Ocean Color Data Research Articles

Deriving inherent optical properties from decomposition of hyperspectral non-water absorption

Semi-analytical algorithms (SAAs) developed for multispectral ocean color sensors have benefited from a variety of approaches for retrieving the magnitude and spectral shape of inherent optical properties (IOPs). SAAs generally follow two approaches: 1) simultaneous retrieval of all IOPs, resulting in pre-defined bio-optical models and spectral dependence between IOPs and 2) retrieval of bulk IOPs (absorption and backscattering) first followed by decomposition into separate components, allowing for independent retrievals of some components. Current algorithms used to decompose hyperspectral remotely-sensed reflectance into IOPs follow the first strategy. Here, a spectral deconvolution algorithm for incorporation into the second strategy is presented that decomposes at-w(λ) from in situ measurements and estimates absorption due to phytoplankton (aph(λ)) and colored detrital material (adg(λ)) free of explicit assumptions. The algorithm described here, Derivative Analysis and Iterative Spectral Evaluation of Absorption (DAISEA), provides estimates of aph(λ) and adg(λ) over a spectral range from 350 to 700 nm. Estimated aph(λ) and adg(λ) showed an average normalized root mean square difference of <30% and <20%, respectively, from 350 to 650 nm for the majority of optically distinct environments considered. Estimated Sdg median difference was <20% for all environments considered, while distribution of Sdg uncertainty suggests that biogeochemical variability represented by Sdg can be estimated free of bias. DAISEA results suggest that hyperspectral satellite ocean color data will improve our ability to track biogeochemical processes affiliated with variability in adg(λ) and Sdg free of explicit assumptions.

A Printable Device for Measuring Clarity and Colour in Lake and Nearshore Waters.

Two expanding areas of science and technology are citizen science and three-dimensional (3D) printing. Citizen science has a proven capability to generate reliable data and contribute to unexpected scientific discovery. It can put science into the hands of the citizens, increasing understanding, promoting environmental stewardship, and leading to the production of large databases for use in environmental monitoring. 3D printing has the potential to create cheap, bespoke scientific instruments that have formerly required dedicated facilities to assemble. It can put instrument manufacturing into the hands of any citizen who has access to a 3D printer. In this paper, we present a simple hand-held device designed to measure the Secchi depth and water colour (Forel Ule scale) of lake, estuarine and nearshore regions. The device is manufactured with marine resistant materials (mostly biodegradable) using a 3D printer and basic workshop tools. It is inexpensive to manufacture, lightweight, easy to use, and accessible to a wide range of users. It builds on a long tradition in optical limnology and oceanography, but is modified for ease of operation in smaller water bodies, and from small watercraft and platforms. We provide detailed instructions on how to build the device and highlight examples of its use for scientific education, citizen science, satellite validation of ocean colour data, and low-cost monitoring of water clarity, colour and temperature.

Simulation of Sedimentation in Lake Taihu with Geostationary Satellite Ocean Color Data

In this study, the goal is to estimate the sedimentation on the bottom bed of Lake Taihu using numerical simulation combined with geostationary satellite ocean color data. A two-dimensional (2D) model that couples the dynamics of shallow water and sediment transport is presented. The shallow water equations are solved using a semi-implicit finite difference method with an Alternating Direction Implicit (ADI) method. Suspended sediment transport is simulated by solving the general convection-diffusion equation with resuspension and deposition terms using a second-order explicit central difference method in space and two-step Adams–Bashforth method in time. Moreover, the total suspended particulate matter (TSM) is retrieved by the world’s first geostationary satellite ocean color sensor Geostationary Ocean Color Imager (GOCI) using atmospheric correction algorithm for turbid waters using ultraviolet wavelengths (UV-AC) and regional empirical TSM algorithm. The 2D model and GOCI-retrieved TSM are applied to study the sediment transport and sedimentation in Lake Taihu. Validation results show rationale TSM concentration retrieved by GOCI, and the simulated TSM concentrations are consistent with GOCI observations. In addition, simulated sedimentation results reveal the dangerous locations that must be observed and desilted.

