Ocean Colour Climate Change Initiative Research Articles

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.

Satellite data is revealing long time changes in the world largest lakes

Lakes are a crucial source of drinking water, provide ecological services from fisheries and aquaculture to tourism and are also a critical part of the global carbon cycle. Therefore, it is important to understand how lakes are changing over time. The ESA Ocean Colour Climate Change Initiative (OC-CCI) database allows to study changes in the largest lakes over 1997–2023 period. The Caspian Sea and ten next largest lakes were under investigation. Changes in the phytoplankton biomass (Chl-a), the concentration of particulate matter (bbp(555)), the colored dissolved organic matter, CDOM (adg(412)), and the light diffuse attenuation coefficient in water (Kd(490)) were analyzed. Both increasing and decreasing trends (or no significant trend at all) of studied parameters were observed in these lakes over the study period. In some of the Laurentian Great Lakes the changes in CDOM over the study period were found to be in accordance with the lake water level changes i.e. with the inflow from the catchment. There was difference between the trends of Chl-a and bbp(555) in lakes Michigan and Huron indicating that there may have been shift in phytoplankton community that took place around 2005. The study demonstrated that remote sensing products, like the ones created by ESA OC-CCI, are valuable tools to study behavior of large lakes ecosystems over time.

Comparison of ocean-colour algorithms for particulate organic carbon in global ocean

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.

Regional to global assessments of ocean transparency dynamics from 1997 to 2019

Water transparency, often quantified using the Secchi disk depth (Zsd), is a key parameter for assessing the water quality and ecological health in marine environments. However, a limited understanding of global Zsd dynamics over the past two decades exists. To assess the variations of ocean transparency over multiple timescales, the Ocean Colour Climate Change Initiative (OC-CCI) datasets are used to retrieve monthly global Zsd products during 1997–2019. On regional to global scales, significant Zsd variations on seasonal, interannual, and long-term timescales are identified. Seasonal and interannual variations of Zsd in most open oceans are found to be closely linked to phytoplankton dynamics and climate change. In the coastal zones, the Zsd variability is attributed to various physical and environmental factors, including seasonal monsoons, river outflow, sediment resuspension, eutrophication, etc. Moreover, the climatological seasonal Zsd variations of 22 typical ocean areas and the associated influencing factors are investigated on regional scales. The long-term trends of Zsd suggest the widespread expansion of oligotrophic waters within the North Pacific, North Atlantic, and South Indian Ocean gyres, indicating ongoing ocean desertification. The OC-CCI Zsd datasets can be used to build a merged centennial time series of Zsd in marine optics by linking to historical measurements. By relying on satellite-derived products, global and time-varying images offer a more comprehensive understanding of regional to global Zsd dynamics.

A new global oceanic multi-model net primary productivity data product

Abstract. Net primary production of the oceans contributes approximately half of the total global net primary production, and long-term observational records are required to assess any climate-driven changes. The Ocean Colour Climate Change Initiative (OC-CCI) has proven to be robust whilst also being one of the longest records of ocean colour. However, to date, only one primary production algorithm has been applied to this data product, with other algorithms typically applied to single-sensor missions. The data product presented here addresses this issue by applying five algorithms to the OC-CCI data product, which allows the user to interrogate the range of distributions across multiple models and to identify consensus or outliers for their specific region of interest. Outputs are compared to single-sensor data missions, highlighting good overall global agreement, with some small regional discrepancies. Inter-model assessments address the source of these discrepancies, highlighting the choice of the mixed-layer data product as a vital component for accurate primary production estimates. The datasets are published in the Zenodo repository at https://doi.org/10.5281/zenodo.7849935, https://doi.org/10.5281/zenodo.7858590, https://doi.org/10.5281/zenodo.7860491 and https://doi.org/10.5281/zenodo.7861158 (Ryan-Keogh et al., 2023a, b, c, d).

Review of algorithms estimating export production from satellite derived properties

Whereas the vertical transport of biomass from productive surface waters to the deep ocean (the biological pump) is a critical component of the global carbon cycle, its magnitude and variability is poorly understood. Global-scale estimates of ocean carbon export vary widely, ranging from ∼5 to ∼20 Gt C y – 1 due to uncertainties in methods and unclear definitions. Satellite-derived properties such as phytoplankton biomass, sea surface temperature, and light attenuation at depth provide information about the oceanic ecosystem with unprecedented coverage and resolution in time and space. These products have been the basis of an intense effort over several decades to constrain different biogeochemical production rates and fluxes in the ocean. One critical challenge in this effort has been to estimate the magnitude of the biological pump from satellite-derived properties by establishing how much of the primary production is exported out of the euphotic zone, a flux that is called export production. Here we present a review of existing algorithms for estimating export production from satellite-derived properties, available in-situ datasets that can be used for testing the algorithms, and earlier evaluations of the proposed algorithms. The satellite-derived products used in the algorithm evaluation are all based largely on the Ocean Colour Climate Change Initiative (OC-CCI) products, and carbon products derived from them. The different resources are combined in a meta-analysis.

