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Ocean oil slicks can be attributed to natural seepages or to anthropogenic discharges. To date, the global picture of their distribution and relative natural and anthropogenic contributions remains unclear. Here, by analyzing 563,705 Sentinel-1 images from 2014–2019, we provide the first global map of oil slicks and a detailed inventory of static-and-persistent sources (natural seeps, platforms, and pipelines). About 90% of oil slicks were within 160 kilometers of shorelines, with 21 high-density slick belts coinciding well with shipping routes. Quantified by slick area, the proportion of anthropogenic discharges was an order of magnitude greater than natural seepages (94 versus 6%), in contrast to the previous estimate quantified by volume during 1990–1999 (54 versus 46%). Our findings reveal that the present-day anthropogenic contribution to marine oil pollution may have been substantially underestimated.

Using data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station between 2010–2014, hyperspectral reflectance spectra of various floating matters in global oceans and lakes are derived for the spectral range of 400–800 nm. Specifically, the entire HICO archive of 9411 scenes is first visually inspected to identify suspicious image slicks. Then, a nearest-neighbor atmospheric correction is used to derive surface reflectance of slick pixels. Finally, a spectral unmixing scheme is used to derive the reflectance spectra of floating matters. Analysis of the spectral shapes of these various floating matters (macroalgae, microalgae, organic particles, whitecaps) through the use of a spectral angle mapper (SAM) index indicates that they can mostly be distinguished from each other without the need for ancillary information. Such reflectance spectra from the consistent 90 m resolution HICO observations are expected to provide spectral endmembers to differentiate and quantify the various floating matters from existing multi-band satellite sensors and future hyperspectral satellite missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) mission; and Surface Biology and Geology (SBG) mission. All spectral data are available at https://doi.org/10.21232/74LvC3Kr (Hu, 2021b).

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

Estuarine and coastal geomorphology, biogeochemistry, water quality, and coastal food webs in river-dominated shelves of the Gulf of Mexico (GoM) are modulated by transport processes associated with river inputs, winds, waves, tides, and deep-ocean/continental shelf interactions. For instance, transport processes control the fate of river-borne sediments, which in turn affect coastal land loss. Similarly, transport of freshwater, nutrients, and carbon control the dynamics of eutrophication, hypoxia, harmful algal blooms, and coastal acidification. Further, freshwater inflow transports pesticides, herbicides, heavy metals, and oil into receiving estuaries and coastal systems. Lastly, transport processes along the continuum from the rivers and estuaries to coastal and shelf areas and adjacent open ocean (abbreviated herein as “river-estuary-shelf-ocean”) regulate the movements of organisms, including the spatial distributions of individuals and the exchange of genetic information between distinct subpopulations. The Gulf of Mexico Research Initiative (GoMRI) provided unprecedented opportunities to study transport processes along the river-estuary-shelf-ocean continuum in the GoM. The understanding of transport at multiple spatial and temporal scales in this topographically and dynamically complex marginal sea was improved, allowing for more accurate forecasting of the fate of oil and other constituents. For this review, we focus on five specific transport themes: (i) wetland, estuary, and shelf exchanges; (ii) river-estuary coupling; (iii) nearshore and inlet processes; (iv) open ocean transport processes; and (v) river-induced fronts and cross-basin transport. We then discuss the relevancy of GoMRI findings on the transport processes for ecological connectivity and oil transport and fate. We also examine the implications of new findings for informing the response to future oil spills, and the management of coastal resources and ecosystems. Lastly, we summarize the research gaps identified in the many studies and offer recommendations for continuing the momentum of the research provided by the GoMRI effort. A number of uncertainties were identified that occurred in multiple settings. These include the quantification of sediment, carbon, dissolved gasses and nutrient fluxes during storms, consistent specification of the various external forcings used in analyses, methods for smooth integration of multiscale advection mechanisms across different flow regimes, dynamic coupling of the atmosphere with sub-mesoscale and mesoscale phenomena, and methods for simulating finer-scale dynamics over long time periods. Addressing these uncertainties would allow the scientific community to be better prepared to predict the fate of hydrocarbons and their impacts to the coastal ocean, rivers, and marshes in the event of another spill in the GoM.