Spatial variations of small phytoplankton contributions in the Northern Bering Sea and the Southern Chukchi Sea

Phytoplankton size classes (hereafter, PSCs) were derived from satellite ocean color data using a present phytoplankton abundance-based optical algorithm in the northern Bering and southern Chukchi Seas to characterize the spatial and seasonal variations of the different PSC and investigate the contributions of small phytoplankton to the total phytoplankton biomass. The comparison results showed that the phytoplankton abundance-based method approach could reasonably classify the three PSCs (pico-, nano-, and micro phytoplankton). The satellite maps of the dominant PSCs were derived using long-term satellite ocean color data. The general spatial distribution showed that the large (micro-) phytoplankton were dominant in the coastal waters and the west side of the Bering strait, while the small size (nano- or pico-) phytoplankton were dominant in the open ocean waters. Nano- and microphytoplankton were dominant in May and October in most of the study area, while pico-phytoplankton were dominant in the summer months in the open ocean waters. The annual variation in small phytoplankton dominance had a strong positive relationship with the annual mean sea surface temperature (SST), which is consistent with the increasing dominance of small phytoplankton biomass as water temperature increases. Microphytoplankton have an apparent increasing trend in the southeastern Chukchi Sea but slightly decreasing trends in Chirikov and St. Lawrence Island Polynya (SLIP). In contrast, there were increasing trends in picophytoplankton in Chirikov and SLIP, which seems to be related to increasing annual SST. It is crucial to monitor changes in dominant groups of phytoplankton community in the Bering and Chukchi Seas as important biological hotspots responding to the recent changes in environmental conditions.

Filling the Gaps of Missing Data in the Merged VIIRS SNPP/NOAA-20 Ocean Color Product Using the DINEOF Method

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) and National Oceanic and Atmospheric Administration (NOAA)-20 has been providing a large amount of global ocean color data, which are critical for monitoring and understanding of ocean optical, biological, and ecological processes and phenomena. However, VIIRS-derived daily ocean color images on either SNPP or NOAA-20 have some limitations in ocean coverage due to its swath width, high sensor-zenith angle, high sun glint, and cloud, etc. Merging VIIRS ocean color products derived from the SNPP and NOAA-20 significantly increases the spatial coverage of daily images. The two VIIRS sensors on the SNPP and NOAA-20 have similar sensor characteristics, and global ocean color products are generated using the same Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Therefore, the merged VIIRS ocean color data from the two sensors have high data quality with consistent statistical property and accuracy globally. Merging VIIRS SNPP and NOAA-20 ocean color data almost removes the gaps of missing pixels due to high sensor-zenith angles and high sun glint contamination, and also significantly reduces the gaps due to cloud cover. However, there are still gaps of missing pixels in the merged ocean color data. In this study, the Data Interpolating Empirical Orthogonal Functions (DINEOF) are applied on the merged VIIRS SNPP/NOAA-20 global Level-3 ocean color data to completely reconstruct the missing pixels. Specifically, DINEOF is applied to 30 days of daily merged global Level-3 chlorophyll-a (Chl-a) data of 9-km spatial resolution from 19 June to 18 July 2018. To quantitatively evaluate the accuracy of the DINEOF reconstructed data, a set of valid pixels are intentionally treated as “missing pixels”, so that reconstructed data can be compared with the original data. Results show that mean ratios of the reconstructed/original are 1.012, 1.012, 1.015, and 0.997 for global ocean, oligotrophic waters, deep waters, and coastal and inland waters, respectively. The corresponding standard deviation (SD) of the ratios are 0.200, 0.164, 0.182, and 0.287, respectively. Gap-filled daily Chl-a images reveal many large-scale and meso-scale ocean features that are invisible in the original SNPP or NOAA-20 Chl-a images. It is also demonstrated that the gap-filled data based on the merged products show more details in the dynamic ocean features than those based on SNPP or NOAA-20 alone.

