Coastal Zone Color Scanner Research Articles

Genesis and Evolution of NASA’s Satellite Ocean Color Program

We recount, based on our involvements in NASA ocean color flight projects, the chronology of technical challenges, lessons learned, and key developments over the past 40 + years of NASA satellite ocean color, beginning with the Nimbus-7/Coastal Zone Color Scanner, that have led to the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Topics include the evolution of 1) satellite ocean color and field bio-optical data access, 2) satellite sensor capabilities, i.e., CZCS to PACE’s hyperspectral Ocean Color Imager, OCI, 3) atmospheric corrections, 4) pre- and post-launch sensor characterization and calibration, 5) bio-optical algorithms, 6) in situ-derived radiometry and photosynthetic pigment data measurement quality, and 7) applications of hyperspectral satellite observations.

PACE: How One NASA Mission Aligns With the United Nations Decade of Ocean Science for Sustainable Development (OceanShot #1)

Abstract The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE; <ext-link ext-link-type="uri" xlink:href="https://pace.gsfc.nasa.gov">https://pace.gsfc.nasa.gov</ext-link>) mission, scheduled for launch in January 2024, will extend the continuous high-quality ocean color, atmospheric aerosol, and cloud data records begun by NASA in the late 1990s, building on the heritage of the Coastal Zone Color Scanner (CZCS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) (Figure 1). PACE's global hyperspectral imaging radiometer design concept will enable new discoveries in Earth's living ocean (Figure 2), such as the diversity of organisms fueling marine food webs and how aquatic ecosystems respond to environmental change. Its instrument payload (Figure 3) will also observe Earth's atmosphere to study clouds, airborne aerosol particles, and the interactions between the two. Looking at the ocean, clouds, and aerosols together will improve our knowledge of the roles each plays in our evolving planet. Other applications of PACE science data records—from identifying the frequency, extent, and duration of aquatic harmful algal blooms to improving our understanding of air quality—will result in direct economic, recreational, and societal benefits. Ultimately, by extending and expanding NASA's long record of global Earth satellite observations, the PACE mission will monitor our home planet in new and advanced ways in the coming decade.

Decadal changes in Arctic Ocean Chlorophyll a: Bridging ocean color observations from the 1980s to present time

Remotely-sensed Ocean color data offer a unique opportunity for studying variations of bio-optical properties which is especially valuable in the Arctic Ocean (AO) where in situ data are sparse. In this study, we re-processed the raw data from the Sea-viewing Wide Field-of-View (SeaWiFS, 1998–2010) and the MODerate resolution Imaging Spectroradiometer (MODIS, 2003–2016) ocean-color sensors to ensure compatibility with the first ocean color sensor, namely, the Coastal Zone Color Scanner (CZCS, 1979–1986). Based on a bio-regional approach, this study assesses the quality of this new homogeneous pan-Arctic Chl a dataset, which provides the longest (but non-continuous) ocean color time-series ever produced for the AO (37 years long between 1979 and 2016). We show that despite the temporal gaps between 1986 and 1998 due to the absence of ocean color satellite, the time series is suitable to establish a baseline of phytoplankton biomass for the early 1980s, before sea-ice loss accelerated in the AO. More importantly, it provides the opportunity to quantify decadal changes over the AO revealing for instance the continuous Chl a increase in the inflow shelves such as the Barents Sea since the CZCS era.

An Unlikely Career in Satellite Ocean Biology or “OK, now what?”

AbstractThis article is a story of my career beginning in a small rural town in Missouri and culminating at NASA Goddard Space Flight Center after nearly 37 years there. Particular focus is on those who served as mentors and colleagues, studies of ocean surface waves, the “ocean color” satellite missions I was involved in, for example, the Coastal Zone Color Scanner and the Sea‐viewing Wide‐Field‐of‐View Sensor, the background of the future Plankton, Aerosol, Cloud and ocean Ecology mission, and the insights into ocean biology gained from these missions. The path was punctuated by what I call “OK, now what?” moments when the next step was unclear. At these junctures, mentors would open doors and I would need to “retool” to adapt to the science I would be involved in.