Ocean color algorithm for the retrieval of the particle size distribution and carbon-based phytoplankton size classes using a two-component coated-sphere backscattering model

Abstract. The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g., in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs), and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations: phytoplankton and non-algal particles (NAPs). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAPs are modeled as homogeneous spheres. The forward model uses Mie and Aden–Kerker scattering computations, for homogeneous and coated spheres, respectively, to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via spectral angle mapping (SAM), which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e., carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. This model formulation also allows the estimation of chlorophyll a concentration via the retrieved PSD, as well as percent of backscattering due to NAPs vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented using PSD, POC, and picophytoplankton carbon in situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of PSD, phytoplankton carbon, and related variables to facilitate future algorithm improvements.

Global ocean colour trends in biogeochemical provinces

Satellite-derived ocean colour data provide continuous, daily measurements of global waters and are an essential tool for monitoring these waters in a changing climate. Merging observations from different satellite sensors is necessary for long-term and continuous climate research because the lifetime of these sensors is limited. A key issue in deriving long-term trends from merged ocean colour data is the inconsistency between the spatiotemporal coverage of the different sensor datasets that can lead to spurious multi-year fluctuations or trends in the time series. This study used the merged ocean colour satellite dataset produced by the Ocean Colour Climate Change Initiative (OC-CCI version 6.0) to infer global and local trends in optically active constituents. We applied a novel correction method to the OC-CCI dataset that results in a spatiotemporally consistent dataset, allowing the examination of long-term trends of optically active constituents with greater accuracy. We included sea surface temperature, salinity, and several climate oscillations in our analysis to gain insight into the underlying processes of derived trends. Our results indicate a significant increase in chlorophyll-a concentration in the polar waters, a decrease in chlorophyll-a concentration in some equatorial waters, and point to ocean darkening, predominantly in the polar waters, due to an increase in non-phytoplankton absorption. This study contributes to broader knowledge of global trends of optically active constituents and their relation to a changing environment.

Merged Multi-Sensor Ocean Colour Chlorophyll Product Evaluation for the British Columbia Coast

Phytoplankton phenology studies require a dataset that is continuous in time and space since missing data have been shown to affect the accuracy of seasonality metrics. The interpolated GlobColour product provided by the Copernicus Marine Environment Monitoring Service (CMEMS) meets these requirements by being ‘gap filled’, thus yielding the highest spatial coverage. Despite being validated on a global scale, a regional comparison to in situ Chl-a concentrations should be conducted to enable product application in optically complex waters. This study aims to evaluate the performance of the GlobColour interpolated product in British Columbia coastal waters via a statistical match-up analysis and a qualitative analysis to determine whether the data reflect the region’s large-scale seasonal trends and latitudinal dynamics. Additionally, the statistical performance of the GlobColour interpolated product was compared to the original GlobColour and Ocean Colour Climate Change Initiative (OC-CCI) merged chlorophyll-a products based on in situ observations. The GlobColour interpolated product performed relatively well and was comparable to the best-performing product for each water type (RMSE = 0.28, r2 = 0.77, MdAD = 1.5, BIAS = 0.90). The statistics for all the products degraded in Case 2 waters, thus highlighting the dilemma of applying algorithms designed for Case 1 waters in Case 2 waters. Our results indicate how the quality of products can vary in different environmental conditions.

A new merged dataset of global ocean chlorophyll-a concentration for better trend detection

Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS (Sea-viewing Wide Field-of-view Sensor) in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve climate change related studies, it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI (Ocean Colour Climate Change Initiative), which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the inter-mission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the SeaWiFS, MODIS (Medium-spectral Resolution Imaging Spectrometer), MERIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), and OLCI (Ocean and Land Colour Instrument) with inter-mission differences corrected in this work. The fitness of the dataset on long-term Chla trend study was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. The results suggest that our dataset can be used for long-term series analysis and trend detection. We also provide the global trend map in Chla over 23 years (1998–2020) and present a significant positive global trend with 0.67% ± 0.37%/yr.

