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Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean's surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union's Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean's productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m−3). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. [ABSTRACT FROM AUTHOR]

In the oceanic surface layer, particulate organic carbon (POC) constitutes the biggest pool of particulate material of biological origin, encompassing phytoplankton, zooplankton, bacteria, and organic detritus. POC is of general interest in studies of biologically-mediated fluxes of carbon in the ocean, and over the years, several empirical algorithms have been proposed to retrieve POC concentrations from satellite products. These algorithms can be categorised into those that make use of remote-sensing-reflectance data directly, and those that are dependent on chlorophyll concentration and particle backscattering coefficient derived from reflectance values. In this study, a global database of in situ measurements of POC is assembled, against which these different types of algorithms are tested using daily matchup data extracted from the Ocean Colour Climate Change Initiative (OC-CCI; version 5). Through analyses of residuals, pixel-by-pixel uncertainties, and validation based on optical water types, areas for POC algorithm improvement are identified, particularly in regions underrepresented in the in situ POC data sets, such as coastal and high-latitude waters. We conclude that POC algorithms have reached a state of maturity and further improvements can be sought in blending algorithms for different optical water types when the required in situ data becomes available. The best performing band ratio algorithm was tuned to the OC-CCI version 5 product and used to produce a global time series of POC between 1997-2020 that is freely available. [ABSTRACT FROM AUTHOR]

In the oceanic surface layer, particulate organic carbon (POC) constitutes the biggest pool of particulate material of biological origin, encompassing phytoplankton, zooplankton, bacteria, and organic detritus. POC is of general interest in studies of biologically-mediated fluxes of carbon in the ocean, and over the years, several empirical algorithms have been proposed to retrieve POC concentrations from satellite products. These algorithms can be categorised into those that make use of remote-sensing-reflectance data directly, and those that are dependent on chlorophyll concentration and particle backscattering coefficient derived from reflectance values. In this study, a global database of in situ measurements of POC is assembled, against which these different types of algorithms are tested using daily matchup data extracted from the Ocean Colour Climate Change Initiative (OC-CCI; version 5). Through analyses of residuals, pixel-by-pixel uncertainties, and validation based on optical water types, areas for POC algorithm improvement are identified, particularly in regions underrepresented in the in situ POC data sets, such as coastal and high-latitude waters. We conclude that POC algorithms have reached a state of maturity and further improvements can be sought in blending algorithms for different optical water types when the required in situ data becomes available. The best performing band ratio algorithm was tuned to the OC-CCI version 5 product and used to produce a global time series of POC between 1997–2020 that is freely available.

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

Climate change has affected the Arctic Ocean (AO) and its marginal seas significantly. The reduction of sea ice in the Arctic region has altered the magnitude of photosynthetically available radiation (PAR) entering the water column, impacting primary productivity. Increasing cloudiness in the atmosphere and rising turbidity in the coastal waters of the Arctic region are considered as the major factors that counteract the effect of reduced sea ice on underwater PAR. Additionally, extreme solar zenith angles and sea-ice cover in the AO increase the complexity of retrieving PAR. In this study, a PAR algorithm based on radiative transfer in the atmosphere and satellite observations is implemented to evaluate the effect of these factors on PAR in the coastal AO. To improve the performance of the algorithm, a flag is defined to identify pixels containing open-water, sea-ice or cloud. The use of flag enabled selective application of algorithms to compute the input parameters for the PAR algorithm. The PAR algorithm is validated using in situ measurements from various coastal sites in the Arctic and sub-Arctic seas. The algorithm estimated daily integrated PAR above the sea surface with an uncertainty of 19% in summer. The uncertainty increased to 24% when the algorithm was applied year-round. The PAR values at the seafloor were estimated with an uncertainty of 76%, with 36% of the samples under sea ice and/or cloud cover. The robust performance of the PAR algorithm in the pan-Arctic region throughout the year will help to effectively study the temporal and spatial variability of PAR in the Arctic coastal waters. The calculated PAR data are used to quantify the changing trend in PAR at the seafloor in the coastal AO with depth < 100 m using MODIS-Aqua data from 2003 to 2020. The general trends calculated using the pixels with average PAR > 0.415 mol m − 2 day − 1 at the seafloor during summer indicate that the annual average of PAR entering the water column in the coastal AO between 2003 and 2020 increased by 23%. Concurrently, due to increased turbidity, the attenuation in the water column increased by 22%. The surge in incident PAR in the water column due to retreating sea ice first led to increased PAR observed at the seafloor (∼12% between 2003 and 2014). However, in the last decade, the rapid increase in light attenuation of the water column has restricted the increase in average annual PAR reaching the bottom in the coastal AO. [ABSTRACT FROM AUTHOR]