Remote sensing of phytoplankton decline during the late 1980s and early 1990s in the South China Sea

ABSTRACT The duration of a single satellite deployment is insufficient to detect long-term trends in phytoplankton biomass. By integrating data from different platforms, we quantified the phytoplankton trends in the South China Sea (SCS) from the early-to-mid 1980s to the present. We focused on winter data because the sea surface temperature (SST) in winter has been increasing over the past three decades. We included data from three different eras: (1) the Coastal Zone Colour Scanner (CZCS) period (1979–1983), (2) the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) period (1998–2002), and (3) the Moderate Resolution Imaging Spectroradiometer (MODIS) period (2010–2014). There was a decline in phytoplankton (approximately 0.15 mg m−3) over the entire SCS from the CZCS period to the SeaWiFS period. The largest reduction (over 0.3 mg m−3) occurred in the northern and western SCS. After the SeaWiFS period, the phytoplankton increased by approximately 0.05 mg m−3. The largest increase was observed to the west of Luzon Strait, extending towards the east coast of Hainan Island. The fluctuations in phytoplankton from the early-to-mid 1980s to the present were consistent with the variation in the wind field rather than with the continued increase in SST. The wind speed decreased from the CZCS period to the SeaWiFS period and increased from the SeaWiFS period to the MODIS period. Moreover, the nutrient variations induced by the mixed-layer depth also contributed to the fluctuations in phytoplankton.

Chlorophyll Pigment Concentration and Sea Surface Temperature of the Black Sea

As an enclosed sea, the Black Sea has countless economic activities (mainly tanker traffic and fisheries) and recreational activities, with the consequence of being threatened by dramatic dangers and pollution. Optical remote sensing can provide a novel look at physical processes and then driving mechanisms. It is worthwhile to know how the temperature and other marine parameters change on seasonal and long-term scales. In the paper CZCS (Coastal Zone Color Scanner) and AVHRR (Advanced Very High Resolution Radiometer) satellite were used to study the relationship between photosynthesis and SST since phytoplankton forms the very lowest clement in the marine ecosystem. Satellite derived data could provide information on the amount of sea life present in any given area throughout the world. The information could also be useful in connection with studies of global changes m temperature and what effect they could have on the total abundance of marine life. Present work which used CD-ROM set from NASA did not found evidence of correlation between chlorophyll pigment concentration and SST of the Black Sea with 99.95% certainty, three channel algorithm or the use of fluorescence or chlorophyll absorption peak in the red suggested for future work.

Performance of operational satellite bio-optical algorithms in different water types in the southeastern Arabian Sea

Summary The in situ remote sensing reflectance (Rrs) and optically active substances (OAS) measured using hyperspectral radiometer, were used for optical classification of coastal waters in the southeastern Arabian Sea. The spectral Rrs showed three distinct water types, that were associated with the variability in OAS such as chlorophyll-a (chl-a), chromophoric dissolved organic matter (CDOM) and volume scattering function at 650 nm (β650). The water types were classified as Type-I, Type-II and Type-III respectively for the three Rrs spectra. The Type-I waters showed the peak Rrs in the blue band (470 nm), whereas in the case of Type-II and III waters the peak Rrs was at 560 and 570 nm respectively. The shifting of the peak Rrs at the longer wavelength was due to an increase in concentration of OAS. Further, we evaluated six bio-optical algorithms (OC3C, OC4O, OC4, OC4E, OC3M and OC4O2) used operationally to retrieve chl-a from Coastal Zone Colour Scanner (CZCS), Ocean Colour Temperature Scanner (OCTS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean Colour Monitor (OCM2). For chl-a concentration greater than 1.0 mg m−3, algorithms based on the reference band ratios 488/510/520 nm to 547/550/555/560/565 nm have to be considered. The assessment of algorithms showed better performance of OC3M and OC4. All the algorithms exhibited better performance in Type-I waters. However, the performance was poor in Type-II and Type-III waters which could be attributed to the significant co-variance of chl-a with CDOM.

Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes.

We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.

Martin, S.An Introduction to Ocean Remote Sensing, 2nd Edition. Cambridge University Press, ISBN 9781107019386, $85.

I remember, about 30 years ago, when J. T. O. Kirk's (1983) book, “Light and Photosynthesis in Aquatic Systems” came out. Ocean bio-optics, a synergy between optical physicists and biological oceanographers, was in its infancy. We thought then that this was “the Bible” for bio-optics. While Kirk's book may not have had the theoretical rigor of Preisendorfer's (1976) “Hydrologic Optics,” it covered all the important aspects of the interaction of light and biology in aquatic systems. The present book, I thought, might be something similar for remote sensing of the ocean, but is instead, as indicated by the title, an introduction (a second edition; I had not seen the earlier one from 2004). It can be considered a first stop before embarking on a more in-depth (sorry) treatment. That said, as a first stop, Prof. Martin's book is invaluable, and rewards the reader with clear explanations, and discussions of the limitations and advantages of satellite remote sensing. The perspective favors the technical, for example, differentiating between what is signal at the satellite sensor and what is noise. The book proceeds in orderly fashion beginning with a background and the basics. Chapter 1, a must-read for novices and veterans alike, traverses definitions, orbits and imaging, and also a brief, but useful history. Martin defines remote sensing as “the use of electromagnetic radiation to acquire information about the ocean, land, and atmosphere without being in physical contact…” According to this definition, as he states, remote sensing will also include sensors on a ship or other platform, and even the spectrophotometer app on my iPhone. He focuses, however, on remote sensing using satellite sensors, and then proceeds to discuss what that lack of physical contact entails in terms of satellite orbits and scanners, resolution issues, and data processing. He covers the definitions and the description of various kinds of satellite sensors (passive vs. active, spectrometers, radars) and how they are designed to sweep and survey the ocean's surface. His history, near the end of Chapter 1, shows the evolution of satellites from demonstration projects, for example the NIMBUS series, and from large multi-instrument satellites the size of a city bus, to constellations of smaller platforms. Constellations mean instrument failures are less catastrophic, there is greater international cooperation, and technology is easier to keep up to date. Martin then proceeds (Chapter 2) to a consideration of ocean surface phenomena, followed in succeeding chapters by treatments of electromagnetic radiation, atmospheric interferences, and the air–sea interface. From there, he introduces ocean color, IR observations, microwave imagers, scatterometers, altimeters, and radars. There is a good treatment of the optical theory underlying the measurement of ocean geophysical variables (temperature, sea level, winds, ice, and color) from space. There is also an excellent discussion of the atmospheric correction, and how it is done when there is no provision in the satellite spectrometer for IR bands (as on the Coastal Zone Color Scanner), wavelengths where the ocean will not have much (or any) reflectance. There is also a good analysis of the necessity for vicarious calibration of ocean color sensors such as for the SeaWiFS and MODIS sensors. There is an excellent chapter on how SAR (synthetic aperture radar) works. A minor error is the identification, in Chapter 6, of “chlorophytes” or “coccolithophores” as species. Chlorophytes in this case refer to a composite: model output meant to represent a variety of phytoplankton groups. Coccolithophores are a group with many different species. The book deals more with the problem of detecting geophysical variables from space, with all the interferences, orbital considerations, and surface complexities (e.g., sea foam, capillary waves), at the expense examining ocean processes. To be sure, there are some examples of the use of the data, but readers will have to look further for how the magnitude and distribution of remote sensing variables help us understand ocean circulation, the movement of sea ice, seasonality, and ocean productivity. The book concerns itself more with “nuts and bolts,” calibration and validation. For myself, I'll place Martin's book on the shelf next to reports from the International Ocean Colour-Coordinating Group, some of which consider the applications of remote sensing to oceanography. Perhaps as an introductory text, there are omissions. There is no consideration of LandSat, even though that platform has applications in coastal areas. (NASA's Earth Observatory often has captivating images from coastal environments.) There is also no consideration of remote sensing from aircraft, nor, in a look to the future, a treatment of LIDARs, such as aboard the CALIPSO satellite. But overall, this is a fine treatment of satellite remote sensing of ocean properties that will find use among students, instructors, and researchers. Reviewed by John Marra, Earth and Environmental Sciences, Aquatic Research and Environmental Assessment Center, Brooklyn College, CUNY, NewYork, NY, USA; jfm7780@brooklyn.cuny.edu