Improving ocean color data coverage through machine learning

Oceanic chlorophyll-a concentration (Chl, mg m−3) maps derived from satellite ocean color measurements are the only data source which provides synoptic information of phytoplankton abundance on global scale. However, after excluding data collected under non-optimal observing conditions such as strong sun glint, clouds, thick aerosols, straylight, and large viewing angles, only ~5% of MODIS ocean measurements lead to valid Chl retrievals, regardless of the fact that about 25–30% of the global ocean is cloud free. A recently developed ocean color index (CI) is effective in deriving relative ocean color patterns under most non-optimal observing conditions to improve coverage, but these patterns cannot be interpreted as Chl. In this study, we combine the advantage of the high-quality, low-coverage Chl and lower-quality, higher-coverage CI to improve spatial and temporal coverage of Chl through machine learning, specifically via a random forest based regression ensemble (RFRE) approach. For every MODIS scene, the machine learning requires CI, Rayleigh-corrected reflectance (Rrc (λ = 469, 555, 645 nm), dimensionless), and high-quality low-coverage Chl from the common pixels where they all have valid data to develop an RFRE-based model to convert CI and Rrc (λ) to Chl. The model is then applied to all valid CI pixels of the same scene to derive Chl. This process is repeated for each scene, and the model parameterization is optimized for each scene independently. The approach has been tested for the Yellow Sea and East China Sea (YSECS) where non-optimal observing conditions frequently occur. Validations using extensive field measurements and image-based statistics for 2017 show very promising results, where coverage in the new Chl maps is increased by ~3.5 times without noticeable degradation in quality as compared with the original Chl data products. The improvement in Chl coverage without compromising data quality is not only critical in revealing otherwise unknown bloom patterns, but also important in reducing uncertainties in time-series analysis. Tests of the RFRE approach for several other regions such as the East Caribbean, Arabian Sea, and Gulf of Mexico suggest its general applicability in improving Chl coverage of other regions.

Spatial and Temporal Variability of Coccolithophore Blooms in the Eastern Bering Sea

AbstractCoccolithophores are a widespread group of marine phytoplankton that produce plates of calcium carbonate that cover their cells. Large blooms of coccolithophores may significantly influence the biogeochemical properties of the ocean and atmosphere and trophic dynamics of the marine ecosystem. Because of the important implications of coccolithophore blooms, their timely monitoring and reporting is necessary for ecosystem management. To communicate with ecosystem management stakeholders, we developed an annual Coccolithophore Bloom Index (CBI) for the eastern Bering Sea shelf using satellite ocean color data. Comparisons between in situ and satellite data and the CBI (years 1997–2017) were used to examine the hypotheses regarding environmental influences on interannual bloom variability. A significant nonlinear relationship with summer stratification was found: the CBI was higher during years with either very low or very high stratification. In addition, while the blooms usually occurred over the middle shelf (50‐ to 100‐m depth), more of the bloom was located over the shallow (30–50 m) inner shelf when stratification was low. Spatial correspondence between nutrient concentrations (nitrate and ammonium) and the areal extent of the coccolithophore bloom provides tantalizing but nonconclusive evidence that nutrient availability plays a role in bloom formation and location.

The Advantages of Landsat 8-OLI-Derived Suspended Particulate Matter Maps for Monitoring the Subtidal Extension of Amazonian Coastal Mud Banks (French Guiana)

The coast of French Guiana is characterised by the northwestward migration of large mud banks alongshore and by high concentrations of suspended particulate matter (SPM) resulting from the strong influence of the Amazon River outflow. Surface OLI SPM concentration, linked to the footprint of the subtidal part of mud banks due to resuspension and migration processes, was used to develop a method to estimate the location of this footprint. A comparison of the results from this method with those obtained by locating the limit of the wave damping, which characterises muddy coasts, revealed good performance of the method based on recurring SPM values. The migration rates of the mud banks in French Guiana were calculated according to the delimitation of their subtidal parts, and showed slightly higher values (2.31 km/year) than suggested by earlier studies. In comparison with other methods, the migration rate estimated using the method proposed within the framework of this study takes into account the variability of the shape of the subtidal part for the first time. It was also shown that the mud banks existing on the coastal area of French Guiana present two different shapes. Our results clearly demonstrate the advantage of ocean colour data to describe mud banks according to their subtidal part, delimited using the assessment of SPM temporal variability.

