A decade of satellite ocean color observations

Shedding light on the sea: André Morel's legacy to optical oceanography

André Morel (1933-2012) was a prominent pioneer of modern optical oceanography, enabling significant advances in this field. Through his forward thinking and research over more than 40 years, he made key contributions that this field needed to grow and to reach its current status. This article first summarizes his career and then successively covers different aspects of optical oceanography where he made significant contributions, from fundamental work on optical properties of water and particles to global oceanographic applications using satellite ocean color observations. At the end, we share our views on André's legacy to our research field and scientific community.

Remote sensing the phytoplankton seasonal succession of the Red Sea

The Red Sea holds one of the most diverse marine ecosystems, primarily due to coral reefs. However, knowledge on large-scale phytoplankton dynamics is limited. Analysis of a 10-year high resolution Chlorophyll-a (Chl-a) dataset, along with remotely-sensed sea surface temperature and wind, provided a detailed description of the spatiotemporal seasonal succession of phytoplankton biomass in the Red Sea. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) data, four distinct Red Sea provinces and seasons are suggested, covering the major patterns of surface phytoplankton production. The Red Sea Chl-a depicts a distinct seasonality with maximum concentrations seen during the winter time (attributed to vertical mixing in the north and wind-induced horizontal intrusion of nutrient-rich water in the south), and minimum concentrations during the summer (associated with strong seasonal stratification). The initiation of the seasonal succession occurs in autumn and lasts until early spring. However, weekly Chl-a seasonal succession data revealed that during the month of June, consistent anti-cyclonic eddies transfer nutrients and/or Chl-a to the open waters of the central Red Sea. This phenomenon occurs during the stratified nutrient depleted season, and thus could provide an important source of nutrients to the open waters. Remotely-sensed synoptic observations highlight that Chl-a does not increase regularly from north to south as previously thought. The Northern part of the Central Red Sea province appears to be the most oligotrophic area (opposed to southern and northern domains). This is likely due to the absence of strong mixing, which is apparent at the northern end of the Red Sea, and low nutrient intrusion in comparison with the southern end. Although the Red Sea is considered an oligotrophic sea, sporadic blooms occur that reach mesotrophic levels. The water temperature and the prevailing winds control the nutrient concentrations within the euphotic zone and enable the horizontal transportation of nutrients.

Impact of computational methods and spectral models on the retrieval of optical properties via spectral optimization

Spectral optimization algorithm (SOA) is a well-accepted scheme for the retrieval of water constituents from the measurement of ocean color radiometry. It defines an error function between the input and output remote sensing reflectance spectrum, with the latter modeled with a few variables that represent the optically active properties, while the variables are solved numerically by minimizing the error function. In this paper, with data from numerical simulations and field measurements as input, we evaluate four computational methods for minimization (optimization) for their efficiency and accuracy on solutions, and illustrate impact of bio-optical models on the retrievals. The four optimization routines are the Levenberg-Marquardt (LM), the Generalized Reduced Gradient (GRG), the Downhill Simplex Method (Amoeba), and the Simulated Annealing-Downhill Simplex (i.e. SA + Amoeba, hereafter abbreviated as SAA). The Garver-Siegel-Maritorena SOA model is used as a base to test these computational methods. It is observed that 1) LM is the fastest method, but SAA has the largest number of valid retrievals; 2) the quality of final solutions are strongly influenced by the forms of spectral models (or eigen functions); and 3) dynamically-varying eigen functions are necessary to obtain smaller errors for both reflectance spectrum and retrievals. Results of this study provide helpful guidance for the selection of a computational method and spectral models if an SOA scheme is to be used to process ocean color images.

Remote chlorophyll-a estimates for inland waters based on a cluster-based classification

Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (R(rs)) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every R(rs) spectrum into one of the three water types by comparing the values from band 7 (central band: 665 nm), band 8 (central band: 681.25 nm), and band 9 (central band: 708.75 nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.

On-orbit calibration of SeaWiFS

Ocean color climate data records (CDRs) require water-leaving radiances with 5% absolute and 1% relative accuracies as input. Because of the amplification of any sensor calibration errors by the atmospheric correction, the 1% relative accuracy requirement translates into a 0.1% long-term radiometric stability requirement for top-of-the-atmosphere (TOA) radiances. The rigorous prelaunch and on-orbit calibration program developed and implemented for Sea-viewing Wide Field-of-view Sensor (SeaWiFS) by the NASA Ocean Biology Processing Group (OBPG) has led to the incorporation of significant changes into the on-orbit calibration methodology over the 13-year lifetime of the instrument. Evolving instrument performance and ongoing algorithm refinement have resulted in updates to approaches for the lunar, solar, and vicarious calibration of SeaWiFS. The uncertainties in the calibrated TOA radiances are addressed in terms of accuracy (biases in the measurements), precision (scatter in the measurements), and stability (repeatability of the measurements). The biases are 2%-3% from lunar calibration and 1%-2% from vicarious calibration. The precision is 0.16% from solar signal-to-noise ratios, 0.13% from lunar residuals, and 0.10% from vicarious gains. The long-term stability of the TOA radiances, derived from the lunar time series, is 0.13%. The stability of the vicariously calibrated TOA radiances, incorporating the uncertainties of the in situ measurements and the atmospheric correction, is 0.30%. This stability of the radiometric calibration of SeaWiFS over its 13-year on-orbit lifetime has allowed the OBPG to produce CDRs from the ocean color data set.