Various approaches have been proposed to estimate surface ocean chlorophyll $a$ concentrations (Chl, mg m−3) from spectral reflectance measured either in the field or from space, each with its own strengths and limitations. Here, we develop a machine learning approach to reduce the impact of spectral noise and improve algorithm performance at the global scale for multiple satellite sensors. Among several candidates, the support vector regression (SVR) approach was found to yield the best algorithm performance as gauged by several statistical measures against field-measured Chl. While statistically the performance of the SVR is slightly worse than the empirical color index (CI) algorithm proposed in Hu et al. (2012) for Chl $x$ approaches, but the SVR leads to much reduced image noise and much improved cross-sensor consistency between SeaWiFS and Moderate Resolution Spectroradiometer (MODIS)/Aqua and between MODIS/Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, compared with the hybrid Ocean CI (OCI) algorithm currently used by the U.S. NASA as the default algorithm for all mainstream ocean color sensors, the SVR avoids the need to merge two different algorithms for intermediate Chl (band subtraction for CI and band ratio for OC $x$ ), thus may serve as an alternative approach for global data processing.

Pelagic Sargassum is a buoyant macroalgae that forms rafts at the ocean surface and serves as a biologically rich habitat for hundreds of diverse marine species. Since 2011, massive blooms of Sarga...

Satellite ocean color missions require accurate system vicarious calibrations (SVC) to retrieve the relatively small remote-sensing reflectance ( $R_{\mathrm {rs}}$ , sr−1) from the at-sensor radiance. However, the current atmospheric correction and SVC procedures do not include calibration of the “long” near infrared band (NIRL—869 nm for MODIS), partially because earlier studies, based primarily on simulations, indicate that accuracy in the retrieved $R_{\mathrm {rs}}$ is insensitive to moderate changes in the NIRL vicarious gain ( $g$ ). However, the sensitivity of ocean color data products to $g$ (NIRL) has not been thoroughly examined. Here, we first derive 10 SVC “gain configurations” (vicarious gains for all visible and NIR bands) for MODIS/Aqua using current operational NASA protocols, each time assuming a different $g$ (869). From these, we derive a suite of ~1.4E6 unique gain configurations with $g$ (869) ranging from 0.85 to 1.2. All MODIS/A data for 25 locations within each of five ocean gyres were then processed using each of these gain configurations. Resultant time series show substantial variability in dominant $R_{\mathrm {rs}}$ (547) patterns in response to changes in $g$ (869) (and associated gain configurations). Overall, mean $R_{\mathrm {rs}}$ (547) values generally decrease with increasing $g$ (869), while the standard deviations around those means show gyre-specific minima for $0.97 (869) $g$ (869) using such time series, finding $g$ (869) = 1.025 most closely comports with expectations. This approach is broadly applicable to other ocean color sensors, and highlights the importance of rigorous cross-sensor calibration of the NIRL bands, with implications on consistency of merged-sensor data sets.

Over the last several decades, multiple environmental issues have led to dramatic changes in the water clarity of the Great Lakes. While many of the key factors are well-known and have direct anthropogenic origins, climatic variability and change can also impact water clarity at various temporal scales, but their influence is less often studied. Building upon a recent examination of the univariate relationships between synoptic-scale weather patterns and water clarity, this research utilizes nonlinear autoregressive models with exogenous input (NARX models) to explore the multivariate climate-to-water clarity relationship. Models trained on the observation period (1997–2016) are extrapolated back to 1979 to reconstruct a daily-scale historical water clarity dataset, and used in a reforecast mode to estimate real-time forecast skill. Of the 20 regions examined, models perform best in Lakes Michigan and Huron, especially in spring and summer. The NARX models perform better than a simple persistence model and a seasonal-trend model in nearly all regions, indicating that climate variability is a contributing factor to fluctuations in water clarity. Further, six of the 20 regions also show promise of useful forecasts to at least 1 week of lead-time, with three of those regions showing skill out to two months of lead time.