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

A Water Quality Monitor (WQM) equipped with a range of oceanographic sensors was deployed from April 2017 to October 2017 in the North Western (NW) Baltic Sea. We assessed here if the data from a moored chlorophyll-a fluorometer can be used to improve satellite validation in coastal waters. Calibrated mooring data and ship-based chlorophyll-a concentrations from 2017 and 2018 were matched with OLCI (Ocean and Land Colour Instrument) data to validate the C2RCC (Case-2 Regional Coast Colour) processor, a locally-adapted version of C2RCC (LA-C2RCC), as well as the POLYMER processor. Using additional mooring data resulted in a substantial increase in paired observations compared to using ship-based data alone (C2RCC; N = 41–63, LA-C2RCC; N = 37–59, POLYMER; N = 108–166). However, the addition of mooring data only reduced the error and bias of the LA-C2RCC (MNB: from 24 % to 22 %, RMSE: from 60 % to 57 %, APD: both 47 %). In contrast, the statistical errors increased with the addition of mooring data both for C2RCC (MNB: −26 % to −33 %, RMSE: 50 %–51 %, APD 84 %–96 %) and for POLYMER (MNB: 26 %–36 %, RMSE: 79 % to 79 %, APD 64 %–64 %). The results indicate that the locally-adapted version of the C2RCC should be used for the area of investigation. These results are most likely also related to the effect of the System Vicarious Calibration (SVC). As opposed to C2RCC, the locally-adapted version had not been vicariously calibrated. The results indicate that SVC is not beneficial for Baltic Sea data and that more work needs to be done to improve SVC for Baltic Sea waters or for other waters with high CDOM absorption. In order to improve the validation capabilities of moored fluorometers in general, they should be strategically placed in waters with representative ranges of chl-a concentrations for the area of research in question.

A bio-optical model for the Barents Sea is determined from a set of in situ observations of inherent optical properties (IOPs) and associated biogeochemical analyses. The bio-optical model provides a pathway to convert commonly measured parameters from glider-borne sensors (CTD, optical triplet sensor—chlorophyll and CDOM fluorescence, backscattering coefficients) to bulk spectral IOPs (absorption, attenuation and backscattering). IOPs derived from glider observations are subsequently used to estimate remote sensing reflectance spectra that compare well with coincident satellite observations, providing independent validation of the general applicability of the bio-optical model. Various challenges in the generation of a robust bio-optical model involving dealing with partial and limited quantity datasets and the interpretation of data from the optical triplet sensor are discussed. Establishing this quantitative link between glider-borne and satellite-borne data sources is an important step in integrating these data streams and has wide applicability for current and future integrated autonomous observation systems. This article is part of the theme issue 'The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'. [ABSTRACT FROM AUTHOR]