Impact of spatiotemporal variability in phytoplankton size structure on benthic macrofaunal distribution in the Pacific Arctic

The size structure of the phytoplankton community strongly affects energy flow through food webs; hence, monitoring the spatial and temporal dynamics of phytoplankton size structure is crucial to evaluate variability in marine ecosystems. In this study, the spatiotemporal changes in phytoplankton size structure and their impact on benthic macrofaunal distribution were examined in the Pacific Arctic as part of the international Distributed Biological Observatory effort. The exponent of the chlorophyll-a (Chla) size distribution (hereafter, CSD slope), which is an index of synoptic size structure of a phytoplankton community, was estimated based on spectral shape of the phytoplankton absorption coefficient using satellite ocean color data. The CSD slope during the post-bloom period was significantly correlated with sediment Chla concentration, suggesting that the amount of organic carbon input to the seafloor was strongly related to surface phytoplankton size structure after the spring bloom. Average macrofaunal biomass in the Pacific Arctic was persistently high (>20 g C m−2) throughout 2000–2013 in the regional hotspots, and the biomass-based latitudinal centroid of macrofaunal habitat in the region shifted significantly (p < 0.05) northward. This northward shift is likely driven by a spatiotemporal change in phytoplankton size structure during the post-bloom period. Our results suggest that bottom-up forcing by the phytoplankton community affected the distribution of benthic macrofauna. We conclude that the remote estimate of phytoplankton size structure largely contributes to advancing our knowledge of ecosystem changes observed in the Pacific Arctic marine ecosystem.

Northward Expansion and Intensification of Phytoplankton Growth During the Early Ice‐Free Season in Arctic

AbstractIn the last decades, reduction of the ice cover has been documented to affect the structure and the functioning of Arctic marine ecosystems. One direct consequence is earlier phytoplankton spring blooms and larger annual primary production compared with previous decades. However, the impact of changes in the dynamics of sea ice specifically on phytoplankton spring blooms, a major contributor of the annual primary production in the Arctic Ocean, remains poorly known. Here we report on their temporal and spatial variabilities in open waters between 2003 and 2013 using satellite ocean color data. We observed a significant increase in primary productivity of phytoplankton spring blooms in different sectors of the Arctic Ocean, especially in the Barents and Kara Seas. Satellite observations also revealed a northward expansion of these blooms at a rate of 1° per decade driven by the Barents and the Kara regions.

Impact of Rossby waves on chlorophyll variability in the southeastern Arabian Sea

ABSTRACTWe use remotely sensed ocean colour data to show that Rossby waves are associated with the westward migration of chlorophyll distribution patterns in the southeastern Arabian Sea (SEAS). During the winter monsoon (November–February), the high chlorophyll concentrations observed along the west coast of India and Sri Lanka drifts westwards along with the downwelling Rossby waves. The speed of these chlorophyll signatures is in rough agreement with the expected second baroclinic mode Rossby wave speed. Observations from Ocean Surface Currents Analyses Real-time (OSCAR) suggest that it is the geostrophic currents associated with the Rossby wave propagation that influences the surface chlorophyll concentrations in the SEAS. The westward migration of chlorophyll is weaker during the summer monsoon (June–September) when the upwelling Rossby wave is observed in the sea-level data.

Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images.

Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain economic damage. Plenty of threshold-based index methods have been developed to detect surface algae pixels in various ocean color data with different manners; however, these methods cannot be used for every satellite sensor because of the spectral band configuration. Also, these traditional methods generally require other reliable indicators, and even visual inspection, in order to achieve an acceptable mapping of MAB that appears under diverse environmental conditions (cloud, aerosol, and sun glint). To overcome these drawbacks, a machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery. The method consists of two MLP models, which consider both spectral and spatial features of Rayleigh-corrected reflectance (Rrc) maps. Accuracy assessment and performance comparison showed that the proposed method has the capability to provide prediction maps of MAB with high accuracy (F1-score approaching 90% or more), and with more robustness than the traditional methods. Most importantly, the model is practically adaptable for other ocean color instruments. This allows customized models to be built and used for monitoring MAB in any regional areas. With the development of machine learning models, long-term mapping of MAB in global ocean is conducive to promoting the associated studies.