With a five-day revisit frequency over coastal regions and a spatial resolution of 10–60 m, the Sentinel-2 multispectral instrument (MSI) has shown its capacity to provide a reasonably accurate remote sensing reflectance ( $R_{\mathrm {rs}}$ ) data product over water when the standard “black pixel” (BP) atmospheric correction algorithm was applied to the top-of-atmospheric (TOA) reflectance data. Alternative atmospheric correction approaches, such as the POLYnomial-based algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) (POLYMER), may show advantages under nonoptimal observation conditions (e.g., in the presence of strong sun glint). Here, POLYMER is implemented to process the data collected by both MSI and the Moderate Resolution Imaging Spectroradiometer (MODIS) with the resulting $R_{\mathrm {rs}}$ evaluated with concurrent and colocated in situ $R_{\mathrm {rs}}$ data collected from the AERONET-OC platforms. The results indicate less uncertainties in the MSI $R_{\mathrm {rs}}$ than those in the MODIS $R_{\mathrm {rs}}$ , and also less uncertainties in the MSI $R_{\mathrm {rs}}$ than those reported earlier. This is possibly attributed to the spatial heterogeneity of coastal waters where MODIS coarse-resolution data may suffer, and to the high-quality AERONET-OC data. In addition, for the evaluation data set, MSI $R_{\mathrm {rs}}$ does not appear to suffer from adjacency effects from the AERONET-OC platform and clouds, leading to more coverage than MODIS in nearshore waters. However, MSI $R_{\mathrm {rs}}$ is noisy in relatively clear waters, possibly due to the noisy TOA reflectance in the atmospheric correction bands over clear waters.

A historical water clarity index (Kd index or KDI) was developed through the use of satellite-derived and validated diffuse light attenuation (Kd; m−1) for each of the Great Lakes (and subbasins) on a daily level from 1998 to 2015. A statistical regionalization was performed with monthly level KDI using k-means clustering to subdivide the Great Lakes into regions with similar temporal variability in water clarity. The KDI was then used to assess the relationship between water clarity and atmospheric circulation patterns and stream discharge. An artificial neural-network-based self-organized map data reduction technique was used to classify atmospheric patterns using four atmospheric variables: mean sea level pressure, 500-hPa geopotential heights, zonal and meridional components of the wind at 10 m, and 850-hPa temperature. Stream discharge was found to have the strongest relationship with KDI, suggesting that sediments and dissolved matter from land runoffs are the key factors linking the atmosphere to water clarity in the Great Lakes. Although generally lower in magnitude than stream discharge, atmospheric circulation patterns associated with increased precipitation tended to have stronger positive correlations with KDI. With no long-range forecasts of stream discharge, the strong relationship between atmospheric circulation patterns and stream discharge may provide an avenue to more accurately model water clarity on a subseasonal-to-seasonal time scale.

Nearly all calibration/validation activities for the satellite ocean color missions have focused on data quality to produce data products of the highest quality (i.e., science quality) for climate-related research. Little attention, however, has been paid to data quantity, particularly on how data quality control during data processing impacts downstream data quality and data quantity. In this letter, we attempt to fill this knowledge gap using measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). For this sensor, the same level-1B data are processed independently using different quality control methods by NASA and NOAA, respectively, allowing for an in-depth evaluation of the interplay between data quantity and quality. The results indicate that the methods to identify stray light and sun glint are the two primary quality control procedures affecting data quantity, where the criteria for flagging pixels “contaminated” by stray light and sun glint may be relaxed in the NASA ocean color data processing to increase data quantity without compromising data quality.

Harmful algal blooms of the toxic dinoflagellate Karenia brevis occur almost annually on the West Florida Shelf (WFS) of the eastern Gulf of Mexico. To date, however, comprehensive assessments of K. brevis bloom spatial extent and temporal occurrence are lacking due to limitations in the two primary bloom monitoring techniques: microscopy evaluation of field-collected water samples and satellite remote sensing of ocean color. This is despite community efforts in expanding sampling coverage statewide and developing remote sensing algorithms to interpret color changes of surface waters. In this work, an approach is developed to combine the strengths of both techniques to estimate mean bloom occurrence frequency and bloom intensity as well as bloom extent at weekly, bi-weekly, monthly, and annual intervals between 2003 and 2019. Here, due to technical constraints on ocean color remote sensing, a bloom is defined as waters with K. brevis concentrations greater than 1.5 × 10

Uncertainties in the retrieval of the remote sensing reflectance, Rrs, from Ocean Color (OC) satellite sensors have a strong impact on the performance of algorithms for the estimation of chlorophyll-a, mineral concentrations, and inherent optical properties (IOPs). The uncertainties are highest in the blue bands. The total radiance measured at the top of the atmosphere captures the instantaneous state of the atmosphere-ocean system: the in-water conditions, sky and Sun glint reflected from the wind-roughened ocean surface, as well as light scattered from molecules and aerosols in the atmosphere. Each of these components has associated uncertainties, and when combined with the additional uncertainties from the instrument noise and the atmospheric correction process, they contribute to the total uncertainty budget for the retrieved Rrs. We analyzed the contribution of each component uncertainties to the total Rrs uncertainties in SNPP-VIIRS level 2 products, taking advantage of the spectral differences between the components. We examined multiple scenes in the open ocean and coastal waters at spatial resolutions ranging from 2250 to 5250 m by comparing the retrieved Rrs to in situ measurements made at several AERONET-OC sites and at the MOBY site. It was shown that uncertainties associated with the molecular (Rayleigh) scattering play the most significant role, while the contributions of other components are usually smaller. Uncertainties in Rayleigh scattering are primarily attributed to the variability of Rayleigh optical thickness (ROT) with a standard deviation of approximately 1.5% of ROT, which can largely explain the frequency of negative Rrs retrievals as observed using the current standard atmospheric correction process employed by NASA. Variability of the sky light reflected from the ocean surface in some conditions also contributed to uncertainties in the blue; water variability proportional to Rrs had a very pronounced peak in the green at coastal sites.

