Ocean color observations from satellites are the principal tool for the synoptic global monitoring of marine ecosystems. It is imperative that ocean color observations are sustained and enhanced into the future. These observations serve the expanding needs of the scientific user community as it seeks to understand long-term trends in marine ecosystems and their interactions with the global carbon cycle. In turn, managers apply this new knowledge to value and manage marine resources. This means that future sensors and algorithms need to be enhanced to support an increasing diversity of ocean color products and applications. However, creating long time-series remains a major challenge, one that is not unique to ocean color remote sensing (NRC, 2004b, 2008b). These challenges and approaches for sustaining long-term ocean color remote sensing are explored in this chapter. The key requirements include planning to ensure continuity and overlap among sensors; building and maintaining the human capital to process, reprocess, and use ocean color products for research; and building international coordination and cooperation.
Although the study task focuses on data continuity, it is important to note that continuity and advancements in the science require far more than simply sustaining SeaWiFS/MODIS-type measurements. Many applications listed in Chapter 2 require more advanced remote sensing capabilities. This chapter, therefore, explores options for enhancing ocean color research products. In addition, the U.S. academic research community, which is primarily funded by National Aeronautics and Space Administration (NASA), needs ocean color instruments such as Aerosol-Cloud-Ecosystems (ACE) and the Pre-Aerosol-Clouds-Ecosystems (PACE) for new and improved applications such as those described in a recent NASA report and summarized below.
Advancing Ocean Biology and Biogeochemistry Research
In 2007, the ocean biology and biogeochemistry research community completed a consensus document that laid out four priority science questions for the NASA Ocean Biology and Biogeochemistry program (NASA, 2007). These questions are:
• How are ocean ecosystems and the biodiversity they support influenced by climate and environmental variability and change, and how will these changes occur over time?
• How do carbon and other elements transition between various reservoirs in the ocean and Earth system, and how do biogeochemical fluxes impact the ocean and Earth’s climate over time?
• How (and why) is the diversity and geographical distribution of coastal marine habitats changing, and what are the implications for the well-being of human society?
• How do hazards and pollutants impact the hydrography and biology of the coastal zone? How do they affect us, and can we mitigate their effects?
The implementation strategy calls for a mix of new sensors including:
1. a global hyperspectral imager that would be an advanced Type 1/Type 2 sensor with capabilities as envisioned for ACE and PACE;
2. a Multi-Spectral High Spatial Resolution Imager similar to Hyperspectral Infrared Imager (HyspIRI);
3. an ocean color sensor in geostationary orbit to focus on coastal and ocean processes that require multiple observations during a single day to resolve changes on short time scales (like Geostationary Coastal and Air Pollution Events [GEOCAPE]); and
4. a space-borne LIght Detection And Ranging (LIDAR) for improved atmospheric correction and oceanographic measurements, as is also planned for ACE (NASA, 2007).
Both the NASA Ocean Biology and Biochemistry (OBB) program and the National Research Council’s (NRC) decadal survey plans call for a mix of ocean color satellite mission types that would help ocean scientists answer the high-level science questions they now face.
Global Hyperspectral Imaging Radiometer
Answering the first two priority science questions above will require the development of advanced global remote sensing capabilities. The planned NASA missions PACE/ACE will provide some of these new capabilities, including:
• Ultraviolet (UV) bands to improve the separation of chlorophyll and color dissolved organic matter (CDOM) absorption and thus significantly improved accuracy of both products. This capability is especially important because of projected changes in the ocean due to rising temperatures and ocean acidification.
• Short wave infrared (SWIR) bands (1,200-1,700 nm) demonstrated by Moderate Resolution Imaging Spectroradiometer (MODIS), which in that case led to improved atmospheric correction over turbid coastal waters, in comparison to what was achieved with Sea-viewing Wide Field-of-view Sensor (SeaWiFS).
• Additional bands in the UV that would help correct for absorbing aerosols, a major source of uncertainty for the present generation of ocean color sensors particularly in coastal waters, and a specific UV band at 317.5 nm that would provide simultaneous ozone corrections.
• Improved atmospheric correction by determining aerosol altitude and type using a profiling LIDAR, advanced polarimeter, or both as envisioned for ACE.
With these capabilities, it will be possible to separate phytoplankton functional groups such as carbon exporters (diatoms), nitrogen fixers (Trichodesmium sp.), calcium carbonate producers (coccolithophores), and the microbial loop organisms (Prochlorococcus sp.). It also will be possible to enable derivation and optimization of fluorescence retrievals, which are particularly beneficial in quantifying phytoplankton chlorophyll biomass during phytoplankton blooms and in coastal waters.