A compilation of global bio-optical in situ data for ocean colour satellite applications – version three

Abstract. A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022).

Differential Response in Chlorophyll-a Concentration to Seasonal Forcings along the West Coast of India

The chlorophyll-a (Chl-a) concentration, an indicator of biomass that is a conduit for fixation of atmospheric carbon dioxide, is analyzed using Ocean Colour Climate Change Initiative (OC-CCI) 4-km eight-day resolution data along the west coast of India. A peak of Chl-a blooms during the southwest monsoon (SWM) along the west coast of India and during northeast monsoon (NEM) in northern regions is observed. The blooms start as early as the end of April in southern regions and spread northward. The Fourier transformation and wavelet analysis explicitly reveals the annual and seasonal variability of these blooms and aids in grouping the regions into three zones. The blooms are strong in the southern regions and occur during SWM, whereas in the northern regions, they occur during SWM as well as NEM. The Indian Ocean Dipole (IOD) and Niño3.4 is negatively correlated with Chl-a concentration in southern regions only during a few months. Similarly, only for a few months do the northern regions show negative correlations between Niño3.4 and Chl-a concentration. The Chl-a blooms are positively correlated with concurrent Ekman mass transport and precipitation and with a lag of four to seven steps of eight-day cycles in southern regions, whereas the northern regions are positively correlated with concurrent precipitation only. The time of onset and end of blooms and the time span for their northward spread during SWM and southward spread during NEM vary from year to year. The different onset and end times of blooms and varying periods of blooms are vital for policy decisions on regulating fishing activity and establishing a ban period along the west coast of India for sustainable utilization of fishery resources.

Correction of inter-mission inconsistencies in merged ocean colour satellite data

Consistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites. Although bias corrections have been performed on merged data set products, significant inconsistencies between missions remain. These inconsistencies appear as sudden steps in the time series of these products when a satellite mission is launched into- or removed from orbit. This inter-mission inconsistency is not caused by poor correction of sensor sensitivities but by differences in the ability of a sensor to observe certain waters. This study, based on a data set compiled by the ‘Ocean Colour Climate Change Initiative’ project (OC-CCI), shows that coastal waters, high latitudes, and areas subject to changing cloud cover are most affected by coverage variability between missions. The “Temporal Gap Detection Method” is introduced, which temporally homogenises the observations per-pixel of the time series and consequently minimises the magnitude of the inter-mission inconsistencies. The method presented is suitable to be transferred to other merged satellite-derived data sets that exhibit inconsistencies due to changes in coverage over time. The results provide insights into the correct interpretation of any merged ocean colour time series.

Variability of Chlorophyll-a and Secchi Disk Depth (1997–2019) in the Bohai Sea Based on Monthly Cloud-Free Satellite Data Reconstructions

Ocean colour data are crucial for monitoring and assessing marine ecosystems. In this study, the Data Interpolating Empirical Orthogonal Functions (DINEOF) approach was applied to the Ocean Colour Climate Change Initiative (OC-CCI), chlorophyll-a (Chl-a) and Secchi disk depth (Zsd) to completely reconstruct the missing pixels in the Bohai Sea during 1997–2019. The results of cross-validation demonstrate that the DINEOF reconstructed data have a good agreement with the satellite-measured data. Based on monthly cloud-free satellite data reconstructions, the Zsd series showed high negative correlation with log10 (Chl-a). The Zsd as a function of log10 (Chl-a) can be well fitted by the cubic polynomial in the offshore waters. The Chl-a in the entire Bohai Sea showed a significant decreasing trend (−0.013 mg/m3/year), while the Zsd exhibited a significant increasing trend (0.0065 m/year), and both had regional-seasonal variations. In addition, the ensemble empirical mode decomposition (EEMD) results reveal highly nonlinear trends of Chl-a and Zsd. The linear and nonlinear trends of Chl-a and Zsd suggest the deterioration of water quality in the Bohai Sea was not continued over the past two decades. This study presents the first simultaneous investigation of Chl-a and Zsd using the 23 years of cloud-free reconstructions in the Bohai Sea.

