Ocean Color Data Research Articles

Modulation of chlorophyll concentration by downwelling Rossby waves during the winter monsoon in the southeastern Arabian Sea

Signatures of Rossby waves that are evident in satellite ocean colour data in the southeastern Arabian Sea (SEAS) are more prominent during the winter than the summer monsoon. During the winter, the sea level is high, and the conditions are less conducive for the injection of nutrients through the eddy-pumping mechanism. In this study, we use satellite observations and a physical-ecosystem model to examine the role of horizontal advection associated with Rossby waves in enhancing chlorophyll concentration in the SEAS. Zonal advection dominates over meridional advection, and its effect is more prominent in the south of Indian subcontinent and west of the Maldives. Except along the coast and around the islands, the vertical processes are weak and do not lead to a substantial increase in chlorophyll. Model simulations show that the currents associated with the Rossby wave also transport nutrients and other physicochemical properties of water along the path of the wave. The advected water decreases the concentration of Arabian Sea High Salinity water mass and replenishes the oxygen in the oxygen minimum zone in the SEAS.

Surface desalinated layer distribution in the Kara Sea determined by shipboard and satellite data

• Strict criterion to determine the surface desalinated layer border is formulated. • Boundaries positions of the surface desalinated layer in 2013–2018 are given. • Interseasonal and interannual variability of border positions is determined by wind regime. This study reports the propagation dynamics of the Kara Sea surface desalinated layer (SDL) during the summer and autumn seasons. We analysed shipboard measurements data collected in 2013–2018 and MODIS ocean colour data that correlated with the shipboard ones. We formulated a comparatively strict criterion to determine the SDL border based on satellite data. For that, we analyzed the shipboard flow-through measuring system data obtained while crossing the surface desalinated layer border. Further, we used a regional algorithm to process the satellite data and estimate the coloured dissolved organic matter absorption coefficient for the Kara Sea. The results demonstrate a significant effect of the wind regime on the interseasonal and interannual variability of the transformation of the SDL boundaries. The positions of the surface desalinated layer boundaries at different times during 2013–2018 are given. The obtained results are important for calculating the heat balance and analyzing the Kara Sea bio-productivity.

Water Quality Properties Derived from VIIRS Measurements in the Great Lakes

Refined empirical algorithms for chlorophyll-a (Chl-a) concentration, using the maximum ratio of normalized water-leaving radiance nLw(λ) at the blue and green bands, and Secchi depth (SD) from nLw(λ) at 551 nm, nLw(551), are proposed for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in the Great Lakes. We demonstrated that water quality properties and phytoplankton production can be successfully monitored and assessed using the new regional Chl-a and SD algorithms, with reasonably accurate estimates of Chl-a and SD from the VIIRS-SNPP ocean color data in the Great Lakes. VIIRS-derived Chl-a and SD products using the proposed algorithms provide the temporal and spatial variabilities in the Great Lakes. Overall, Chl-a concentrations are generally low in lakes Michigan and Huron, while Chl-a data are highest in Lake Erie. The seasonal pattern shows that overall low Chl-a concentrations appear in winter and high values in June to September in the lakes. The distribution of SD in the Great Lakes is spatially and temporally different from that of Chl-a. The SD data are generally lower in summer and higher in winter in most of the Great Lakes. However, the highest SD in Lake Erie appears in summer, and lower values in winter. Significantly high values in Chl-a, and lower values in SD, in the nearshore regions, such as Thunder Bay, Saginaw Bay, and Whitefish Bay, can be related to the very shallow bathymetry and freshwater inputs from the land. The time series of VIIRS-derived Chl-a and SD data provide strong interannual variability in most of the Great Lakes.

Influence of the Suspended Particulate Matter on the Satellite Radiance in the Sunglint Observation Geometry in Coastal Waters