Phytoplankton cell size is a useful ecological indicator for evaluating the response of phytoplankton community structure to environmental changes. Ocean‐color remote observations and algorithms have allowed us to estimate phytoplankton size classes (PSCs) at decadal scale, helping us to understand their trends under ocean warming. Here a large data set of pigments, derived through high performance liquid chromatography, was collected in the Bohai Sea (BS) and Yellow Sea (YS) between 2014 and 2016. The data set was used to reparametrize the sea surface temperature (SST)‐dependent three‐component model of Brewin et al. (2017) to the region. The model was validated using independent in situ data set and subsequently applied to satellite chlorophyll‐a data from Ocean Colour Climate Change Initiative, spanning from 1997 to 2016, to derive percentages of three PSCs to total chlorophyll‐a. Monthly‐averaged PSCs exhibited spatial‐temporal variations in the study area, linked to topography, temperature, solar radiation, currents, and monsoonal winds. In the surface central south Yellow Sea (SYS), influenced by bottom Yellow Sea Cold Water Mass, tight relationships between PSCs and environmental factors were observed, where high SST, high sea level anomaly, low mixed‐layer depth, and low wind speed resulted in higher proportions of nanoplankton and picoplankton from June to October. Significant interannual anomlies in PSCs were found associated with El Niño events in the central SYS, related to anomalies in SST. The refined model characterized 20‐year variations in chlorophyll‐a concentration and PSCs in complicated optical, hydrodynamic, and biogeochemical environments in the BS and YS. Plain Language Summary: Phytoplankton are the fundamental component of the marine ecosystem, and the size structure of phytoplankton influences many processes in phytoplankton biology, marine ecology, and marine biogeochemistry. Phytoplankton can be divided into three phytoplankton size classes (PSCs): microplankton (>20 μm), nanoplankton (2–20 μm), and picoplankton (<2 μm). The Bohai Sea (BS) and Yellow Sea (YS) are shallow marginal seas in the northwest Pacific Ocean, strongly impacted by large river plumes, ocean processes, and seasonal monsoons, supporting high primary and fishery productivity. Using a 20‐year time series satellite ocean color data from 1997 to 2016, and a SST‐dependent model that links chlorophyll‐a concentration to the size structure of phytoplankton, we observe spatial and temporal variations of PSCs in the BS and YS and tight correlations between the size structure and physical variables in the central south Yellow Sea. Interannual variations in the PSCs are coupled with changes of sea surface temperature in El Niño events. Our results demonstrate that variations in the phytoplankton size structure are coupled with changes in climate variability, with implications for how the regional ecosystem may change with predicted changes in climate. Key Points: The SST‐dependent three‐component model was retuned and validated for remotely sensed PSCs in the Bohai Sea and Yellow SeaMonthly variations of PSCs derived from 20‐year time series satellite data were correlated with physical variables in the central Yellow SeaDifferent impacts of each type of El Niño events in climate variability on interannual variations of PSCs were observed and related to SST [ABSTRACT FROM AUTHOR]

The colours from natural waters differ markedly over the globe, depending on the water composition and illumination conditions. The space-borne “ocean colour” instruments are operational instruments designed to retrieve important water-quality indicators, based on the measurement of water leaving radiance in a limited number (5 to 10) of narrow (≈10 nm) bands. Surprisingly, the analysis of the satellite data has not yet paid attention to colour as an integral optical property that can also be retrieved from multispectral satellite data. In this paper we re-introduce colour as a valuable parameter that can be expressed mainly by the hue angle (α). Based on a set of 500 synthetic spectra covering a broad range of natural waters a simple algorithm is developed to derive the hue angle from SeaWiFS, MODIS, MERIS and OLCI data. The algorithm consists of a weighted linear sum of the remote sensing reflectance in all visual bands plus a correction term for the specific band-setting of each instrument. The algorithm is validated by a set of 603 hyperspectral measurements from inland-, coastal- and near-ocean waters. We conclude that the hue angle is a simple objective parameter of natural waters that can be retrieved uniformly for all space-borne ocean colour instruments.

The Gulf of Aden, situated in the northwest Arabian Sea and linked to the Red Sea, is a relatively unexplored ecosystem. Understanding of large-scale biological dynamics is limited by the lack of adequate datasets. In this study, we analyse 15 years of remotely-sensed chlorophyll-a data (Chl- a , an index of phytoplankton biomass) acquired from the Ocean Colour Climate Change Initiative (OC-CCI) of the European Space Agency (ESA). The improved spatial coverage of OC-CCI data in the Gulf of Aden allows, for the first time, an investigation into the full seasonal succession of phytoplankton biomass. Analysis of indices of phytoplankton phenology (bloom timing) reveals distinct phytoplankton growth periods in different parts of the gulf: a large peak during August (mid-summer) in the western part of the gulf, and a smaller peak during November (mid-autumn) in the lower central gulf and along the southern coastline. The summer bloom develops rapidly at the beginning of July, and its peak is approximately three times higher than that of the autumnal bloom. Remotely-sensed sea-surface temperature (SST), wind-stress curl, vertical nutrient profiles and geostrophic currents inferred from the sea-level anomaly, were analysed to examine the underlying physical mechanisms that control phytoplankton growth. During summer, the prevailing southwesterlies cause upwelling along the northern coastline of the gulf (Yemen), leading to an increase in nutrient availability and enhancing phytoplankton growth along the coastline and in the western part of the gulf. In contrast, in the central region of the gulf, lowest concentrations of Chl- a are observed during summer, due to strong downwelling caused by a mesoscale anticyclonic eddy. During autumn, the prevailing northeasterlies enable upwelling along the southern coastline (Somalia) causing local nutrient enrichment in the euphotic zone, leading to higher levels of phytoplankton biomass along the coastline and in the lower central gulf. The monsoon wind reversal is shown to play a key role in controlling phytoplankton growth in different regions of the Gulf of Aden. [ABSTRACT FROM AUTHOR]