Impact of missing data on the estimation of ecological indicators from satellite ocean-colour time-series

Ocean-colour remote sensing provides high-resolution and global-coverage of chlorophyll concentration, which can be used to estimate ecological indicators and to study inter-annual and long-term trends in the state of the marine ecosystem. To date, the record of ocean-colour observations is a rich one, including data from a number of sensors spanning more than three decades. The ESA Ocean-Colour Climate Change Initiative has advanced seamless merging of ocean-colour observations from missions during the period 1990s to 2010s. However, comparison of these more recent observations with records from 1970s to 1980s remains a complex undertaking, particularly for absolute values of chlorophyll concentration, primarily due to differences in the sensors. A further impediment to the analysis of the past records is the non-uniform distribution of gaps in the observations, in both time and space dimensions, when data from two or more sensors are compared. Here, we use the CZCS gap distribution from the Coastal Zone Color Scanner (CZCS, 1978–1986) as a mask to evaluate the impact that missing data may have on the estimation of six ecological indicators, when using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data set. Specifically, we evaluate the precision and accuracy of indicators by computing the root-mean-square-error (RMSE) and the bias arising purely from missing data. We develop an original resampling method allowing comparison of indicator estimates between SeaWiFS reference time-series and SeaWiFS time-series with CZCS-like gaps. We reduce some of the sampling gaps by applying a linear interpolation procedure, and compute multi-year averages of the indicators for every one-by-one degree pixel where sufficient data are available. Indicators from SeaWiFS reference and SeaWiFS with CZCS-like gaps are compared. Lowest uncertainty arising from missing data is observed in the indicators of annual mean and median chlorophyll concentration (global mean RMSE of 8% and |bias| ≤1%), whilst higher uncertainty is recorded for the peak chlorophyll values and the duration of the phytoplankton growing period (global mean RMSE of 33 and 47% respectively and |bias| ≤20%). Timing of initiation of the increasing phase of chlorophyll concentration in the seasonal cycle and timing of peak chlorophyll are subject to a global mean RMSE of nearly two months and a bias of two weeks or less. The present quantitative evaluation of uncertainty due to missing data demonstrates that, when pooled to create a nine-year climatology at 8-day temporal resolution, the coverage of CZCS is adequate for many climate-related studies on the marine ecosystem. Phytoplankton annual mean biomass can be estimated with low error in approximately 95% of the global oceans (i.e. regions where the indicators can be estimated with RMSE values of less than 30% and bias within ±10%), and the phenological patterns can be estimated with low error in approximately 25% of the global oceans.

Biogeography of the Oceans: a Review of Development of Knowledge of Currents, Fronts and Regional Boundaries from Sailing Ships in the Sixteenth Century to Satellite Remote Sensing