Ocean eddies along the Loop Current (LC)/Florida Current (FC) front have been studied for decades, yet studies of the entire evolution of individual eddies are rare. Here, satellite altimetry and ocean color observations, Argo profiling float records and shipborne acoustic Doppler current profiler (ADCP) measurements, together with high-resolution simulations from the global Hybrid Coordinate Ocean Model (HYCOM) are used to investigate the physical and biochemical properties, 3-dimensional (3-D) structure, and evolution of a long-lasting cyclonic eddy (CE) in the Straits of Florida (SoF) along the LC/FC front during April–August 2017. An Angular Momentum Eddy Detection Algorithm (AMEDA) is used to detect and track the CE during its evolution process. The long-lasting CE is found to form along the eastern edge of the LC on April 9th, and remained quasi-stationary for about 3 months (April 23 to July 15) off the Dry Tortugas (DT) until becoming much smaller due to its interaction with the FC and topography. This frontal eddy is named a Tortugas Eddy (TE) and is characterized with higher Chlorophyll (Chl) and lower temperature than surrounding waters, with a mean diameter of ∼100 km and a penetrating depth of ∼800 m. The mechanisms that contributed to the growth and evolution of this long-lasting TE are also explored, which reveal the significant role of oceanic internal instability.

Legacy mining facilities pose significant risks to aquatic resources. From March 30th to April 9th, 2021, 814 million liters of phosphate mining wastewater and marine dredge water from the Piney Point facility were released into lower Tampa Bay (Florida, USA). This resulted in an estimated addition of 186 metric tons of total nitrogen, exceeding typical annual external nitrogen load estimates to lower Tampa Bay in a matter of days. An initial phytoplankton bloom (non-harmful diatoms) was first observed in April. Filamentous cyanobacteria blooms (Dapis spp.) peaked in June, followed by a bloom of the red tide organism Karenia brevis. Reported fish kills tracked K. brevis concentrations, prompting cleanup of over 1600 metric tons of dead fish. Seagrasses had minimal changes over the study period. By comparing these results to baseline environmental monitoring data, we demonstrate adverse water quality changes in response to abnormally high and rapidly delivered nitrogen loads.

From extensive evaluations, it is found that, of all satellite data products of sea surface temperature (SST), MODIS SST is the most appropriate in assessing long-term trends of water temperature in the South Florida estuaries. Long-term SST data show significant warming trends in these estuaries during both daytime (0.55 °C/decade) and nighttime (0.42 °C/decade) between 2000 and 2021. The warming rates are faster during winter (0.70 °C/decade and 0.67 °C/decade for daytime and nighttime, respectively) than during summer (0.48 °C/decade and 0.28 °C/decade for daytime and nighttime, respectively). Overall, the South Florida estuaries experienced rapid warming over the past two decades, 1.7 and 1.3 times faster than the Gulf of Mexico (0.33 °C/decade and 0.32 °C/decade for daytime and nighttime), and 6.9 and 4.2 times faster than the global oceans (0.08 °C/decade and 0.10 °C/decade for daytime and nighttime).

