Ocean Color Data Research Articles

Estimating Particulate Inorganic Carbon Concentrations of the Global Ocean From Ocean Color Measurements Using a Reflectance Difference Approach

AbstractA new algorithm for estimating particulate inorganic carbon (PIC) concentrations from ocean color measurements is presented. PIC plays an important role in the global carbon cycle through the oceanic carbonate pump, therefore accurate estimations of PIC concentrations from satellite remote sensing are crucial for observing changes on a global scale. An extensive global data set was created from field and satellite observations for investigating the relationship between PIC concentrations and differences in the remote sensing reflectance (Rrs) at green, red, and near‐infrared (NIR) wavebands. Three color indices were defined: two as the relative height of Rrs(667) above a baseline running between Rrs(547) and an Rrs in the NIR (either 748 or 869 nm), and one as the difference between Rrs(547) and Rrs(667). All three color indices were found to explain over 90% of the variance in field‐measured PIC. But, due to the lack of availability of Rrs(NIR) in the standard ocean color data products, most of the further analysis presented here was done using the color index determined from only two bands. The new two‐band color index algorithm was found to retrieve PIC concentrations more accurately than the current standard algorithm used in generating global PIC data products. Application of the new algorithm to satellite imagery showed patterns on the global scale as revealed from field measurements. The new algorithm was more resistant to atmospheric correction errors and residual errors in sun glint corrections, as seen by a reduction in the speckling and patchiness in the satellite‐derived PIC images.

Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm

AbstractThe objective of this study is to develop an approach to estimate the gelbstoff absorption coefficient (ag) from remote sensing reflectance (Rrs). This approach includes two components: the inherent optical properties are semianalytically derived from the Rrs by a neural quasianalytical algorithm (NQAA), and then the derivations are semianalytically extended to ag estimations using a band difference approach. This method is then evaluated with the various type of ocean color data including synthetic, field measured, and satellite‐observed data. The results show that the method can produce an excellent quantitative agreement between the estimated and known ag in ocean waters with a wide range of optical properties, while significantly reducing the effects of residual error in SeaWiFS Rrs, primarily from the imperfect atmospheric correction algorithm on the retrieval of ag in the clear open oceans. Furthermore, with the application of this new algorithm, the SeaWiFS ag products exhibit more spatially and temporally uniform results than the band ratio approach‐based ag retrieval algorithm. These results indicate that the new algorithm is an encouraging approach to process ocean color images for ag retrieval, although a greater number of independent tests with in situ and satellite data are required to further validate and improve this approach.

Mycosporine-like amino acids and xanthophyll-cycle pigments favour a massive spring bloom development of the dinoflagellate Prorocentrum minimum in Grande Bay (Argentina), an ozone hole affected area

Abstract In Grande Bay (Southern Patagonian Shelf) in a eutrophic and recirculating area slightly stratified during spring, we observed an intense (up to 1 × 10 7 cells L − 1 ) and shallow, quasi mono-specific bloom of the dinoflagellate Prorocentrum minimum . Peridinin was the most abundant carotenoid, but the relative amounts of the xanthophyll cycle carotenoids (diadinoxanthin + diatoxanthin = D T ) to light-harvesting pigments were high (D T /Chl a ratio = 0.32 and D T /peridinin ratio = 0.40). Shinorine, usujirene, palythene, mycosporine-serine-glycine methyl ester and palythenic acid were the primary mycosporine-like amino acids (MAAs), followed by mycosporine-glycine, palythine, and porphyra-334. The ΣMAAs/Chl a ratios (up to 27.9 nmol/nmol) were in the upper range reported either in nutrient-replete dinoflagellate cultures or natural populations. We monitored, from space (using satellite ocean colour data), the spatial and temporal bloom variability (from September 22 to October 31, 2005) using an approach to discriminate dinoflagellate from diatom blooms. The results indicated that an intense diatom bloom started in early spring but was rapidly replaced by an intense bloom of the dinoflagellate P. minimum , although the nutrient concentrations were apparently not limiting. The most notorious change in this period was a sharp increase in the levels of solar UVB radiation (UVB index ~ 9.0) as a consequence of the overpass of the polar vortex over this area. We postulated that the synthesis and accumulation of MAAs and xanthophyll pigments, were competitive advantages for the opportunistic red tide dinoflagellate P. minimum over the sensitive diatoms, favouring the development of their surface blooms in this seasonally solar UVB radiation (UVBR) affected area.

Spaceborne Lidar in the Study of Marine Systems.