Characterization of turbidity in Florida's Lake Okeechobee and Caloosahatchee and St. Lucie estuaries using MODIS-Aqua measurements

This paper describes the use of ocean color remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to characterize turbidity in Lake Okeechobee and its primary drainage basins, the Caloosahatchee and St. Lucie estuaries from 2002 to 2010. Drainage modification and agricultural development in southern Florida transport sediments and nutrients from watershed agricultural areas to Lake Okeechobee. As a result of development around Lake Okeechobee and the estuaries that are connected to Lake Okeechobee, estuarine conditions have also been adversely impacted, resulting in salinity and nutrient fluctuations. The measurement of water turbidity in lacustrine and estuarine ecosystems allows researchers to understand important factors such as light limitation and the potential release of nutrients from re-suspended sediments. Based on a strong correlation between water turbidity and normalized water-leaving radiance at the near-infrared (NIR) band (nL(w)(869)), a new satellite water turbidity algorithm has been developed for Lake Okeechobee. This study has shown important applications with satellite-measured nL(w)(869) data for water quality monitoring and measurements for turbid inland lakes. MODIS-Aqua-measured water property data are derived using the shortwave infrared (SWIR)-based atmospheric correction algorithm in order to remotely obtain synoptic turbidity data in Lake Okeechobee and normalized water-leaving radiance using the red band (nL(w)(645)) in the Caloosahatchee and St. Lucie estuaries. We found varied, but distinct seasonal, spatial, and event driven turbidity trends in Lake Okeechobee and the Caloosahatchee and St. Lucie estuary regions. Wind waves and hurricanes have the largest influence on turbidity trends in Lake Okeechobee, while tides, currents, wind waves, and hurricanes influence the Caloosahatchee and St. Lucie estuarine areas.

Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework

Harmful algal blooms (HABs) are severe ecological disasters threatening aquatic systems throughout the World, which necessitate scientific efforts in detecting and monitoring them. Compared with traditional in situ point observations, satellite remote sensing is considered as a promising technique for studying HABs due to its advantages of large-scale, real-time, and long-term monitoring. The present review summarizes the suitability of current satellite data sources and different algorithms for detecting HABs. It also discusses the spatial scale issue of HABs. Based on the major problems identified from previous literature, including the unsystematic understanding of HABs, the insufficient incorporation of satellite remote sensing, and a lack of multiple oceanographic explanations of the mechanisms causing HABs, this review also attempts to provide a comprehensive understanding of the complicated mechanism of HABs impacted by multiple oceanographic factors. A potential synthesized framework can be established by combining multiple accessible satellite remote sensing approaches including visual interpretation, spectra analysis, parameters retrieval and spatial-temporal pattern analysis. This framework aims to lead to a systematic and comprehensive monitoring of HABs based on satellite remote sensing from multiple oceanographic perspectives.

Optical assessment of particle size and composition in the Santa Barbara Channel, California

The suspended particle assemblage in complex coastal waters is a mixture of living phytoplankton, other autochthonous matter, and materials of terrestrial origin. The characterization of suspended particles is important for understanding regional primary productivity and rates of carbon sequestration, the fate of anthropogenic materials released to the coastal environment, as well as its effects on bulk optical properties, which influence the passive optical remote sensing of the coastal ocean. Here, the extensive bio-optical Plumes and Blooms data set is used to characterize the surface particle assemblage in the Santa Barbara Channel, California, a highly productive, upwelling-dominated, coastal site affected by episodic sediment inputs. Available variables sensitive to characteristics of the particle assemblage include particle beam attenuation and backscattering coefficients, High Performance Liquid Chromatography (HPLC) pigment concentration observations, chlorophyll and particulate organic carbon concentration, particulate and phytoplankton absorption coefficients, and Laser In-situ Scattering and Transmissometry (LISST) 100-X particle sizer observations. Comparisons among these particle assemblage proxy variables indicate good agreement and internal consistency among the data set. Correlations among chlorophyll concentration, particulate organic carbon concentration (POC), HPLC pigments, and proxies sensitive to the entire particle assemblage such as backscattering and LISST data strongly indicate that in spite of its coastal character, variability in the particle assemblage in the Santa Barbara Channel is dominated by its marine biogenic component. Relatively high estimates of the bulk real index of refraction and its positive correlation with chlorophyll and lithogenic silica concentration tentatively indicate that there is minerogenic particle influence in the Santa Barbara Channel that tends to covary with the phytoplankton blooms. Limitations of each particle assemblage proxy and remote-sensing applications are discussed.