The analysis of satellite ocean color data that are acquired over coastal waters is highly relevant to gain understanding of the functioning of these complex ecosystems. In particular, the estimation of the suspended particulate matter (SPM) concentrations is of great interest for monitoring the coastal dynamics. However, a high number of pixels of satellite images could be affected by the surface-reflected solar radiation, so-called the sunglint. These pixels are either removed from the data processing, which results in a loss of information about the ocean optical properties, or they are subject to the application of glint correction techniques that may contribute to increase the uncertainties in the SPM retrieval. The objective of this study is to demonstrate the high potential of exploiting satellite observations acquired in the sunglint viewing geometry for determining the water leaving radiance for SPM dominated coastal waters. For that purpose, the contribution of the water leaving radiance Lw to the satellite signal LTOA is quantified for the sunglint observation geometry using forward radiative transfer modelling. Some input parameters of the model were defined using in-situ bio-optical measurements performed in various coastal waters to make the simulations consistent with real-world observations. The results showed that the sunglint radiance is not sufficiently strong to mask the influence of the oceanic radiance at the satellite level, which oceanic radiance remains significant (e.g., 40% at 560 nm for a SPM concentration value of 9 g m−3). The influence of the sunglint radiance is even weaker for highly turbid waters and/or for strong wind conditions. In addition, the maximum radiance simulated in the sunglint region for highly turbid waters remains lower than the saturation radiances specified for the current ocean color sensors. The retrieval of Lw and SPM should thus be feasible from radiances measured in the sunglint pattern by satellite sensors, thus increasing the number of exploitable pixels within a satellite image. The results obtained here could be used as a basis for the development of inverse ocean color algorithms that would interestingly use the radiance measured in sunglint observation geometry as it has been done for other topics than the field of ocean color research.

Satellite-measured water properties in high altitude Lake Tahoe

It has been difficult in satellite remote sensing to derive accurate water optical, biological, and biogeochemical products over high-altitude inland waters due to issues in satellite data processing (i.e., atmospheric correction). In this study, we demonstrate that accurate normalized water-leaving radiance spectra nLw(λ) can be derived for a high-altitude lake, Lake Tahoe, using improved Rayleigh radiance computations (Wang, M., Opt. Express, 24, 12414–12429, 2016) which accurately account for water surface altitude effects in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2018 are used to evaluate and validate satellite-derived nLw(λ) spectra, and to quantitatively characterize water properties in the lake. Results show that VIIRS-derived nLw(λ) spectra are quite comparable with those from the in situ measurements. Subsequent retrievals of water biological and biogeochemical products show that chlorophyll-a (Chl-a) concentration and Secchi depth (SD) are reasonably well-estimated, and captured normal seasonal variations in the lake, e.g., the annual highest Chl-a and SD normally occur in the winter while the lowest occur in the summer, which is consistent with in situ measurements. Interannual variability of these water quality parameters is also observed. In particular, Lake Tahoe experienced a significant environmental anomaly associated with an extreme weather condition event in 2017, showing considerably decreased nLw(λ) at the spectral bands of 410, 443, and 486 nm, and significantly reduced SD values in the entire lake. The low SD measurements from VIIRS are consistent with in situ observations. Following the event in the 2017–2018 winter, Lake Tahoe recovered and returned to its typical conditions in spring 2018.

Seasonal Reversal of the Near‐Surface Chlorophyll Response to the Presence of Mesoscale Eddies in the South Pacific Subtropical Countercurrent

AbstractMesoscale eddies have been shown to have significant effects on biogeochemical cycles, as observed in local levels of near‐surface chlorophyll. There are many regional differences in the expected response of near‐surface chlorophyll to the presence of eddies, caused by differences in the driving mechanisms. However, in many high eddy activity areas, previous studies found low correlation and an inconsistent chlorophyll anomaly response to the presence of eddies. One such area is in the South Pacific Subtropical Countercurrent. Using GlobColour ocean color data and Aviso altimetry data, an investigation of the area found that a seasonal reversal occurs in the character of the chlorophyll anomaly within eddies (reversal from positive to negative and vice versa). The cause of this reversal is inferred to be a seasonally changing limiting factor within the region. Argo float profiles colocated inside and outside of eddies are used to show the coincidence of chlorophyll anomalies with seasonally changing mixed layer depths and the ability of the eddies to access deep nutrient pools. Observations of other mechanisms, such as eddy stirring or eddy‐Ekman pumping, are found to be seasonally less important than the mixed layer depth change‐induced nutrient flux . Additionally, metrics are developed to globally identify oceanic regions in which such seasonal reversals in chlorophyll anomalies could occur.

Stability of the Malvinas Current.