Satellite passive ocean color instruments have provided an unbroken ∼20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here, we present a review of the lidar technique, its applications in marine systems, a perspective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multiplatform virtual constellation of observational assets that would enable a three-dimensional reconstruction of global ocean ecosystems.

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, Rrs(λ), was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 105 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance), Chl, are ranging from 0 to 200 mg m-3, concentrations of non-algal particles, NAP, from 0 to 1500 g m-3, and absorption coefficients of chromophoric dissolved organic matter (CDOM) at 440 nm from 0 to 20 m-1. A second, independent, smaller dataset of simulated Rrs(λ) used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ Rrs(λ) dataset collected during a cyanobacterial bloom in Lake Taihu (China) is carried out and the approach predicts blue cyanobacteria to be dominant. This fits well with observation of the blue cyanobacteria Microcystis sp. in the lake. This study provides an efficient approach, which can be promisingly applied to hyperspectral sensors, for identifying dominant phytoplankton spectral groups purely based on Rrs(λ) spectra.

Pan-Arctic optical characteristics of colored dissolved organic matter: Tracing dissolved organic carbon in changing Arctic waters using satellite ocean color data

Light absorption of the colored fraction of dissolved organic matter (CDOM) is a dominant optical component of the Arctic Ocean (AO). Here we show Pan-Arctic characteristics of CDOM light absorption for various Arctic regions covering both coastal and oceanic waters during the Tara Oceans Polar Circle expedition. The Siberian (or eastern) side of the AO is characterized by higher CDOM absorption values compared to the North American (or western) side. This is due to the difference in watersheds between the eastern and western sides of the AO and is consistent with an Arctic absorption database recently built by Matsuoka et al. (2014). A direct comparison between in situ and satellite data demonstrates that CDOM absorption is derived Arctic-wide from satellite ocean color data with an average uncertainty of 12% (root mean square error of 0.3m−1) using our previously published algorithm. For river-influenced coastal waters, we found a single and highly significant relationship between concentrations of dissolved organic carbon (DOC) and CDOM absorption (r2>0.94) covering major Arctic river mouths. By applying this in situ relationship to satellite-derived CDOM absorption, DOC concentrations in the surface waters are estimated for river-influenced coastal waters with an average uncertainty of 28%. Implications for the monitoring of DOC concentrations in Arctic coastal waters are discussed.

Validation and Intercomparison of Ocean Color Algorithms for Estimating Particulate Organic Carbon in the Oceans

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-colour signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean colour data requires algorithms that are well validated, with uncertainties characterised. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean colour data provided by the Ocean Colour Climate Change Initiative (OC-CCI) and validated against the largest database of $\textit{in situ}$ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of \cite{stramski2008} and \cite{loisel2002}) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

Reproduction of Spatio-Temporal Patterns of Major Mediterranean Phytoplankton Groups from Remote Sensing OC-CCI Data

During the last two decades, several satellite algorithms have been proposed to retrieve information about phytoplankton groups using ocean colour data. One of these algorithms, the so-called PHYSAT-Med, was developed specifically for the Mediterranean Sea due to the optical peculiarities of this basin. The method allows the detection from ocean colour images of the dominant Mediterranean phytoplankton groups, namely nanoeukaryotes, Prochlorococcus, Synechococcus, diatoms, coccolithophorids and Phaeocystis-like phytoplankton. Here, we present a new version of PHYSAT-Med applied to the Ocean Colour – Climate Change Initiative (OC-CCI) database. The OC-CCI database consists of a multi-sensor, global ocean-colour product that merges observations from four different sensors. This retuned version presents improvements with respect to the previous version, as it increases the temporal range (since 1998), decreases the cloud cover, improves the bias correction and a validation exercise was performed in the NW Mediterranean Sea. In particular, the PHYSAT-Med version has been used here to analyse the annual cycles of the major phytoplankton groups in the Mediterranean Sea. Wavelet analyses were used to explore the spatial variability in dominance both in the time and frequency domains in several Mediterranean sub-regions, such as the Alboran Sea, Ligurian Sea, Northern Adriatic Sea and Levantine basin. Results extended the interpretation of previously detected patterns, indicating the dominance of Synechococcus-like versus prochlorophytes throughout the year at the basin level, and the predominance of nanoeukaryotes during the winter months. The method successfully reproduced the diatom blooms normally detected in the basin during the spring season (March to April), especially in the Adriatic Sea. According to our results, the PHYSAT-Med OC-CCI algorithm represents a useful tool for the spatio-temporal monitoring of dominant phytoplankton groups in Mediterranean surface waters. The successful applications of other regional ocean colour algorithms to the OC-CCI database will give rise to extended time series of phytoplankton functional types, with promising applications to the study of long-term oceanographic trends in a global change context.