The development of knowledge of global biogeography of the oceans from sixteenthcentury European voyages of exploration to present-day use of satellite remote sensing is reviewed in three parts; the pre-satellite era (1513–1977), the satellite era leading to a first global synthesis (1978–1998), and more recent studies since 1998. The Gulf Stream was first identified as a strong open-ocean feature in 1513 and by the eighteenth century, regular transatlantic voyages by sailing ships had established the general patterns of winds and circulation, enabling optimisation of passage times. Differences in water temperature, water colour and species of animals were recognised as important cues for navigation. Systematic collection of information from ships’ logs enabled Maury (The Physical Geography of the Sea Harper and Bros. New York 1855) to produce a chart of prevailing winds across the entire world’s oceans, and by the early twentieth century the global surface ocean circulation that defines the major biogeographic regions was well-known. This information was further supplemented by data from large-scale plankton surveys. The launch of the Coastal Zone Color Scanner, specifically designed to study living marine resources on board the Nimbus 7 polar orbiting satellite in 1978, marked the advent of the satellite era. Over subsequent decades, correlation of satellite-derived sea surface temperature and chlorophyll data with in situ measurements enabled Longhurst (Ecological Geography of the Sea. Academic Press, New York 1998) to divide the global ocean into 51 ecological provinces with Polar, Westerly Wind, Trade Wind and Coastal Biomes clearly recognisable from earlier subdivisions of the oceans. Satellite imagery with semi-synoptic images of large areas of the oceans greatly aided definition of boundaries between provinces. However, ocean boundaries are dynamic, varying from season to season and year to year. More recent work has focused on the study of variability of currents, fronts and eddies, which are often the focus of high biological productivity. Direct tracking of animals using satellite-based systems has helped resolve the biological function of such features and indeed animals instrumented in this way have helped the study of such features in three dimensions, including depths beyond the reach of conventional satellite remote sensing. Patterns of surface productivity detected by satellite remote sensing are reflected in deep sea life on the sea floor at abyssal depths >3,000 m. Satellite remote sensing has played a major role in overcoming the problems of large spatial scales and variability in ocean dynamics and is now an essential tool for monitoring global change.

Decadal variability of chlorophyll a in the South China Sea: a possible mechanism

Four climatologies on a monthly scale (January, April, May and November) of chlorophyll a within the South China Sea (SCS) were calculated using a Coastal Zone Color Scanner (CZCS) (1979-1983) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (1998-2002). We analyzed decadal variability of chlorophyll a by comparing the products of the two observation periods. The relationships of variability in chlorophyll a with sea surface wind speed (SSW), sea surface temperature (SST), wind stress (WS), and mixed layer depth (MLD) were determined. The results indicate that there is obvious chlorophyll a decadal variability in the SCS. The decadal chlorophyll a presents distinct seasonal variability in characteristics, which may be as a result of various different dynamic processes. The negative chlorophyll a concentration anomaly in January was associated with the warming of SST and a shallower MLD. Generally, there were higher chlorophyll a concentrations in spring during the SeaWiFS period compared with the CZCS period. However, the chlorophyll a concentration exhibits some regional differences during this season, leading to an explanation being diffi cult. The deepened MLD may have contributed to the positive chlorophyll a concentration anomalies from the northwestern Luzon Island to the northeastern region of Vietnam during April and May. The increases of chlorophyll a concentration in northwestern Borneo during May may be because the stronger SSW and higher WS produce a deeper mixed layer and convective mixing, leading to high levels of nutrient concentrations. The higher chlorophyll a off southeastern Vietnam may be associated with the advective transport of the colder water extending from the Karimata Strait to southeastern Vietnam.

Climatological, annual, and seasonal variability in chlorophyll concentration in the Gulf of Mexico, western Caribbean, and Bahamas using NASA colour maps

This study investigates climatological, seasonal, and interannual variability in chlorophyll concentration (Chl) throughout the Gulf of Mexico (GM), the western Caribbean Sea (WC), and the Bahamas (BI). For this purpose, Coastal Zone Color Scanner (CZCS) (1979–1986), Ocean Color and Temperature Scanner (OCTS) (1996–1997), and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) (1997–2008) NASA climatology, yearly, and seasonal Level-3 Standard Mapped Image series were used. Inspection of the original Chl and the obtained fuzzy unsupervised classified maps show the existence of a transition zone between the already known coastal and open waters. The extension (total number of pixels) and form of polygons representing these water masses vary both annually and seasonally, showing their greatest differences during spring and autumn in the northeastern and northwestern GM, Campeche Sound, and the Honduras coast in the Caribbean. In contrast, the BI present polygons having an almost invariant extension and form. The seasonal averaged Chl values up to 0.8 mg m−3 present a cyclic variation, showing the highest Chl during winter months and the lowest Chl during summer months, independent of the basin or the sensor under consideration. The CZCS and OCTS products must be considered with care; however, they provide results that are compatible with findings from the SeaWiFS time series. Annual and winter/autumn trends – a decrease in Chl – were identified in the GM and BI. The Caribbean reports constant Chl values during the two periods under study. Possible interpretations of these trends will come from detailed interpretation of local data.