The atmospheric correction approach currently being used operationally by NASA [termed as NASA standard atmospheric correction (NSAC) approach] to process ocean color data relies on traditional “black pixel” approach, with additional modifications to account for nonnegligible water-leaving radiance in the near-infrared (NIR) bands. The NSAC approach underestimates remote-sensing reflectance ( $R_{\mathrm {rs}}$ , sr−1) in blue wavelengths in the presence of absorbing aerosols. Addressing this issue requires realistic absorbing-aerosol model and knowledge of the vertical distribution of aerosols, which are currently difficult to achieve. An alternative atmospheric correction approach has been evaluated in this paper for Moderate Resolution Imaging Spectroradiometer (MODIS) data. The approach is based on a previously developed spectra-matching optimization [POLYnomial-based approach established for the atmospheric correction of MERIS data (POLYMER)], where polynomial functions are used to express atmospheric contribution to the measured radiance and where a bio-optical model is used to estimate the water contribution. Evaluation against in situ data measured over the regions frequently affected by absorbing aerosols indicates that, compared with the NSAC approach, the POLYMER approach improves the $R_{\mathrm {rs}}$ retrievals in blue wavelengths while having a slightly worse performance in other wavelengths. Evaluation using NSAC-retrieved $R_{\mathrm {rs}}$ in adjacent days free of absorbing aerosols suggests that the POLYMER approach could improve the spectral shape and increase valid spatial coverage. When applied to time-series MODIS data, the POLYMER approach could generate more temporary coherent daily and monthly $R_{\mathrm {rs}}$ patterns than the NSAC approach. These results suggest that the POLYMER approach could be an alternative approach to partly correct for absorbing aerosols in the absence of explicit information on the aerosol type and the vertical distribution.

The biggest bloom Floating mats of Sargassum seaweed in the center of the North Atlantic were first reported by Christopher Columbus in the 15th century. These mats, although abundant, have until recently been limited and discontinuous. However, Wang et al. report that, since 2011, the mats have increased in density and aerial extent to generate a 8850-kilometer-long belt that extends from West Africa to the Caribbean Sea and Gulf of Mexico (see the Perspective by Gower and King). This represents the world's largest macroalgal bloom. Such recurrent blooms may become the new normal. Science , this issue p. 83 ; see also p. 27

Surface seawater partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. Yet, the remote estimation of pCO2 in coastal waters (under influences of multiple processes) has been difficult due to complex relationships between environmental variables and surface pCO2. To date there is no unified model to remotely estimate surface pCO2 in oceanic regions that are dominated by different oceanic processes. In our study area, the Gulf of Mexico (GOM), this challenge is addressed through the evaluation of different approaches, including multi-linear regression (MLR), multi-nonlinear regression (MNR), principle component regression (PCR), decision tree, supporting vector machines (SVMs), multilayer perceptron neural network (MPNN), and random forest based regression ensemble (RFRE). After modeling, validation, and extensive tests using independent cruise datasets, the RFRE model proved to be the best approach. The RFRE model was trained using data comprised of extensive pCO2 datasets (collected over 16 years by many groups) and MODIS (Moderate Resolution Imaging Spectroradiometer) estimated sea surface temperature (SST), sea surface salinity (SSS), surface chlorophyll concentration (Chl), and diffuse attenuation of downwelling irradiance (Kd). This RFRE-based pCO2 model allows for the estimation of surface pCO2 from satellites with a spatial resolution of ~1 km. It showed an overall performance of a root mean square difference (RMSD) of 9.1 μatm, with a coefficient of determination (R2) of 0.95, a mean bias (MB) of −0.03 μatm, a mean ratio (MR) of 1.00, an unbiased percentage difference (UPD) of 0.07%, and a mean ratio difference (MRD) of 0.12% for pCO2 ranging between 145 and 550 μatm. The model, with its original parameterization, has been tested with independent datasets collected over the entire GOM, with satisfactory performance in each case (RMSD of ≤~10 μatm for open GOM waters and RMSD of ≤~25 μatm for coastal and river-dominated waters). The sensitivity of the RFRE-based pCO2 model to uncertainties of each input environmental variable was also thoroughly examined. The results showed that all induced uncertainties were close to, or within, the uncertainty of the model itself with higher sensitivity to uncertainties in SST and SSS than to uncertainties in Chl and Kd. The extensive validation, evaluation, and sensitivity analysis indicate the robustness of the RFRE model in estimating surface pCO2 for the range of 145–550 μatm in most GOM waters. The RFRE model approach was applied to the Gulf of Maine (a contrasting oceanic region to GOM), with local model training. The results showed significant improvement over other models suggesting that the RFRE may serve as a robust approach for other regions once sufficient field-measured pCO2 data are available for model training.