Atmospheric Correction Using the Information From the Short Blue Band

We describe our effort to use normalized water-leaving radiance at the short blue band 410 nm, nLw (410), derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership to improve the VIIRS ocean color products over coastal and inland waters, in particular, over the regions contaminated by strongly absorbing aerosols. The current standard atmospheric correction algorithm has significant issues when dealing with cases of strongly absorbing aerosols, e.g., dust, smoke, and air pollution from nearby cities. For such cases, satellite-derived normalized water-leaving radiance spectra nLw ( $\lambda $ ) are usually biased low and may be negative, particularly for the short blue bands, e.g., for the VIIRS nLw (410). In addition, for extremely turbid waters and waters dominated by colored dissolved organic matter (CDOM) that is strongly absorbing at the short blue band, slightly negative nLw (410) data are also sometimes observed. Obviously, cases with nLw (410) nLw (410) nLw (410) information. Specifically, for cases with nLw (410) nLw (410) $\ge 0$ [e.g., assuming nLw (410) = 0], thereby removing unphysical retrievals for VIIRS-derived nLw ( $ \lambda $ ) spectra. Using this technique, VIIRS-derived nLw ( $ \lambda $ ) spectra are now all ≥ 0, showing considerable improvements for VIIRS ocean color products, particularly for biological and biogeochemical products [e.g., chlorophyll-a concentration and diffuse attenuation coefficient at 490 nm $K_{d}$ (490)] which are derived using VIIRS nLw ( $\lambda $ ) spectra. Several detailed examples from VIIRS measurements over various coastal and inland waters are provided and discussed. The new technique has been implemented in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, which has been used for the routine production of VIIRS global ocean color products.

Global Distribution of Non‐algal Particles From Ocean Color Data and Implications for Phytoplankton Biomass Detection

AbstractIn the last few decades, phytoplankton biomass has been commonly studied from space. However, satellite analysis of non‐algal particles (NAPs), including heterotrophic bacteria and viruses, is relatively recent. In this work, we estimate the backscattering coefficient associated with the NAP fraction that does not covary with chlorophyll based on satellite particulate backscattering coefficient and chlorophyll (bbpNAP). bbpNAP is computed at 100‐km resolution using 19 years of monthly satellite data. We find clear differences in bbpNAP between northern and southern oceans. High bbpNAP values are found in the Arctic and Southern Oceans, the North Atlantic area influenced by the Gulf Stream current, as well as shelf regions (i.e., Patagonian shelf) affected by upwelling regimes. Low correlation between chlorophyll and backscattering prevents precise bbpNAP estimations in oligotrophic areas (e.g., subtropical gyres). These bbpNAP estimations lead to a reduction to half in satellite‐based phytoplankton biomass estimates respect to previously published results.

Gap Filling of Missing Data for VIIRS Global Ocean Color Products Using the DINEOF Method

Ocean color data are critical for the monitoring and understanding of biological and ecological processes and phenomena, and the data are also important sources of input data for physical and biogeochemical ocean models. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership has continued to provide global ocean color data since its launch in October 2011. However, there are always many missing pixels in the original VIIRS-measured ocean color images due to clouds and various other reasons. The data interpolating empirical orthogonal functions (DINEOF) is a method to reconstruct (gap filling) missing data in geophysical data sets based on the empirical orthogonal function. In this paper, the DINEOF is applied to VIIRS-derived global Level-3 binned ocean color data of 9-km spatial resolution, and the DINEOF-reconstructed ocean color data are used to fill the gaps of missing data. In particular, daily, eight-day, and monthly VIIRS global Level-3 binned ocean color data, including chlorophyll-a concentration, diffuse attenuation coefficient at 490 nm [ $K_{d}(490)$ ], and normalized water-leaving radiance spectra [ $nL_{w}(\lambda)$ ] at the five VIIRS visible bands, are tested and evaluated. To validate and evaluate the gap-filled data, a set of original valid nonmissing pixels in the VIIRS images are selected randomly and treated as missing pixels in the DINEOF process, so that the reconstructed pixels can be compared with the original data. Results show that the DINEOF method can successfully reconstruct and gap-fill meso-scale and large-scale spatial ocean features in the global VIIRS Level-3 images, as well as capture the temporal variations of these features.