Phenological changes of oceanic phytoplankton in the 1980s and 2000s as revealed by remotely sensed ocean‐color observations

We investigated the phenology of oceanic phytoplankton at large scales over two 5‐year time periods: 1979–1983 and 1998–2002. Two ocean‐color satellite data archives (Coastal Zone Color Scanner (CZCS) and Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS)) were used to investigate changes in seasonal patterns of concentration‐normalized chlorophyll. The geographic coverage was constrained by the CZCS data distribution. It was best for the Northern Hemisphere and also encompassed large areas of the Indian, South Pacific, and Equatorial Atlantic regions. For each 2° pixel, monthly climatologies were developed for satellite‐derived chlorophyll, and the resulting seasonal cycles were statistically grouped using cluster analysis. Five distinct groups of mean seasonal cycles were identified for each half‐decade period. Four types were common to both time periods and correspond to previously identified phytoplankton regimes: Bloom, Tropical, Subtropical North, and Subtropical South. Two other mean seasonal cycles, one in each of the two compared 5‐year periods, were related to transitional or intermediate states (Transitional Tropical and Transitional Bloom). Five mean seasonal cycles (Bloom, Tropical, Subtropical North, and Subtropical South, Transitional Bloom) were further confirmed when the whole SeaWiFS data set (1998–2010) was analyzed. For ∼35% of the pixels analyzed, characteristic seasonal cycles of the 1979–1983 years differed little from those of the 1998–2002 period. For ∼65% of the pixels, however, phytoplankton seasonality patterns changed markedly, especially in the Northern Hemisphere. Subtropical regions of the North Pacific and Atlantic experienced a widespread expansion of the Transitional Bloom regime, which appeared further enhanced in the climatology based on the full SeaWiFS record (1998–2010), and, as showed by a more detailed analysis, is associated to La Niña years. This spatial pattern of Transitional Bloom regime reflects a general smoothing of seasonality at macroscale, coming into an apparent greater temporal synchrony of the Northern Hemisphere. The Transitional Bloom regime is also the result of a higher variability, both in space and time. The observed change in phytoplankton dynamics may be related not only to biological interactions but also to large‐scale changes in the coupled atmosphere–ocean system. Some connections are indeed found with climate indices. Changes were observed among years belonging to opposite phases of ENSO, though discernible from the change among the two periods and within the SeaWiFS era (1998–2010). These linkages are considered preliminary at present and are worthy of further investigation.

Space-borne study of seasonal, multi-year, and decadal phytoplankton dynamics in the Bay of Biscay

Seasonal and inter-annual variations in phytoplankton community abundance in the Bay of Biscay are studied. Preliminarily processed by the National Aeronautics and Space Administration (NASA) to yield normalized water-leaving radiance and the top-of-the-atmosphere solar radiance, Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Coastal Zone Color Scanner (CZCS) data are further supplied to our dedicated retrieval algorithms to infer the sought for parameters. By applying the National Oceanic and Atmospheric Administration's (NOAA's) Advanced Very High Resolution Radiometer (AVHRR) data, the surface reflection coefficient in the only band in the visible spectrum is derived and employed for analysis. Decadal bridged time series of variations of diatom-dominated phytoplankton and green dinoflagellate Lepidodinium chlorophorum within the shelf zone and the coccolithophore Emiliania huxleyi in the pelagic area of the Bay are documented and analysed in terms of impacts of some biogeochemical and geophysical forcing factors. It is shown that in the shelf zone of the Bay, the diatom-dominated phytoplankton community variations are predominantly controlled by river discharge variations, by water column stratification conditions (forming in winter–early spring), and by wind action (resulting in such phenomena as up-wellings and sediment re-suspension). Satellite data indicate that while in river deltas and adjoining waters the L. chlorophorum blooming events occur annually, in the Iroise Sea and near the Bailiwick of Guernsey, they happen irregularly. It is thought that such an irregular pattern, possibly, arises from L. chlorophorum competing with other phytoplankton species for nutrients. E miliania huxleyi blooms are found to occur nearly every year in the northern part of the Bay, whereas in the central area, this phenomenon occurs very irregularly. Satellite data indicate that variations in the water chemistry (variations in the nitrogen : phosphorus ratio due to preceding blooms of diatoms), and the incident irradiance level (degree of cloudiness), are important factors controlling the occurrence of E. huxleyi blooming in the central part of the Bay. Covering a 30 year period, the bridged data from CZCS, AVHRR, SeaWiFS, and MODIS imply that climate change might be responsible for the observed increase in E. huxleyi blooming events in the Bay since 1979.