Impacts of pre‐existing ocean cyclonic circulation on sea surface chlorophyll‐a concentrations off northeastern Taiwan following episodic typhoon passages

AbstractOff northeastern Taiwan, enhancement of sea surface chlorophyll‐a (Chl‐a) concentration is frequently found after typhoon passages. From 1998 to 2013, 46 typhoon events are analyzed to examine the variations in Chl‐a concentration from satellite ocean color data. On average, Chl‐a concentration increased by 38% after a typhoon passage. Noticeably, four remarkable Chl‐a increases after typhoons coincide with pre‐existing oceanic cyclones in the study area. The Chl‐a increase is significantly anticorrelated (p < 0.01) with relative SSH, defined as the difference of SSH in the study area and in the surrounding area. To assess the impact of pre‐existing cyclones on the upper ocean response to typhoons, we conduct a series of numerical experiments to simulate the oceanic response to Typhoon Kaemi (2006) with or without a pre‐existing oceanic cyclone, and with or without strong typhoon winds. The results show that the experiment with a pre‐existing oceanic cyclone produces the largest upwelling due to cyclone intensification, mainly induced by the positive wind stress curl dipole northeast of Taiwan.

Response to Temperature of a Class of In Situ Hyperspectral Radiometers

AbstractThe response to temperature of sample hyperspectral radiometers commonly used to support the validation of satellite ocean color data was characterized in the 400–800-nm spectral range. Measurements performed in the 10°–40°C interval at 5°C increments showed mean temperature coefficients varying from −0.04 × 10−2 (°C)−1 at 400 nm to +0.33 × 10−2 (°C)−1 at 800 nm, which are largely explained by the temperature coefficient of the photodetector array constituting the core of the sensor. Overall, the results indicate the possibility of applying temperature corrections with an uncertainty of approximately 0.03 × 10−2 (°C)−1 for the class of hyperspectral radiometers investigated in the study.

Inflow of shelf waters into the Mississippi Sound and Mobile Bay estuaries in October 2015

The exchange of coastal waters between the Mississippi Sound (MSS), Mobile Bay, and Mississippi Bight is an important pathway for oil and pollutants into coastal ecosystems. This study investigated an event of strong and persistent inflow of shelf waters into MSS and Mobile Bay during October 2015 by combining in situ measurements, satellite ocean color data, and ocean model predictions. Navy Coastal Ocean Model predicted high-salinity shelf waters continuously flowing into the estuarine system and forecasted low-salinity waters trapped inside the estuaries which did not flush out until the passage of tropical cyclone Patricia’s remnants in late October. The October 2015 chlorophyll-a anomaly was significantly low inside and outside the MSS for the 2003 to 2015 time series. Similar low-chlorophyll-a anomalies were only seen in 2003. The October 2015 mean in situ salinities were up to 8 psu higher than mean from 2007 to 2015, and some estuarine stations showed persistent salinities above 30 psu for almost a month in agreement with model predictions. October 2015 was associated with low fall seasonal discharge, typical of fall season, and wind which was persistently out of the east to southeast [45–180]°. These persistent wind conditions were linked to the observed anomalous conditions.

Glider and satellite monitoring of the variability of the suspended particle distribution and size in the Rhône ROFI

An experiment was carried out in the Gulf of Lions (NW Mediterranean) in February 2014 to assess the temporal and spatial variability of the distribution and size of suspended particulate matter (SPM) in the Rhône Region of Freshwater Influence (ROFI). A set of observations from an autonomous underwater glider, satellite ocean color data, and meteorological and hydrological time-series data highlighted the high variability of the Rhône River surface turbid plume and presence of a bottom nepheloid layer (BNL) that depended on wind and river discharge conditions. While continental winds pushed the surface plume offshore, marine winds pressed the plume at the coast and favored the sedimentation of as well as nourishment of the BNL. Moderate storm events favored breakage of the plume stratification and along-shelf transport of Rhône River particles. The spectral slopes of glider and satellite-derived light backscattering coefficients, γ, were used as a proxies of the SPM size distribution. The results clearly showed that the change of the SPM size in the nepheloid layers was induced by the flocculation of fine sediments, which became finer seaward throughout the ROFI, as well as the effect of rough weather in the breakup of flocs.

Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products

The only observations of marine biogeochemistry with routine global coverage are satellite ocean colour, which provide measurements of ocean bio-optical properties, with coverage still incomplete and limited to the sea surface. Models are therefore required to provide full spatial coverage, and through data assimilation can be combined with observations to create a reanalysis. This can then be used to investigate both observed and non-observed variables, including those relating to the carbon cycle. As part of the Climate Modelling User Group (CMUG) within the European Space Agency's Climate Change Initiative (CCI) project, two global marine biogeochemical reanalyses have been produced by assimilating ocean colour-derived chlorophyll data into a coupled physical-biogeochemical ocean model over the period September 1997 to July 2012. One reanalysis assimilated CCI products, the other assimilated GlobColour products, with a non-assimilative hindcast run for comparison. Each has been validated against independent in situ observations of chlorophyll, nutrients and carbon cycle variables. The assimilation of either source of ocean colour data was found to improve the model's representation of chlorophyll concentration throughout the water column, including the frequency and positioning of deep chlorophyll maxima. The assimilation also resulted in a slight improvement in nutrient concentrations and surface fugacity of carbon dioxide compared with in situ observations, although the overall impact on mean fields was small. This was found to be due in part to cancelling errors within the model, with the assimilation providing information on model biases, which can be used to inform future climate model development. The reanalyses were also able to reproduce expected seasonal cycles, as well as inter-annual variability related to major climate drivers. This study concludes that both CCI and GlobColour products are suitable for assimilation purposes, and that assimilating ocean colour data is of clear benefit.

SeaConditions: a web and mobile service for safer professional and recreational activities in the Mediterranean Sea

Abstract. Reliable and timely information on the environmental conditions at sea is key to the safety of professional and recreational users as well as to the optimal execution of their activities. The possibility of users obtaining environmental information in due time and with adequate accuracy in the marine and coastal environment is defined as sea situational awareness (SSA). Without adequate information on the environmental meteorological and oceanographic conditions, users have a limited capacity to respond, which has led to loss of lives and to large environmental disasters with enormous consequent damage to the economy, society and ecosystems. Within the framework of the TESSA project, new SSA services for the Mediterranean Sea have been developed. In this paper we present SeaConditions, which is a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. Model forecasts and satellite products from operational services, such as ECMWF and CMEMS, can be visualized in SeaConditions. In addition, layers of information related to bathymetry, sea level and ocean-colour data (chl a and water transparency) are displayed. Ocean forecasts at high spatial resolutions are included in the version of SeaConditions presented here. SeaConditions provides a user-friendly experience with a fluid zoom capability, facilitating the appropriate display of data with different levels of detail. SeaConditions is a single point of access to interactive maps from different geophysical fields, providing high-quality information based on advanced oceanographic models. The SeaConditions services are available through both web and mobile applications. The web application is available at www.sea-conditions.com and is accessible and compatible with present-day browsers. Interoperability with GIS software is implemented. User feedback has been collected and taken into account in order to improve the service. The SeaConditions iOS and Android apps have been downloaded by more than 105 000 users to date (May 2016), and more than 100 000 users have visited the web version.

Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-colour data. There is a growing demand from the ecosystem modelling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeller these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modellers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size (pico- (20μm)). The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterise the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.

Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohen's κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012–2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.

Assessment of Radiometric Data from a Buoy in the St. Lawrence Estuary

AbstractFisheries and Oceans Canada maintains a network of scientific buoys in the St. Lawrence estuary and gulf. Among a suite of environmental parameters documented, the in-water upwelling radiance is measured using an ocean color radiometer located underneath the center of the buoy. The shadow effect from the 1.05-m-radius buoy on the measured upwelling radiance is estimated and empirical models to correct for it are proposed. On average, the shading error (i.e., the percent of missing radiance) was and for the 555- and 412-nm channels, respectively. Two analytical models were tested to predict the shading error using measured inherent optical properties, the sun zenith angle, and the fraction of diffuse sky irradiance. Neglecting light scattering led to overestimates of the shading error. In contrast, the bias was removed when the scattering coefficient was accounted for, but the overall error was only barely improved (root-mean-square error ). Empirical relationships based on the uncorrected reflectance ratio measured by the buoy were used to predict both the shading error and the diffuse attenuation of the upwelling radiance, two quantities needed to calculate remote sensing reflectance . Overall, was retrieved with an averaged absolute percent difference ranging from 12% to 20%, which appears adequate for the validation of ocean color data such as the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) and Visible Infrared Imaging Radiometer Suite (VIIRS) products in the optically complex waters of the St. Lawrence estuary.