Remote sensing of transparency in the China seas from the ESA-OC-CCI data

Water transparency, often measured by Secchi disk, is an intuitive indicator of water quality and plays an important role in aquatic environments. The European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI) datasets provide daily and monthly remote-sensing reflectance over the period 1997–2019, which can be used to estimate Secchi disk depth (Zsd) with an innovative mechanistic model developed by Lee et al. (2015). In this study, the analytically-derived Zsd was validated by in situ Zsd measurements ranging from 0.1 to 38 m. The in situ Zsd measurements were collected in the first large-scale systematic survey on the marine environment in the China's offshore waters. Results indicated a good agreement between the in situ and satellite measurements. Based on the validated OC-CCI Zsd products, the spatio-temporal patterns and long-term changes of transparency in the China seas over the period 1997–2019 were studied. Regions with significant increase and decrease in Zsd on a long-term scale were also identified. Results of this work demonstrate the importance of both the OC-CCI Zsd products and remote sensing techniques in accessing water quality from long-term observations.

Interannual variability of chlorophyll-a and impact of extreme climatic events in the South Eastern Arabian Sea

South Eastern Arabian Sea (SEAS) is an upwelling dominated region, where the distribution of chlorophyll-a (Chl-a) is mainly controlled by basin-scale to mesoscale processes associated with seasonal reversal of winds and currents. The analysis of long-term satellite-derived Chl-a data revealed interannual variability in association with extreme climatic events. Chl-a data were obtained from Ocean Colour Climate Change Initiative (OC-CCI) which provides high resolution cloud free data for the Arabian Sea during summer monsoon season (June to September). Satellite measurements of Sea Level Anomaly (SLA), Sea Surface Temperature (SST) and sea surface wind data were also compiled from various sources. The monthly data for the above environmental variables during 1998 to 2016 were analysed as representatives of the possible causative physical processes influencing the variability in Chl-a. The time series and Hovmöller analyses shows that the maximum variability occurred during summer monsoon. This study shows that the interannual variability in Chl-a was prominently influenced by the underlying processes involved in coastal upwelling. The relative variability in Chl-a and associated environmental variables with these events was remarkable during strong El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events. Cross Correlation Function (CCF) analysis points out that Chl-a content is negatively correlated with IOD to an extent of one month lag or less whereas, ENSO does not indicate such direct relations. Further, the variability associated with IOD was more conspicuous than ENSO. The study elucidates the interannual variability of Chl-a and the overall influence of associated extreme events in the SEAS.

Phytoplankton Biomass and the Hydrodynamic Regime in NEOM, Red Sea

NEOM (short for Neo-Mustaqbal) is a $500 billion coastal city megaproject, currently under construction in the northwestern part of the Red Sea, off the coast of Tabuk province in Saudi Arabia, and its success will rely on the preservation of biodiverse marine ecosystems. Monitoring the variability of ecological indicators, such as phytoplankton, in relation to regional environmental conditions, is the foundation for such a goal. We provide a detailed description of the phytoplankton seasonal cycle of surface waters surrounding NEOM using satellite-derived chlorophyll-a (Chl-a) observations, based on a regionally-tuned product of the European Space Agency’s Ocean Colour Climate Change Initiative, at 1 km resolution, from 1997 to 2018. The analysis is also supported with in situ cruise datasets and outputs of a state-of-the-art high-resolution hydrodynamic model. The open waters of NEOM follow the oligotrophic character of the Northern Red Sea (NRS), with a peak during late winter and a minimum during late summer. Coral reef-bound regions, such as Sindala and Sharma, are characterised by higher Chl-a concentrations that peak during late summer. Most of the open waters around NEOM are influenced by the general cyclonic circulation of the NRS and local circulation features, while shallow reef-bound regions are more isolated. Our analysis provides the first description of the phytoplankton seasonality and the oceanographic conditions in NEOM, which may support the development of a regional marine conservation strategy.

Using bottom trawls to monitor subsurface water clarity in marine ecosystems

Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.

Global maps of Forel–Ule index, hue angle and Secchi disk depth derived from 21 years of monthly ESA Ocean Colour Climate Change Initiative data

Abstract. We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel–Ule index, hue angle and Secchi disk depth. Source data come from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data-merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ∼4 km at the Equator and are available at PANGAEA (https://doi.org/10.1594/PANGAEA.904266; Pitarch et al., 2019a). Two smaller and easier-to-handle test datasets have been produced from the former: a global dataset at 1∘ spatial resolution and another one for the North Atlantic at 0.25∘ resolution. The computer code for the generation of the Forel–Ule index, hue angle and Secchi disk depth from a given remote-sensing reflectance is also shared at https://doi.org/10.5281/zenodo.4439646 (Pitarch et al., 2021) and can be easily set in loop mode for batch calculations.

Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods

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.