Does horizontal mixing explain phytoplankton dynamics?

The modern era of biological oceanography arguably began in 1978 with the successful launch of the Coastal Zone Color Scanner on the Nimbus 7 satellite. Although limited by today's standards, the Coastal Zone Color Scanner provided the first glimpse of the complex, beautiful, and difficult-to-sample interactions between single-celled phytoplankton and the turbulent mixing of the surface ocean. In the intervening decades, oceanographers have made tremendous advances, with more and better ocean color sensors such as the Sea-viewing Wide Field-of-view Sensor, Moderate Resolution Imaging Spectroradiometer, and Medium Resolution Imaging Spectrometer. The PNAS report by d'Ovidio et al. (1) shows us how much further we need to go and provides a glimpse into the true complexity of the surface ocean and the mechanisms driving this ecological landscape.

A Decade of Satellite Ocean Color Observations

After the successful Coastal Zone Color Scanner (CZCS, 1978-1986) demonstration that quantitative estimations of geophysical variables such as chlorophyll a and diffuse attenuation coefficient could be derived from top of the atmosphere radiances, a number of international missions with ocean color capabilities were launched beginning in the late 1990s. Most notable were those with global data acquisition capabilities, i.e., the Ocean Color and Temperature Sensor (OCTS,Japan, 1996-1997), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, United States, 1997-present), two Moderate Resolution Imaging Spectroradiometers (MODIS, United States, Terra/2000-present and Aqua/2002-present), the Global Imager (GLI, Japan, 2002-2003), and the Medium Resolution Imaging Spectrometer (MERIS, European Space Agency, 2002-present). These missions have provided data of exceptional quality and continuity, allowing for scientific inquiries into a wide variety of marine research topics not possible with the CZCS. This review focuses on the scientific advances made over the past decade using these data sets.

Remote sensing study of the phytoplankton spatial-temporal cycle in the southeastern Indian Ocean

Data from the Coastal Zone Color Scanner (CZCS) was used to study the abundance and variability of chlorophyll a in the southeastern Indian Ocean, an interesting region because it includes the Indonesian through-flow and the anomalous Leeuwin Current. This study is the first to interpret the CZCS observations in terms of the spatial-temporal variability of this large area. The period of data (1979 to 1986) covered the full cycle (pre, during and post) of a major El Nino event, which has yet to be done by the more recent ocean color sensors. The highest seasonal mean chlorophyll a concentrations along the NorthWest Shelf of Australia occurred in Summer (January-March) and in coastal areas off south-western Australia in Autumn (April-June). Concentrations in the offshore oceanic regions were mostly poor. Exceptions to this occurred in proximity to the adjacent Indonesian islands and directly south of Albany (possibly due to northwards flow of subantarctic nutrient-rich waters). A considerable interannual variation was also noted, with the highest mean chlorophyll a concentrations occurring in 1981, 1982 and 1983.