Deterministic and probabilistic tools from nonlinear dynamics are used to assess enduring near-surface Lagrangian aspects of the Malvinas Current. The deterministic tools are applied to a multiyear record of velocities derived from satellite altimetry data, revealing a resilient cross-stream transport barrier. This is composed of shearless-parabolic Lagrangian coherent structures (LCSs), which, extracted over sliding time windows along the multiyear altimetry-derived velocity record, lie in near-coincidental position. The probabilistic tools are applied on a large collection of historical satellite-tracked drifter trajectories, revealing weakly communicating flow regions as basins of attraction for long-time asymptotic almost-invariant sets on either side of the altimetry-derived barrier. Shearless-parabolic LCSs are detected for the first time from altimetry data, and their significance is supported on satellite-derived ocean color data, which reveal shapes that quite closely resemble the peculiar V shapes, dubbed "chevrons," that have recently confirmed the presence of similar LCSs in the atmosphere of Jupiter. Finally, using available in situ velocity and hydrographic data, sufficient and necessary conditions for nonlinear symmetric stability are found to be satisfied, suggesting a duality between Lagrangian and Eulerian stability for the Malvinas Current.

Statistical evaluation of satellite ocean color data retrievals

We develop a statistical approach to evaluate the performance of the ocean color data processing system for satellite-derived ocean color data products based on temporal stability of retrievals. We use the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system to obtain the normalized water-leaving reflectance ρwN(λ) spectra from the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements. The deviations of ρwN(λ) spectra from temporally and spatially averaged values are investigated, and the statistics with respect to various retrieval parameters are collected, including the solar-sensor geometry (solar-zenith, sensor-zenith, and relative azimuth angles), and various ancillary data (surface wind speed, surface atmospheric pressure, water vapor amount, and ozone concentration). The performance of MSL12 is also evaluated with respect to other intermediate retrieval parameters. The study shows that MSL12 produces statistically consistent VIIRS ocean color retrievals in the global open ocean, with respect to retrieval geometry parameters, as well as the ancillary inputs.

Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm

Particulate optical backscattering (bbp) is a crucial parameter for the study of ocean biology and oceanic carbon estimations. In this work, bbp retrieval, by the quasi-analytical algorithm (QAA), is assessed using a large in situ database of matched bbp and remote-sensing reflectance (Rrs). The QAA is also applied to satellite Rrs (ESA OC-CCI project) as well, after their validation against in situ Rrs. Additionally, the effect of Raman Scattering on QAA retrievals is studied. Results show negligible biases above random noise when QAA-derived bbp is compared to in situ bbp. In addition, Rrs from the CCI archive shows good agreement with in situ data. The QAA’s functional form of spectral backscattering slope, as derived from in situ radiometry, is validated. Finally, we show the importance of correcting for Raman Scattering over clear waters prior to semi-analytical retrieval. Overall, this work demonstrates the high efficiency of QAA in the bbp detection in case of both in situ and ocean color data, but it also highlights the necessity to increase the number of observations that are severely under-sampled in respect to others environmental parameters.

Atmospheric Correction of Satellite Ocean Color Remote Sensing in the Presence of High Aerosol Loads

The coverage of valid pixels of remote-sensing reflectance (Rrs) from ocean color imagery is relatively low due to the presence of clouds. In fact, it is also related to the presence of high aerosol optical depth (AOD) and other factors. In order to increase the valid coverage of satellite-retrieved products, a layer removal scheme for atmospheric correction (LRSAC) has been developed to process the ocean color data. The LRSAC used a five-layer structure including atmospheric absorption layer, Rayleigh scattering layer, aerosol scattering layer, sea surface reflection layer, and water-leaving reflectance layer to deal with the relationship of the components of the atmospheric correction. A nonlinear approach was used to solve the multiple reflections of the interface between two adjoining layers and a step-by-step procedure was used to remove effects of each layer. The LRSAC was used to process data from the sea-viewing wide field-of-view sensor (SeaWiFS) and the results were compared with standard products. The average of valid pixels of the global daily Rrs images of the standard products from 1997 to 2010 is only 11.5%, while it reaches up to 30.5% for the LRSAC. This indicates that the LRSAC recovers approximately 1.65 times of invalid pixels as compared with the standard products. Eight-day standard composite images exhibit many large areas with invalid values due to the presence of high AOD, whereas these areas are filled with valid pixels wusing the LRSAC. The ratio image of the mean valid pixel of the LRSAC to that of the standard products indicates that the number of valid pixels of the LRSAC increases with an increase of AOD. The LRSAC can increase the number of valid pixels by more than two times in about 33.8% of ocean areas with high AOD values. The accuracy of Rrs from the LRSAC was validated using the following two in situ datasets: the Marine Optical BuoY (MOBY) and the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Most matchup pairs are distributed around the 1:1 line indicating that the systematic bias of the LRSAC is relatively small. The global mean relative error (MRE) of Rrs is 7.9% and the root mean square error (RMSE) is 0.00099 sr−1 for the MOBY matchups. Similarly, the MRE and RMSE are 2.1% and 0.0025 sr−1 for the NOMAD matchups, respectively. The accuracy of LRSAC was also evaluated by different groups of matchups according to the increase of AOD values, indicating that the errors of Rrs were little affected by the presence of high AOD values. Therefore, the LRSAC can significantly improve the coverage of valid pixels of Rrs with a similar accuracy in the presence of high AOD.