On the use of optical remote sensing to assess phytoplankton biomass dynamics in the Pelagos Sanctuary (Ligurian-Provençal Sea)

If Marine Protected Areas (MPAs) are to aid in protecting and conserving marine ecosystems in the face of extensive and severe impacts of human activities, underpinning scientific knowledge and understanding of MPAs are imperative. This study aims to describe and evaluate phytoplankton dynamics in a pelagic Mediterranean MPA (The Pelagos International Marine Mammals Sanctuary) in the north-western Mediterranean Sea as a means to understand better the spatial and temporal distribution of basal ecosystem resources. Ocean colour data collected by orbital remote sensing (Sea-viewing Wide Field-of-view Sensor [SeaWiFS] data) were analysed to elucidate spatial and temporal variations in the concentration of chlorophyll-like pigments (chl). Mean chl images were generated for consecutive 10-day periods, to provide quasi-continuous coverage of the study area, combined with a Principal Component Analysis and analysis of a time series ​(1998–2004). The chl surface patterns traced seasonal upwelling events recurring annually, demonstrating the critical role of physical oceanography in sustaining the food web of the PelagosSanctuary. Phytoplankton blooms, however, differ in terms of peak timing, spatial distribution and chl concentration, due in part to the influence of broad-scale environmental factors that are beyond direct and regional control. Further research is needed to elucidate the relationship between the biomass of primary and secondary producers to understand better the implications of phytoplankton biomass distribution in space and time for charismatic and protected biota such as cetaceans.

An Empirical Ocean Colour Algorithm for Estimating the Contribution of Coloured Dissolved Organic Matter in North-Central Western Adriatic Sea

The performance of empirical band ratio models were evaluated for the estimation of Coloured Dissolved Organic Matter (CDOM) using MODIS ocean colour sensor images and data collected on the North-Central Western Adriatic Sea (Mediterranean Sea). Relationships between in situ measurements (2013–2016) of CDOM absorption coefficients at 355 nm (aCDOM355) with several MODIS satellite band ratios were evaluated on a test data set. The prediction capability of the different linear models was assessed on a validation data set. Based on some statistical diagnostic parameters (R2, APD and RMSE), the best MODIS band ratio performance in retrieving CDOM was obtained by a simple linear model of the transformed dependent variable using the remote sensing reflectance band ratio Rrs(667)/Rrs(488) as the only independent variable. The best-retrieved CDOM algorithm provides very good results for the complex coastal area along the North-Central Western Adriatic Sea where the Po River outflow is the main driving force in CDOM and nutrient circulation, which in winter mostly remains confined to a coastal boundary layer, whereas in summer it spreads to the open sea as well.

An improved spectral optimization algorithm for atmospheric correction over turbid coastal waters: A case study from the Changjiang (Yangtze) estuary and the adjacent coast

Remote sensing-based retrieval of the concentrations of water components relies largely on the accuracy of the atmospheric correction. Although a variety of atmospheric correction algorithms have been developed for turbid waters, the water-leaving reflectance is still underestimated in extremely turbid waters, such as in the Changjiang (Yangtze) estuary and the adjacent coast. To address this issue, this paper proposes an improved algorithm that is based on a spectral optimization algorithm (ESOA) with a coupled water-atmosphere model. The model combines an aerosol model that is constructed from Aerosol Robotic Network (AERONET) observation data and a simple semi-empirical radiative transfer (SERT) model (Shen et al. 2010) for water component retrieval. Four unknown parameters are involved in the coupled model: the relative humidity (RH), fine-mode fraction (FMF), aerosol optical thickness in the near-infrared (NIR) wavelength τa(λ0) and suspended particulate matter (SPM) concentration (Cspm). These parameters are estimated by a global optimization approach that is based on a genetic algorithm (GA) without any initial inputs. Validation results of the atmospherically corrected remote sensing reflectance Rrs(λ) from matchups between Geostationary Ocean Color Imager (GOCI) data and in situ data show that the algorithm has satisfactory accuracy. The root mean square error (RMSE) and the absolute percentage difference (APD) are 0.0089 and 35.12, respectively. By contrast, the Rrs(λ) values retrieved from the same matchups using the GOCI data processing system (GDPS) have higher RMSE and APD of 0.0104 and 69.15, respectively. The ESOA method can be implemented conveniently within the open source code of SeaDAS (v7.1) as an alternative and operational tool for atmospheric correction of ocean color data, including GOCI, MERIS and MODIS, over highly turbid estuarine and coastal regions, such as the Yangtze estuary, the Hangzhou Bay and most of the coastal ocean in Eastern China.