between sampling points in the four dumpsites based on nutrient concentrations. We conclude that the influence of the dumping sites on the local bio-optical and chemical properties significantly exceeds their borders and spreads to the adjacent waters. Nutrient concentrations in many cases exceeded the legal policy limits.

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R2 > 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission's Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblages.

Field-Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel-3A OLCI and Ground-Based Hyperspectral Spectroradiometers

Frequent Aureoumbra lagunensis blooms in the Indian River Lagoon (IRL), Florida, have devastated populations of seagrass and marine life and threaten public health. To substantiate a more reliable remote sensing early-warning system for harmful algal blooms, we apply varimax-rotated principal component analysis (VPCA) to 12 images spanning ~1.5 years. The method partitions visible-NIR spectra into independent components related to algae, cyanobacteria, suspended minerals, and pigment degradation products. The components extracted by VPCA are diagnostic for identifiable optical constituents, providing greater specificity in the resulting data products. We show that VPCA components retrieved from Sentinel-3A Ocean and Land Colour Instrument (OLCI) and a field-based spectroradiometer are consistent despite vast differences in spatial resolution (~50 cm vs. 300 m). Furthermore, the VPCA components associated with A. lagunensis in both spectral datasets indicate high correlations to Ochrophyta cell counts (R2 ≥ 0.92, p < 0.001). Recombining components exhibiting a red-edge response produces a Chl a algorithm that outperforms empirical band ratio algorithms and preforms as well or better than a variety of semianalytical algorithms. The results from the VPCA spectral decomposition method are more efficient than traditional Empirical Orthogonal Function or PCA, requiring fewer components to explain as much or more variance. Overall, our observations provide excellent validation for Sentinel-3A OLCI-based VPCA spectral identification and indicate A. lagunensis was highly concentrated within the Banana River region of the IRL during the study. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

Detection of Phytoplankton Temporal Anomalies Based on Satellite Inherent Optical Properties: A Tool for Monitoring Phytoplankton Blooms

The baseline of a specific variable defines the average behavior of that variable and it must be built from long data series that represent its spatial and temporal variability. In coastal and marine waters, phytoplankton can produce blooms characterized by a wide range of total cells number or chlorophyll a concentration. Classifying a phytoplankton abundance increase as a bloom depends on the species, the study area and the season. The objective of this study was to define the baseline of satellite absorption coefficients in Todos Santos Bay (Baja California, Mexico) to determine the presence of phytoplankton blooms based on the satellite inherent optical properties index (satellite IOP index). Two field points were selected according to historical bloom reports. To build the baseline, the data of phytoplankton absorption coefficients ( a p h y , G I O P ) and detritus plus colored dissolved organic matter (CDOM) ( a d C D O M , G I O P ) from the generalized inherent optical property (GIOP) satellite model of the NASA moderate resolution imaging spectroradiometer (MODIS-Aqua) sensor was studied for the period 2003 to 2016. Field data taken during a phytoplankton bloom event on June 2017 was used to validate the use of satellite products. The association between field and satellite data had a significant positive correlation. The satellite baseline detected a trend change from high values to low values of the satellite IOP index since 2010. Improved wastewater treatment to waters discharged into the Bay, and increased aquaculture of filter-feeding mollusks could have been the cause. The methodology proposed in this study can be a supplementary tool for permanent in situ monitoring programs. This methodology offers several advantages: A complete spatial coverage of the specific coastal area under study, appropriate temporal resolution and a tool for building an objective baseline to detect deviation from average conditions during phytoplankton bloom events.

Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

Phytoplankton in the ocean are extremely diverse. The abundance of various intracellular pigments are often used to study phytoplankton physiology and ecology, and identify and quantify different phytoplankton groups. In this study, phytoplankton absorption spectra ( a p h ( λ ) ) derived from underway flow-through AC-S measurements in the Fram Strait are combined with phytoplankton pigment measurements analyzed by high-performance liquid chromatography (HPLC) to evaluate the retrieval of various pigment concentrations at high spatial resolution. The performances of two approaches, Gaussian decomposition and the matrix inversion technique are investigated and compared. Our study is the first to apply the matrix inversion technique to underway spectrophotometry data. We find that Gaussian decomposition provides good estimates (median absolute percentage error, MPE 21–34%) of total chlorophyll-a (TChl-a), total chlorophyll-b (TChl-b), the combination of chlorophyll-c1 and -c2 (Chl-c1/2), photoprotective (PPC) and photosynthetic carotenoids (PSC). This method outperformed one of the matrix inversion algorithms, i.e., singular value decomposition combined with non-negative least squares (SVD-NNLS), in retrieving TChl-b, Chl-c1/2, PSC, and PPC. However, SVD-NNLS enables robust retrievals of specific carotenoids (MPE 37–65%), i.e., fucoxanthin, diadinoxanthin and 19 ′ -hexanoyloxyfucoxanthin, which is currently not accomplished by Gaussian decomposition. More robust predictions are obtained using the Gaussian decomposition method when the observed a p h ( λ ) is normalized by the package effect index at 675 nm. The latter is determined as a function of “packaged” a p h ( 675 ) and TChl-a concentration, which shows potential for improving pigment retrieval accuracy by the combined use of a p h ( λ ) and TChl-a concentration data. To generate robust estimation statistics for the matrix inversion technique, we combine leave-one-out cross-validation with data perturbations. We find that both approaches provide useful information on pigment distributions, and hence, phytoplankton community composition indicators, at a spatial resolution much finer than that can be achieved with discrete samples.