The exclusive economic zones (EEZ) of coastal countries are coming under increasing pressure from various economic sectors such as fishing, aquaculture, shipping and energy production. In Europe, there is a policy to expand the maritime economic sector without damaging the environment by ensuring that these activities comply with legally binding Directives, such as the Marine Strategy Framework Directive (MSFD). However, monitoring an extensive maritime area is a logistical and economic challenge. Remote sensing is considered one of the most cost effective methods for providing the spatial and temporal environmental data that will be necessary for the effective implementation of the MSFD. However, there is still a concern about the uncertainties associated with remote sensed products. This study has tested how a specific satellite product can contribute to the monitoring of a MSFD Descriptor for “good environmental status” (GES). The results show that the quality of the remote sensing product Algal Pigment Index 1 (API 1) from the MEdium Resolution Imaging Spectrometer (MERIS) sensor of the European Space Agency for ocean colour products can be effectively validated with in situ data from three stations off the SW Iberian Peninsula. The validation results show good agreement between the MERIS API 1 and the in situ data for the two more offshore stations, with a higher coefficient of determination ( R 2 ) of 0.79, and with lower uncertainties for the average relative percentage difference (RPD) of 24.6% and 27.9% and a root mean square error (RMSE) of 0.40 and 0.38 for Stations B and C, respectively. Near to the coast, Station A has the lowest R 2 of 0.63 and the highest uncertainties with an RPD of 112.9% and a RMSE of 1.00. It is also the station most affected by adjacency effects from the land: when the Improved Contrast between Ocean and Land processor (ICOL) is applied the R 2 increases to 0.77 and there is a 30% reduction in the uncertainties estimated by RPD. The MERIS API 1 product decreases from inshore to offshore, with higher values occurring mainly between early spring and the end of the summer, and with lower values during winter. By using the satellite images for API 1, it is possible to detect and track the development of algal blooms in coastal and marine waters, demonstrating the usefulness of remote sensing for supporting the implementation of the MSFD with respect to Descriptor 5: Eutrophication. It is probable that remote sensing will also prove to be useful for monitoring other Descriptors of the MSFD. [ABSTRACT FROM AUTHOR]

The colours from natural waters differ markedly over the globe, depending on the water composition and illumination conditions. The space-borne "ocean colour" instruments are operational instruments designed to retrieve important water-quality indicators, based on the measurement of water leaving radiance in a limited number (5 to 10) of narrow (≈10 nm) bands. Surprisingly, the analysis of the satellite data has not yet paid attention to colour as an integral optical property that can also be retrieved from multispectral satellite data. In this paper we re-introduce colour as a valuable parameter that can be expressed mainly by the hue angle (α). Based on a set of 500 synthetic spectra covering a broad range of natural waters a simple algorithm is developed to derive the hue angle from SeaWiFS, MODIS, MERIS and OLCI data. The algorithm consists of a weighted linear sum of the remote sensing reflectance in all visual bands plus a correction term for the specific band-setting of each instrument. The algorithm is validated by a set of 603 hyperspectral measurements from inland-, coastal- and near-ocean waters. We conclude that the hue angle is a simple objective parameter of natural waters that can be retrieved uniformly for all space-borne ocean colour instruments. [ABSTRACT FROM AUTHOR]

Hirawake, T., Shinmyo, K., Fujiwara, A., and Saitoh, S. 2012. Satellite remote sensing of primary productivity in the Bering and Chukchi Seas using an absorption-based approach. – ICES Journal of Marine Science, 69: .Ocean colour remote sensing has been utilized for studying primary productivity in the Arctic Ocean. However, phytoplankton chlorophyll a (Chl a) is not predicted accurately because of the interference of coloured dissolved organic matter (CDOM) and non-algal particles (NAP). To enhance the estimation accuracy, a phytoplankton absorption-based primary productivity model (ABPM) was applied to the Bering and Chukchi Seas. The phytoplankton absorption coefficient was determined correctly from sea surface remote sensing reflectance (Rrs) and reduced the effect of CDOM and NAP in primary productivity (PPeu) estimates. PPeu retrieved from in situ Rrs using the ABPM satisfied a factor of 2 of measured values. PPeu estimated from the Moderate Resolution Imaging Spectroradiometer Rrs data were within the range of historical values. These estimated PPeu values were less than half of those of the model based on Chl a, and the difference between the two models reflected the influence of CDOM and NAP absorptions. Interannual variation in August and September over the period 2002–2010 showed an increase in primary productivity. The increase in 2007 was especially large, by a factor of 1.51–2.71, compared with 2006. The significant temporal increase in productivity detected here differs from earlier studies that detected little, if any, change in the region. [ABSTRACT FROM PUBLISHER]