An Ocean Color Algorithm Based on Power Functions to Retrieve Inherent Optical Properties From Remotely Sensed Data Off the Baja California Peninsula, Mexico

Ocean color is determined by the different components of seawater. Satellite ocean color data are commonly used to infer the inherent optical properties (IOPs) of seawater and concentration of chlorophyll $a$ (Chl- $a$ ). Many of the algorithms that relate satellite data to the components of seawater use global data to determine their parameters. Regionalizing the algorithms is recommended to improve the retrieval of IOPs from satellite data. In this article, we use the in situ and satellite data from the southern part of the California Current System (CCS) off the Baja California Peninsula during 1999 to test the Garver and Siegel IOPs’ inversion model (GSM). The strong seasonal variability observed in the data was not reproduced by this algorithm. Thus, we propose a new regional algorithm whose order and coefficient depend on seasonal variability. This algorithm includes the linear form of the GSM model but allows changes in two parameters according to the season. These two parameters are the magnitude and spectral shape of the relationship of the remote sensing reflectance with the ratio of the backscattering coefficient and the sum of backscattering and absorption coefficients. In this area, both parameters increase gradually from January to October. The new algorithm improves the retrieval of the IOPs’ ratio from an ${r}^{2}$ below 0.5 with the GSM model to an ${r}^{2}$ that ranges from 0.87 in April and October to 0.93 and 0.94 in January and August, respectively. Furthermore, Chl- $a$ data were used as a proxy to evaluate the new algorithm. Absorption data retrieved from remotely sensed data for 1999, 2008, and 2011 relate well to the in situ Chl- $a$ data.

An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments

AbstractA cross-validation algorithm is developed to perform probabilistic observing system simulation experiments (OSSEs). The use of a probability distribution of “true” states is considered rather than a single “truth” using a cross-validation algorithm in which each member of an ensemble simulation is alternatively used as the “truth” and to simulate synthetic observation data that reflect the observing system to be evaluated. The other available members are used to produce an updated ensemble by assimilating the specific data, while a probabilistic evaluation of the observation impacts is obtained using a comprehensive set of verification skill scores. To showcase this new type of OSSE studies with tractable numerical costs, a simple biogeochemical application under the Horizon 2020 AtlantOS project is presented for a single assimilation time step, in order to investigate the value of adding biogeochemical (BGC)-Argo floats to the existing satellite ocean color observations. Further experiments must be performed in time as well for a rigorous and effective evaluation of the BGC-Argo network design, though some evidence from this preliminary work suggests that assimilating chlorophyll data from a BGC-Argo array of 1000 floats can provide additional error reduction at the surface, where the use of spatial ocean color data is limited (due to cloudy conditions), as well at depths ranging from 50 to 150 m.

Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms

Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneouslyobserving the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance. By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments. We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3.

Submesoscale and Mesoscale Eddies in the Florida Straits: Observations from Satellite Ocean Color Measurements

AbstractDespite their well‐recognized importance in driving ocean physics and biology, submesoscale (diameter < Rossby radius of deformation) eddies have been extremely difficult to observe due to technical difficulties from both field and remote platforms. Here using novel satellite ocean color data products and modified algorithms, we address this challenge for the Florida Straits (22–28°N, 78–85°W). Between 2002 and 2018, while mesoscale eddies (radius >15 km) show strong seasonality with occurrence frequency decreasing from Lower Keys to Upper Keys, submesoscale eddies show little or no seasonality with high occurrence frequency restricted to 30–200‐m isobaths. The number of mesoscale eddies decreases exponentially in size, but submesoscale eddies show a normal distribution in size. These findings are significant in filling our knowledge gap in submesoscale eddies in this physically and ecologically important region as it encompasses world‐renowned coral reefs, seagrasses, and fisheries.

Filling the Gaps in Ocean Maps

A new software application merges ocean color data from instruments aboard two satellites to provide gap-free, near-real-time monitoring of the global ocean environment.