Conclusion: Advanced ocean color remote sensing capabilities are central to answering questions related to changing conditions in the marine ecosystem and biogeochemical cycles due to climate change.
Multi-Spectral High Spatial Resolution Imaging
Many coastal applications—such as monitoring for Harmful Algal Blooms (HABs), ecosystem-based fisheries management, and research on benthic habitats including coral reefs and coastal wetlands—require greater spatial resolution and additional spectral bands than are currently available from most satellites to resolve the complex optical signals that coastal waters produce. These measurements historically have been made from airborne sensors, usually flown by airplanes over a particular region. Airborne hyperspectral observations are well suited for routine studies of localized areas (e.g., coral reefs, seagrass beds) and for episodic events (e.g., HABs, oil spills) that require high spatial or spectral resolution, or on-demand repeat times. The technology is well proven for mapping shallow-water bathymetry and bottom type (e.g., Mobley et al., 2005; Dekker et al., in press), mapping and monitoring coral reefs (Hochberg and Atkinson, 2003; Lesser and Mobley, 2007), and detection of oil spills (Lennon et al., 2006). For example, the Airborne Visible and InfraRed Imaging Spectrometer1 (AVIRIS), developed by the Jet Propulsion Laboratory (JPL), made many flights over the Deepwater Horizon BP oil spill site.2 The hyperspectral information enabled researchers to map out the oil spill location and thickness. In addition, JPL is supporting the construction of a portable hyperspectral imager (i.e., the Portable Remote Imaging SpectroMeter [PRISM]3).
Although the capability has been built and demonstrated, past applications of this hyperspectral technology have been limited to short surveys yielding single snapshots of a given coastal region. Routine and sustained surveys of the U.S. coastal waters are not undertaken because it is difficult to find the necessary funding to routinely fly these airborne systems. Other countries, Australia and the People’s Republic of China in particular, have invested heavily in airborne hyperspectral imaging systems and routinely employ them in studies of their coastal and inland waters. The United States also would benefit greatly from dedicated and adequate support for airborne hyperspectral imaging systems that could be used for routine observations of coastal waters or to respond to episodic events as needed.
High spatial resolution, hyperspectral measurements also can be made from satellite missions and were recommended as part of the Decadal Survey (NRC, 2007). Airborne missions that gather such measurements provide them on an intermittent basis. Satellite hyperspectral remote sensing would make these observations routine and allow sustained application of the data for HAB detection, oil spill monitoring, shallow benthic habitat characterization, and other research and research management applications.
2 See http://www.jpl.nasa.gov/news/news.cfm?release=2010-184; accessed February 8, 2011.
Satellite hyperspectral remote sensing mission was piloted with the Hyperion EO-1 mission.4 The EO-1 satellite was launched in fall 2000 to demonstrate the technology for the Landsat Data Continuity Mission. Because of the satellite’s success, the research community successfully advocated to continue the image acquisition from EO-1. Data from the hyperspectral satellite SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), a European sensor designed to measure various trace gases, aerosols and clouds, also are used for deriving ocean color measurements and have been applied to distinguish phytoplankton groups (Bracher et al., 2009).
In addition, the Hyperspectral Imager for the Coastal Ocean (HICO;5 Lewis et al., 2009; Davis et al., 2010) was developed by the Office of Naval Research and installed on the International Space Station in late 2009. Because of mission constraints of the International Space Station, HICO currently collects only one image per orbit of selected targets, but with good results. Although unable to provide global or highly accurate data, HICO is well suited for certain applications that require high spatial or spectral data in coastal waters during a limited period. HICO collects hyperspectral imagery (380-1,000 nm by 5.7 nm bandwidth) at ~100 m spatial resolution. Its primary applications are retrieval of coastal optical properties, bathymetry, bottom classification, and water inherent optical properties (IOP), along with terrain and vegetation maps. Nevertheless, data availability to the community is minimal.
Plans also are under way for a global hyperspectral mission, the HyspIRI,6 as outlined by the Decadal Survey (NRC, 2007). HyspIRI is a Tier 2 mission in the Decadal Survey, which NASA plans to launch after 2020 (NASA, 2010). The goal of the HyspIRI mission, which is currently in the study stage, is to detect ecosystem changes due to climate change and human impacts on land and in the ocean. HyspIRI will make hyperspectral observations of radiance from 380 to 2,500 nm at 10-nm resolution with a 60-m pixel at nadir. It will also be configured with a multispectral thermal IR imager. The temporal revisit times are ~3 weeks for the visible/SWIR instrument and a one-week revisit for the thermal IR sensor. Besides looking at ecosystem changes, HyspIRI will be able to map surface rock, soil, and snow composition. Because it samples a fixed location in the global ocean only once every three weeks, HyspIRI’s ability to measure change in fast-moving planktonic communities is limited and not as useful for characterizing pelagic ocean conditions. The high spatial and spectral features of the sensor will be used to assess coastal habitats on global and seasonal scales, particularly benthic features such as corals, seagrasses, and kelp.