Abstract: Coastal areas of the North Sea are commercially important for fishing and tourism, and are subject to the increasingly adverse effects of harmful algal blooms, eutrophication and climate change. Monitoring phytoplankton in these areas using Ocean Colour Remote Sensing is hampered by the high spatial and temporal variations in absorption and scattering properties. In this paper we demonstrate a clustering method based on specific-absorption properties that gives accurate water quality products from the Medium Resolution Imaging Spectrometer (MERIS). A total of 468 measurements of Chlorophyll a (Chla), Total Suspended Material (TSM), specific- (sIOP) and inherent optical properties (IOP) were measured in the North Sea between April 1999 and September 2004. Chla varied from 0.2 to 35mgm−3, TSM from 0.2 to 75gm−3 and absorption properties of coloured dissolved organic material at 442nm (aCDOM(442)) was 0.02 to 0.26m−1. The variation in absorption properties of phytoplankton (aph) and non-algal particles (aNAP) were an order of magnitude greater than that for aph normalized to Chla (aph*) and aNAP normalized to TSM (aNAP*). Hierarchical cluster analysis on aph*, aNAP* and aCDOM reduced this large data set to three groups of high aNAP*–aCDOM, low aph* situated close to the coast, medium values further offshore and low aNAP*–aCDOM, high aph* in open ocean and Dutch coastal waters. The median sIOP of each cluster were used to parameterize a semi-analytical algorithm to retrieve concentrations of Chla, TSM and aCDOM(442) from MERIS data. A further 60 measurements of normalized water leaving radiance (nLw), Chla, TSM, aCDOM(442) and aNAP(442) collected between 2003 and 2006 were used to assess the accuracy of the satellite products. The regionalized MERIS algorithm showed improved performance in Chla and aCDOM(442) estimates with relative percentage differences of 29 and 8% compared to 34 and 134% for standard MERIS Chla and adg(442) products, and similar retrieval for TSM at concentrations >1g−3. [Copyright &y& Elsevier]

Abstract: In the upwelling area along the Namibian coast of SW-Africa sulphur discolorations were investigated to study the impact of hydrogen sulphide on the ecosystem using satellite imagery. The formation of colloidal sulphur in the upper water layer results from the oxidation of hydrogen sulphide. The occurrence of sulphur plumes as well as their temporal and spatial development was investigated in relation to the driving meteorological and oceanographic conditions. Because of the sporadic occurrence of sulphur events and the limited number of ship-borne investigations in that area remote sensing of ocean colour is the only method to follow these phenomena continuously and synoptically. In the past the sulphur plumes were studied by true colour images derived from ocean colour satellite data like sea-viewing wide field of view sensor or moderate resolution imaging spectroradiometer and identified by their typical milky turquoise discoloration. For the first time, medium resolution imaging spectrometer (MERIS) reduced resolution Level-2 products were applied to identify sulphur discoloration in the surface water off Namibia. Based on their high spectral resolution typical spectral water-leaving reflectances were identified for sulphur enriched waters and distinguished from other optical water types dominated by absorbing or scattering phytoplankton groups and suspended matter. This was the basis for the development of a classification algorithm for the identification of sulphur plumes. This algorithm was derived on available MERIS scenes from the first half of the year 2004 and extended to summer 2005 to study the occurrence, the temporal and spatial development, the extension, and the strength of such events as well as inter-annual differences in these years. Only near-shore sulphur occurrences were identified in the considered time period compared to other studies. A lifetime of sulphur patches between 1 and 6 days and a zonal extent of up to 21km were determined. The patches propagate in north-westerly direction with a current velocity of approximately 14cms−1. The strongest events were detected in the Meob- and Saint Francis Bay in April 2004 and in April 2005. [Copyright &y& Elsevier]