Dinocysts as proxy of primary productivity in mid–high latitudes of the Northern Hemisphere

In order to explore the potential of dinocyst assemblages in marine sediment as a proxy for primary productivity, we analyzed a reference “modern” database including 1171 sites from the North Atlantic Ocean ( n = 483), the Arctic Ocean ( n = 401) and the North Pacific Ocean ( n = 287). We compiled two sets of primary productivity data derived from satellite observations: (1) The dataset from the Coastal Zone Color Scanner (CZCS) program applied to observations from 1978 to 1989 and (2) the data set from the MODerate resolution Imaging Spectroradiometer (MODIS) program using observations from 2002 to 2005. We performed canonical correspondence analysis (CCA) on a data matrix that included 62 dinocyst taxa and eight sea-surface parameters (winter and summer salinity, winter and summer temperature, sea-ice cover, summer, winter and annual primary productivity). CCA results show that primary productivity is a determinant parameter of dinocyst assemblages (including both phototrophic and heterotrophic taxa) in the North Atlantic, North Pacific, and at hemispheric scale. In the North Pacific, the relationship between productivity and dinocyst assemblages is particularly strong. We tested the modern analogue technique to reconstruct productivity using the North Atlantic, North Pacific, Arctic and hemispheric dinocyst data sets. With the exception of the Arctic Ocean alone, which is characterized by overall low productivity, productivity can be estimated with an accuracy (Root Mean Square Error = RMSE) of ± 11–25%. The best performance is obtained for reconstruction of winter productivity from the MODIS data. It is noteworthy that the RMSE for all estimated productivity parameters is narrower than the mean differences between productivity data derived from the MODIS and CZCS data sets. Therefore, we conclude that dinocysts can be used to reconstruct productivity with an accuracy equivalent to that of primary productivity estimated from satellite observations. Application of the approach in a sedimentary core from the northwest North Atlantic (core HU 91-045-094) revealed large amplitude variations of productivity over the last 25,000 years. The use of both MODIS and CZCS datasets indicate generally low productivity during the glacial stage, the Younger Dryas and Heinrich events, with annual productivity of less than 100 gC m − 2 . The reconstructions also suggest higher productivity during the early Holocene, especially based on the MODIS data that suggest annual values of up to 350 gC m − 2 .

Determination of Primary Spectral Bands for Remote Sensing of Aquatic Environments.

About 30 years ago, NASA launched the first ocean-color observing satellite:the Coastal Zone Color Scanner. CZCS had 5 bands in the visible-infrared domain with anobjective to detect changes of phytoplankton (measured by concentration of chlorophyll) inthe oceans. Twenty years later, for the same objective but with advanced technology, theSea-viewing Wide Field-of-view Sensor (SeaWiFS, 7 bands), the Moderate-ResolutionImaging Spectrometer (MODIS, 8 bands), and the Medium Resolution ImagingSpectrometer (MERIS, 12 bands) were launched. The selection of the number of bands andtheir positions was based on experimental and theoretical results achieved before thedesign of these satellite sensors. Recently, Lee and Carder (2002) demonstrated that foradequate derivation of major properties (phytoplankton biomass, colored dissolved organicmatter, suspended sediments, and bottom properties) in both oceanic and coastalenvironments from observation of water color, it is better for a sensor to have ~15 bands inthe 400 - 800 nm range. In that study, however, it did not provide detailed analysesregarding the spectral locations of the 15 bands. Here, from nearly 400 hyperspectral (~ 3-nm resolution) measurements of remote-sensing reflectance (a measure of water color)taken in both coastal and oceanic waters covering both optically deep and optically shallowwaters, first- and second-order derivatives were calculated after interpolating themeasurements to 1-nm resolution. From these derivatives, the frequency of zero values foreach wavelength was accounted for, and the distribution spectrum of such frequencies wasobtained. Furthermore, the wavelengths that have the highest appearance of zeros wereidentified. Because these spectral locations indicate extrema (a local maximum orminimum) of the reflectance spectrum or inflections of the spectral curvature, placing the bands of a sensor at these wavelengths maximizes the potential of capturing (and then restoring) the spectral curve, and thus maximizes the potential of accurately deriving properties of the water column and/or bottom of various aquatic environments with a multi-band sensor.