Conclusion: Almost all coastal research and operational applications require ocean color remote sensing capabilities that are not routinely available. To sustain and advance these coastal applications, a high spectral and high spatial resolution sensor is required.
Geostationary Radiometers and Geostationary Hyperspectral Imaging Radiometers (Type 4 sensors)
All current sensors except for the South Korean sensor Geostationary Ocean Color Imager (GOCI) are in polar orbit (see Chapter 4). South Korea launched the first geostationary ocean color sensor in June 2010 (Table 5.1). Although the limited geographic scope of this satellite makes it less relevant to the U.S. research and operational community, access to these data would be valuable for developing future geostationary missions in the United States. Initial Korean plans called for open data access in 2011.
Although the current systems in polar orbit can provide global data coverage once every day or two, they offer only coarse spatial resolution. The resulting lack of data with high spatial and temporal sampling frequency was recognized many years ago. Although near-daily data may be adequate for climate data records from the open ocean (assuming data comparability from different sensing systems), daily acquisition is inadequate for critical coastal operational and research applications. Coastal waters and shorelines exhibit significant diurnal variability. A single geostationary earth orbit (GEO) instrument could provide near-hourly data updates for the continental U.S. coasts, as required to properly characterize important changes in coastal marine environments. For example, water clarity, tidal variability of shoreline and estuaries, effluent discharge, diffusion and absorption, and other parameters must be tracked frequently (IOCCG, 2008). Because many space agencies are interested in and have plans for geostationary ocean color satellites, a new International Ocean Colour Coordinating Group (IOCCG) working group was formed to address requirements, advocate for coordination, and foster collaboration.
Two U.S. options exist for increasing the supply of ocean color data from sensors in geostationary orbit. First, a geostationary ocean color sensor hosted on a commercial satellite could be a cost-effective choice to obtain coastal high-resolution or hyperspectral ocean color radiance (for details see Appendix D). A second option is the GEOCAPE mission. The Earth Science Decadal Survey (NRC, 2007) recommended GEOCAPE as a Tier 2 mission, which NASA plans to launch after 2020 (NASA, 2010). This mission would focus on retrievals of tropospheric trace gases and aerosols and coastal ocean color from a geostationary spacecraft. Although an ocean color GEOCAPE would be optimized for coastal observation, its orbit also allows for observations of offshore waters. This capability could support research cruises within the covered area. Because GEOCAPE would be able to dwell over any area, it could
TABLE 5.1 Current and Planned Type 4 Sensors (geostationary)
|Sensor/Satellite||Agency||Launch Date||Swath||Resolution||Bands||Spectral Coverage (nm)|
|GOCI/COMS||KARI/KORDI (S. Korea)||2010||2,500||500||8||400-865|
|GOCI-II/KMGS-B||KARI/KORDI (S. Korea)||2018||1,200 × 1,500 TBD||250/1,000||13||412-1,240 TBD|
|GEOCAPE||NASA (USA)||After 2020||300 m||Hyperspectral at 10 nm|
be used to cross-calibrate radiances measured by different ocean color satellites in polar orbit.
NASA has initiated plans and science and engineering studies to equip GEOCAPE with the high spatial and temporal resolution required for coastal research.7 The capabilities of the geostationary ocean color sensor include: multiple sampling per day of continental U.S. coastal waters and Great Lakes, 300- to 375-m spatial resolution and hyperspectral resolution (with capability of binning spectral bands), broad spectral coverage including UV-visible spectrum (VIS), near-infrared (NIR), and SWIR bands, high signal-to-noise ratio (SNR) and dynamic range, cloud avoidance, minimal polarization sensitivity (<0.2 percent), minimal stray light, narrow field-of-view (FOV) optics, low scatter gratings (<0.1 percent), no image striping or latency, and the capability of performing solar and lunar on-orbit calibration.8
Conclusion: The availability of a geostationary satellite would give federal agencies concerned with the degradation of coastal habitats the necessary capabilities to monitor the near-shore environment.