Atmospheric correction using near-infrared bands for satellite ocean color data processing in the turbid western Pacific region

A regional near-infrared (NIR) ocean normalized water-leaving radiance (nL(w)(λ)) model is proposed for atmospheric correction for ocean color data processing in the western Pacific region, including the Bohai Sea, Yellow Sea, and East China Sea. Our motivation for this work is to derive ocean color products in the highly turbid western Pacific region using the Geostationary Ocean Color Imager (GOCI) onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS). GOCI has eight spectral bands from 412 to 865 nm but does not have shortwave infrared (SWIR) bands that are needed for satellite ocean color remote sensing in the turbid ocean region. Based on a regional empirical relationship between the NIR nL(w)(λ) and diffuse attenuation coefficient at 490 nm (K(d)(490)), which is derived from the long-term measurements with the Moderate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, an iterative scheme with the NIR-based atmospheric correction algorithm has been developed. Results from MODIS-Aqua measurements show that ocean color products in the region derived from the new proposed NIR-corrected atmospheric correction algorithm match well with those from the SWIR atmospheric correction algorithm. Thus, the proposed new atmospheric correction method provides an alternative for ocean color data processing for GOCI (and other ocean color satellite sensors without SWIR bands) in the turbid ocean regions of the Bohai Sea, Yellow Sea, and East China Sea, although the SWIR-based atmospheric correction approach is still much preferred. The proposed atmospheric correction methodology can also be applied to other turbid coastal regions.

Climate change impacts on marine ecosystems

In marine ecosystems, rising atmospheric CO2 and climate change are associated with concurrent shifts in temperature, circulation, stratification, nutrient input, oxygen content, and ocean acidification, with potentially wide-ranging biological effects. Population-level shifts are occurring because of physiological intolerance to new environments, altered dispersal patterns, and changes in species interactions. Together with local climate-driven invasion and extinction, these processes result in altered community structure and diversity, including possible emergence of novel ecosystems. Impacts are particularly striking for the poles and the tropics, because of the sensitivity of polar ecosystems to sea-ice retreat and poleward species migrations as well as the sensitivity of coral-algal symbiosis to minor increases in temperature. Midlatitude upwelling systems, like the California Current, exhibit strong linkages between climate and species distributions, phenology, and demography. Aggregated effects may modify energy and material flows as well as biogeochemical cycles, eventually impacting the overall ecosystem functioning and services upon which people and societies depend.

Marine primary production in relation to climate variability and change

Marine photosynthetic plankton are responsible for approximately 50 petagrams (10(15)) of carbon per year of net primary production, an amount equivalent to that on land. This primary production supports essentially all life in the oceans and profoundly affects global biogeochemical cycles and climate. This review discusses the general distribution of primary production in the sea, the processes that regulate this distribution, and how marine primary production is sensitive to climate variability and change. Statistical modes of ocean variability and their characteristic interannual to multi-decadal timescales over the last century are described. Recent in situ and satellite time-series of primary production can be clearly linked to interannual ocean variability. Global marine primary production appears to have increased over the past several decades in association with multi-decadal variations. A paleoclimate record extends discussion to the centennial scale, providing contrasting insights into how marine primary production might vary in the future.

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

Phytoplankton biomass and productivity have been continuously monitored from ocean color satellites for over a decade. Yet, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more. Such variability is due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals. The empirical algorithms have built-in assumptions that follow the basic precept of biological oceanography--namely, oligotrophic regions with low phytoplankton biomass are populated with small phytoplankton, whereas more productive regions contain larger bloom-forming phytoplankton. With a changing world ocean, phytoplankton composition may shift in response to altered environmental forcing, and CDOM and mineral concentrations may become uncoupled from phytoplankton stocks, creating further uncertainty and error in the empirical approaches. Hence, caution is warranted when using empirically derived Chl to infer climate-related changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellite-derived Chl for different phytoplankton assemblages. Accurate global assessments of phytoplankton will require improved technology and modeling, enhanced field observations, and ongoing validation of our "eyes in space."

Global phytoplankton decline over the past century

In the oceans, ubiquitous microscopic phototrophs (phytoplankton) account for approximately half the production of organic matter on Earth. Analyses of satellite-derived phytoplankton concentration (available since 1979) have suggested decadal-scale fluctuations linked to climate forcing, but the length of this record is insufficient to resolve longer-term trends. Here we combine available ocean transparency measurements and in situ chlorophyll observations to estimate the time dependence of phytoplankton biomass at local, regional and global scales since 1899. We observe declines in eight out of ten ocean regions, and estimate a global rate of decline of approximately 1% of the global median per year. Our analyses further reveal interannual to decadal phytoplankton fluctuations superimposed on long-term trends. These fluctuations are strongly correlated with basin-scale climate indices, whereas long-term declining trends are related to increasing sea surface temperatures. We conclude that global phytoplankton concentration has declined over the past century; this decline will need to be considered in future studies of marine ecosystems, geochemical cycling, ocean circulation and fisheries.