Time series of in situ surface seawater partial pressure of CO2 (pCO2) data collected between 2005 and 2017, together with other environmental variables from field or satellite measurements, along the coasts of the United States of America and its territories at different latitudes, are analyzed to separate the temperature effect from the remaining non-temperature effects (i.e., biological and other physical effects) on driving surface pCO2. Similar to the findings in the open ocean, on seasonal time scales, the temperature effect (pCO2_T) tends to override the non-temperature effect (pCO2_nonT) in modulating surface pCO2 in tropical and subtropical oceanic waters. However, the balance between pCO2_T and pCO2_nonT tends to shift towards pCO2_nonT in temperate zone waters, with a few exceptions in some specific oceanic environments. On interannual time scales, both atmospheric pCO2 and surface pCO2 show significant increasing trends over short time scales (i.e.

Optical remote sensing is one of the most commonly used techniques to detect oil in the surface ocean. This is because oil has optical properties that are different from water to modulate oil–water spatial and spectral contrasts. However, understanding these contrasts is challenging because of variable results from laboratory and field experiments as well as from different observing conditions and spatial/spectral resolutions of remote sensing imagery. Here, through reviewing published oil–water spectral contrasts and analyzing remotely sensed spectra collected by several satellite and airborne sensors (MERIS, MODIS, MISR, Landsat, and AVIRIS) from the Deepwater Horizon oil spill, we provide the interpretation of the spatial/spectral contrasts of various oil slicks and discuss the challenges in such interpretations. In addition to oil thickness, several other factors also affect oil–water spatial/spectral contrasts, including sun glint strength, oil emulsification state, optical properties of oil covered water, and spatial/spectral resolutions of remote sensing imagery. In the absence of high spatial- and spectral-resolution imagery, a multistep scheme may be used to classify oil type (emulsion and non-emulsion) and to estimate relative oil thickness for each type based on the known optical properties of oil, yet such a scheme requires further research to improve and validate.

A recently developed algorithm to estimate surface ocean chlorophyll a concentrations (Chl in milligrams per cubic meter), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl maps. The algorithm is a hybrid between a band‐difference color index algorithm for low‐Chl waters and the traditional band‐ratio algorithms (OCx) for higher‐Chl waters. In this study, the OCI algorithm is revisited for its algorithm coefficients and for its algorithm transition between color index and OCx using a merged data set of high‐performance liquid chromatography and fluorometric Chl. Results suggest that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for Chl less than 0.05 milligrams per cubic meter, but smoother algorithm transition for Chl between 0.25 and 0.40 milligrams per cubic meter. Evaluation using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and cross‐sensor consistency between SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MODISA (Moderate Resolution Imaging Spectroradiometer on Aqua), and VIIRS (Visible Infrared Imaging Radiometer Suite) over the OCx algorithms for oligotrophic oceans. Mean cross‐sensor difference in monthly Chl data products over global oligotrophic oceans reduced from approximately 10 percent for OCx to 1-2 percent for OCI2. More importantly, data statistics suggest that the current straylight masking scheme used to generate global Chl maps can be relaxed from 7 by 5 to 3 by 3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (R (sub rs) by lambda (sensor wavelength), per steradian (sr (sup −1)) for not just oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average relative increase of 39 percent in data quantity for global oceans, thus making it possible to reduce data product uncertainties and fill data gaps.

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.

Since the outbreak of a large-scale Ulva prolifera bloom in the Yellow Sea during the Qingdao Olympic Sailing Competition in summer 2008, Ulva blooms have been a marine hazard every summer. Accurate and timely information on Ulva areal coverage and biomass is of critical importance for governmental responses, decision making, and field studies. Previous studies have shown that satellite remote sensing is the most effective method for this purpose, yet Ulva areal coverage has been estimated in different ways with significantly different results. The objective of this paper is to determine the lower and upper bounds (T0 and T1) of algae-containing pixels in Floating Algae Index images with an objective method that accurately estimates the Ulva areal coverage in individual images, and then converts coverage to biomass using a previously established conversion equation. First, a seawater background image, FAIsw, is constructed to determine T0, which varies for different algae patches. Then, T1 is determined from water tank and in situ measurements as well as radiative transfer simulations to account for different sensor configurations, solar/viewing geometry, and atmospheric conditions. Such determined T1 for MODIS 250-m resolution data is validated using concurrent and collocated 2-m resolution WorldView-2 data. Finally, Ulva areal coverage derived from MODIS data using this method are compared with those from the high-resolution data (OLI/Landsat, WFV/GaoFen-1), with a mean relative difference of 9.6%. Furthermore, an analysis of 17 same-day MODIS/Terra and MODIS/Aqua image pairs shows that large viewing angles, atmospheric turbidity, and sunglint can lead to an underestimation of Ulva coverage of up to 45% under extreme conditions.