Monitoring and modeling of the distribution of suspended particulate matter (SPM) is an important task, especially in coastal environments. Several SPM models have been developed for the North Sea. However, due to waves in shallow water and strong tidal currents in the southern part of the North Sea, this is still a challenging task. In general there is a lack of measurements to determine initial distributions of SPM in the bottom sediment and essential model parameters, e.g., appropriate exchange coefficients. In many satellite-borne ocean color images of the North Sea a plume is visible, which is caused by the scattering of light at SPM in the upper ocean layer. The intensity and length of the plume depends on the wave and current climate. It is well known that the SPM plume is especially obvious shortly after strong storm events. In this paper a quasi-3-D and a 3-D SPM transport model are presented. Utilizing the synergy of satellite-borne ocean color data with numerical models, the vertical exchange coefficients due to currents and waves are derived. This results in models that for the first time are able to reproduce the temporal and spatial evolution of the plume intensity. The SPM models consist of several modules to compute ocean dynamics, the vertical and horizontal exchange of SPM in the water column, and exchange processes with the seabed such as erosion, sedimentation, and resuspension. In the bottom layer, bioturbation via benthos and diffusion processes is taken into account. [ABSTRACT FROM AUTHOR]

In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case IT waters. The ANN-based algorithms have been developed based on a constraint condition, which represents, to a certain degree, the correlation between suspended particulate matter (SPM) and pigment (CHL), coloured dissolved organic matter (CDOM) and CHL, as well as CDOM and SPM, found in Case II waters. Compared with the ANN-based algorithm developed without a constraint condition, the performance of ANN-based algorithms developed with a constraint conditions is much better for the retrieval of CHL and CDOM, especially in the case of high noise levels; however, there is not significant improvement for the retrieval of SPM. [ABSTRACT FROM AUTHOR]

Time series measurements at the Wadden Sea time series station Spiekeroog (WSS) in the southern North Sea were used to empirically develop approaches for determining turbidity from ocean colour remote sensing products (OCPs). Turbidity was observed by a submerged optical sensor. Radiometric quantities were collected using hyperspectral radiometers. Surface reflected glint correction was applied to the radiometric quantities to compute remote sensing reflectance (RRS) and the RRS was converted into perceived colour of seawater matching the Forel-Ule colour Index (FUI) scale. The empirical approaches for determining turbidity from OCPs showed good least squares linear correlations and statistical significance (R2 > 0.7, p < 0.001). These OCP approaches had relatively low uncertainties in predicting turbidity with encouraging mean absolute percent difference less than 31 %. The problem of bio-fouling on submerged sensors and the potential application of OCPs to monitor or correct for sensor drifts was evaluated. A protocol is proposed for the acquisition and processing of hyperspectral radiometric measurements at this optically complex station. Use of the classic FUI as a time series indicator of surface seawater changes did show promising results. The application of these OCPs in operational monitoring changes in water quality was also explored with the aim to evaluate the potential use of the WSS datasets in calibration and validation of satellite ocean colour remote sensing of these very turbid coastal waters.

In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms. [Display omitted] • A scheme is developed to estimate UV Rrs from visible ocean colour bands. • The scheme works well for both clear oceanic and optically complex waters. • Model-estimated UV Rrs are even better than satellite-sensed values. • The method holds potentials for improving and extending satellite data. [ABSTRACT FROM AUTHOR]

Particulate organic carbon (POC) plays vital roles in marine carbon cycle, serving as a part of "biological pump" moving carbon to the deep ocean. The blue-to-green band ratio algorithm is applied operationally to derive POC concentrations in global oceans; it, however, tends to underestimate high values in optically complex waters. With an attempt to develop accurate and robust oceanic POC models, this study aimed to explore machine learning methods in satellite retrieval of POC concentrations. Three machine learning methods, i.e. extreme gradient boosting (XGBoost), support vector machine (SVM) and artificial neural network (ANN), were tested, and the recursive feature elimination (RFE) method was employed to identify sensitive features. Matchups of global in situ POC measurements and Ocean Colour Climate Change Initiative (OC-CCI) products were used to train and evaluate POC models. Results showed that machine learning methods produced obvious better performance than the blue-to-green band ratio algorithm, and XGBoost was the most robust among the tested three machine learning methods. However, the blue-to-green band ratio algorithm still worked well for clear open ocean waters with low POC, and ANN was more effective for optically complex waters with extremely high POC. This study provided globally applicable methods for satellite retrieval of POC concentrations, which should be helpful for studying POC dynamics in global oceans as well as in productive marginal seas. • Globally oceanic POC models are developed using machine learning methods. • Machine learning methods work well for oligotrophic to productive waters. • Machine learning methods perform better than blue-to-green band ratio algorithm. • Machine learning methods are more robust for retrieving of POC concentrations. [ABSTRACT FROM AUTHOR]