Seasonal Trends in Surface pCO2 and Air‐Sea CO2 Fluxes in Apalachicola Bay, Florida, From VIIRS Ocean Color

AbstractEstuaries have been recognized as important sources of carbon dioxide (CO2) to the atmosphere; however, contributions of these systems to regional and global carbon budgets are not well constrained due to limited information on seasonal and spatial variability. In this study, we use satellite remote sensing to obtain seasonal pCO2 distribution and air‐sea CO2 fluxes in Apalachicola Bay, a national estuarine research reserve located in the northern Gulf of Mexico that receives seasonally varying dissolved organic matter‐rich waters from the Apalachicola River. A combination of time series (2005–2016) and seasonal field observations (2015–2016) of pH and biophysical variables were used to develop seasonal pH‐pCO2 relationships for obtaining surface pCO2 estimates and air‐sea CO2 fluxes from Visible and Infrared Imaging Radiometer Suite (VIIRS) ocean color data. Monthly and seasonal maps of pCO2 and air‐sea CO2 fluxes showed a general trend of higher fluxes in winter and summer corresponding to high river flow and warm water temperatures. However, CO2 fixation via photosynthesis and low water temperatures contributed to lower fluxes to the atmosphere in spring and fall, respectively. Throughout the study period, Apalachicola Bay was a net source of CO2 with large seasonal and spatial variability and a mean annual CO2 flux to the atmosphere of 3.4 ± 3.1 mol·m−2·year−1 (9.4 ± 8.5 mmol·m−2·day−1), consistent with fluxes reported for other estuaries. This study demonstrates the critical role that satellite observations can play to improve the estuarine contributions to the global carbon flux estimates.

Enduring Lagrangian coherence of a Loop Current ring assessed using independent observations

Ocean flows are routinely inferred from low-resolution satellite altimetry measurements of sea surface height assuming a geostrophic balance. Recent nonlinear dynamical systems techniques have revealed that surface currents derived from altimetry can support mesoscale eddies with material boundaries that do not filament for many months, thereby representing effective transport mechanisms. However, the long-range Lagrangian coherence assessed for mesoscale eddy boundaries detected from altimetry is constrained by the impossibility of current altimeters to resolve ageostrophic submesoscale motions. These may act to prevent Lagrangian coherence from manifesting in the rigorous form described by the nonlinear dynamical systems theories. Here we use a combination of satellite ocean color and surface drifter trajectory data, rarely available simultaneously over an extended period of time, to provide observational evidence for the enduring Lagrangian coherence of a Loop Current ring detected from altimetry. We also seek indications of this behavior in the flow produced by a data-assimilative system which demonstrated ability to reproduce observed relative dispersion statistics down into the marginally submesoscale range. However, the simulated flow, total surface and subsurface or subsampled emulating altimetry, is not found to support the long-lasting Lagrangian coherence that characterizes the observed ring. This highlights the importance of the Lagrangian metrics produced by the nonlinear dynamical systems tools employed here in assessing model performance.

Satellite ocean colour algorithm for Prochlorococcus, Synechococcus, and picoeukaryotes concentration retrieval in the South China Sea