Spatial variability of fishing grounds in response to oceanic front changes detected by multiple satellite measurements in the East (Japan) sea

ABSTRACT In the East (Japan) Sea, the ocean fronts resulting from coastal upwelling and eddies affect the formation of fishing grounds by creating an environment with abundant food resources. The aim of this study was to examine the relationship between fishing activities and the locations of these ocean fronts. To do this, Day/Night Band (DNB) images were used, and ocean fronts were detected based on satellite ocean colour data. It was found that the illuminated pixels on the DNB images were related to the number of sailed out boats (coefficient of determination, R 2 = 0.55) and so may represent the number of fishing activities. Consequently, a new index was developed to assess the spatial correlation between the locations of fishing activities and ocean fronts, referred to as the ‘DNB score,’ which combined the scientific method of determining the locations of regional ocean fronts with the social method of observing the locations of fishing activities based on fishermen’s experiences. The DNB score was approximately 21% for the coastal upwelling front and 44% for the eddy front. Since most boats with lights in the East Sea are known to be fishing for Todarodes pacificus, the relationship between the DNB score and the catch of T. pacificus was also examined. This showed that the DNB score reflected the temporal catch variability and the catch per unit effort of T. pacificus, suggesting that it could be used as an index to evaluate the location of fishing grounds in association with fishing activities.

Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea

Long-term ocean color satellite missions have the ability to help monitor algal blooms. However, satellite identification of algal blooms and species in turbid coastal waters has been challenging. There is an urgent need for simple and effective methods to identify locations, areas, durations, and species present in algal blooms through satellite observation, to aid in the operational and emergency monitoring of the marine environment. In this study, based on a three-band blended model, we propose an indicator (Red tide Detection Index, RDI) that is suitable for the purpose of detecting algal blooms in turbid coastal waters using multi-source ocean color data. MERIS, MODIS, and GOCI data used for the detection of algal blooms demonstrate consistent results using the RDI indicator, and these results correlate with in situ investigations. Furthermore, based on a green-red spectral slope, we propose a method that uses MERIS data to identify dominant species of diatoms and dinoflagellates in algal blooms in the East China Sea (ECS). The 10-year time series MERIS data collected between 2003 and 2012 indicates that algal bloom occurrences are mainly distributed in the nearshore areas of the ECS, and possess a distinct climatological cycle. The 10-year time series MERIS data help discriminate diatom and dinoflagellate blooms in the ECS, and show the dissimilarity in the seasonal timing of their life cycles. We find that these two dominant species in algal blooms are usually distributed in different spatial locations in the ECS. Additionally, by defining a divergence index (DI), the limitations of spectral resolutions of satellite data used for algal bloom species differentiation are quantitatively assessed.

Spectral index-based dynamic threshold technique for detecting cloud contamination in ocean colour data

ABSTRACT Coastal and lagoon water colour products are often greatly contaminated by clouds and accompanying shadows. Adjacency effects due to the strong reflection from contiguous cloud pixels can introduce further uncertainty into the water-leaving radiance retrieval process. This study aims to propose a robust cloud-index dynamic-threshold (CIDT) algorithm based on the spectral and spatial characteristics of the clouds and evaluate its performance in comparison with results obtained from the existing algorithms using multispectral and hyperspectral images from the lagoon and coastal water zones. Detection of cloud contamination from these images is performed before applying atmospheric correction in order to prevent significant loss of valuable data and ensure the quality of higher-level products. The CIDT starts with data pre-processing (in order to avoid misclassification across the clouds and non-cloud regions) by excluding signatures other than the cloud and non-cloud (water and land) pixels. Based on the spectral and spatial variability characteristic of clouds, a new cloud index is then introduced that uses the top-of-atmosphere reflectance in conjugation with the dynamic threshold values to detect the cloud pixels. For quantitative and qualitative analyses, CIDT is tested on a number of scenes provided by Landsat 8 Operational Land Imager (OLI) and Hyperspectral Imager for the Coastal Ocean (HICO) sensors over the coastal and lagoon zones. According to the comparison of the algorithm results with the reference cloud flags, CIDT shows a significant improvement in identifying clouds of varying opacity, achieves a higher cloud detection accuracy, and reduces the number of the previously misclassified pixels by other algorithms in different coastal and lagoon water regions. The CIDT is extensively tested in mid-latitude and tropical areas and CIDT can be applied to both multispectral and hyperspectral images over the lagoon and coastal zones without the need for ancillary information.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Comparing level-2 and level-3 satellite ocean color retrieval validation methodologies.

Many ocean color data applications leverage global spatially composited level-3 (L3) satellite data because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities. L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ dataset, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond the instrument's native spatiotemporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.