An algorithm for retrieval of surface waters cell concentrations (in cell/ml) for three picophytoplankton components, Prochlorococcus (Pro), Synechococcus (Syn), and picoeukaryotes (Peuk) in the South China Sea (SCS), from ocean colour satellite data was developed and tested. Level 3 merged multisensor Ocean Colour Climate Change Initiative satellite data is used. Training is performed using in situ data on abundances of the three phytoplankton components. Several predictors derived from satellite reflectance data were tested. The regression form that assures the highest accuracy of the algorithm was chosen based on cross-validation (CV). According to the CV on test data subset, the algorithm performance is characterized by the r value 0.89, 0.72, and 0.73 and MAPD 38, 71 and 51% for Peuk, Pro, and Syn respectively. This is one of the few studies aimed at the Peuk, Pro, and Syn distribution research in the northern SCS using ocean colour satellite data. This is the only research providing algorithm with accuracy estimates of the Peuk, Pro, and Syn concentrations retrieval from the ocean colour data. Analysis of the developed algorithm allows us to conclude that both mechanisms (specific spectral features caused by pigments composition and spectrum features sensitive to general primary productivity, e.g. band ratios in 443–510 nm range and spectrum absolute values) are important for getting accurate information on the picophytoplankton composition.

Impact of decadal reversals of the north Ionian circulation on phytoplankton phenology

Abstract. In the north Ionian, water circulation is characterized by a decadal alternation of cyclonic and anticyclonic regime driven by the mechanism called BiOS (bimodal oscillating system). The circulation regimes affect both vertical dynamics and the nutrient distribution. The north Ionian is then a good study area to investigate how changes in circulation can affect phytoplankton dynamics in oligotrophic regions. From in situ observations, for each circulation regime the averaged distribution of isopycnals is provided, and a depth difference of about 80 m is estimated for the nitracline between the cyclonic and anticyclonic regime. Based on phytoplankton phenology metrics extracted from annual time series of satellite ocean color data for the period 1998–2012, the cyclonic and anticyclonic regimes are compared. Results show that the average chlorophyll in March, the date of bloom onset and the date of maximum chlorophyll were affected by circulation patterns in the north Ionian. In the center of the north Ionian gyre, the bloom started in December and chlorophyll was low in March when circulation was anticyclonic, whereas during the cyclonic circulation regime, a late chlorophyll peak, likely resulting from different phytoplankton dynamics, was commonly observed in March. An additional analysis shows that the winter buoyancy losses, which govern the mixed layer depth (MLD), also contribute to explaining the interannual variability in bloom onset and intensity. Two trophic regimes were then identified in the north Ionian gyre (NIG) and they could be explained with the relative position of the MLD and nitracline. The first one is characterized by an early winter bloom onset and the absence of a chlorophyll peak in March. It was observed when circulation was anticyclonic or when winter MLD was relatively shallow. Dominant regenerated production all year and an absence of significant nutrient supplies to surface waters are proposed to explain this trophic regime. Conversely, the second trophic regime is marked by a bloom onset in late winter (i.e., February) and a chlorophyll peak in March. The chlorophyll increase was interpreted as a direct response to the nutrient enrichment of surface waters. This winter–spring bloom was observed when circulation was cyclonic and when winter mixing was relatively strong.

Phytoplankton size class in the East China Sea derived from MODIS satellite data

Abstract. The distribution and variation of phytoplankton size class (PSC) are key to understanding ocean biogeochemical processes and ecosystems. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many algorithms have been developed to estimate PSC. Here based on a local dataset from the ECS, a regional model was tuned to estimate the PSC from the spectral features of normalized phytoplankton absorption (aph) using a principal component analysis approach. Before applying the refined PSC model to MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite remote sensing reflectance (Rrs) at 412 and 443 nm was critical through modeling them from Rrs between 469 and 555 nm using multiple regression analysis. Satellite-derived PSC results compared well with those derived from pigment composition, which demonstrated the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. Application of the refined PSC model to the reconstructed MODIS data from 2003 to 2016 yielded the seasonal distributions of the PSC in the ECS, suggesting that the PSC distributions were heterogeneous in both temporal and spatial scales. Micro-phytoplankton were dominant in coastal waters throughout the year, especially in the Changjiang estuary. For the middle shelf region, the seasonal shifts from the dominance of micro- and nano-phytoplankton in the winter and spring to the dominance of nano- and pico-phytoplankton in the summer and autumn were observed. Pico-phytoplankton were especially dominant in the Kuroshio region in the spring, summer, and autumn. The seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio. Additionally, human activity and riverine discharge might also influence the PSC distribution in the ECS, especially in the coastal region.