Active Remote Sensing—LIDAR—to Measure the Ocean’s Scattering Properties and Phytoplankton Variable Fluorescence
Vertical changes in light-scattering properties measured through the atmosphere and into the ocean from a space-based LIDAR9 can provide important new information for solving major ocean carbon and biogeochemistry science questions. Future missions that use measurements of water-leaving radiances to retrieve geophysical parameters related to ocean elemental cycles depend on accurate atmospheric corrections. This requires a strict accounting for the contribution of absorbing aerosols to top of the atmosphere (TOA) radiances, including information on their vertical distributions and total optical thickness. LIDAR measurements, both ground-based (e.g., Micropulse) and space-based (e.g., Geoscience Laser Altimeter System [GLAS]), can provide information on vertical aerosol structure at a resolution well beyond what’s required for ocean applications (<0.5 km). LIDAR aerosol profiling measurements, simultaneous with passive radiometric data, will enable unsurpassed atmospheric corrections that will result in vastly improved ocean geophysical parameters. This capability is currently available from CALIPSO (Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observation) and is planned for the ACE mission.
In addition to aerosol assessments, LIDAR remote sensing can measure the ocean’s light-scattering properties independent of passive radiometer observations. Measures of LIDAR light scattering are related to particulate concentrations in the mixed layer (e.g., Churnside et al., 1998). Airborne LIDAR have been used for some time to demonstrate the ability to make profiles of light scattering at 532 nm and 355 nm into the water column (Wright et al., 2001). Most recently, this technique has also been applied to LIDAR measurements from space with data from CALIPSO (Hu, 2009). Radiative transfer modeling and observational data indicate that with an 100-m eye-safe Nd:Yg laser at an altitude of 600 km, sufficient subsurface scattering values can be retrieved to >15 m in clear ocean waters and to 5 m in turbid coastal waters. To cover this full range of optical conditions, space-based measurements will require 1 to 2-m vertical resolution and a LIDAR angle of incidence of 15 degrees relative to nadir (to avoid detector saturation at the surface). However, LIDAR will not provide global coverage and thus will benefit from simultaneous passive remote sensing to obtain the global coverage.
LIDAR also could enable the measurement of the photosynthetic rate and the physiological state of phytoplankton. LIDAR-based fluorescence has advantages over passive, solar-stimulated fluorescence (such as implemented on MODIS and described previously). This is because the laser source provides a consistent input of radiance and the changes can be measured over very short time periods, enabling the determination of variable fluorescence. Changes in variable fluorescence appear to behave in a consistent manner under a variety of environmental conditions, although a few stresses create unique behaviors. One of these conditions is iron-limited growth in the presence of high macronutrient concentrations. However, techniques
9 LIDAR refers to a technology that measures the property of a target by illuminating it with light and detecting the reflected light.
have been developed to distinguish these iron-limited regions from iron-replete areas (e.g., Behrenfeld and Kolber, 1999; Behrenfeld et al., 2009). Therefore, remote sensing assessments of the variable fluorescence can provide a means for globally defining HNLC conditions, monitoring temporal shifts in physiological province boundaries, and establishing functional links between new iron inputs (e.g., dust deposition events) and ecosystem responses.
Phytoplankton fluorescence kinetics has been measured in the field for more than 20 years and, through NASA support, has been successfully measured from aircraft (Chekalyuk et al., 2000). The “pump and probe” technique employed for these airborne tests will need to be modified for space-based applications to reduce LIDAR energy demands and to meet eye safety requirements. Technological solutions for these issues are under development.
Globally defining and monitoring physiological provinces through satellite variable fluorescence measurements will require aggregation of multiple LIDAR returns to improve signal to noise ratios. Measurements are needed at midnight and at dawn to determine maximal variable fluorescence and the relative nocturnal percentage decrease in variable fluorescence (both critical diagnostics). This approach would require two satellites and would also provide information on the degree of midday light inhibition of photosynthetic electron transport. The excitation laser wavelength must penetrate a wide range of ocean waters (e.g., 532 nm from a Nd:Yg laser) and be effectively absorbed by chlorophyll. Simultaneous Raman measurements at 651 nm are also required for baseline calibration. Additional capabilities for detecting fluorescence at high spectral resolution (1-2 nm) would expand the utility of the LIDAR measurements by allowing detection of specific phytoplankton groups through taxon-specific fluorescence features.
Improving Atmospheric Corrections, Ocean Color Algorithms, and Products
The availability of new satellites and additional spectral bands will require improvements in algorithms and atmospheric corrections. In addition, the creation of a more comprehensive field dataset and the use of standardized sampling protocols would significantly increase the quality of data products.
Atmospheric Corrections and Algorithm Development
Advances in bio-optical algorithms and atmospheric corrections are required to make full use of hyperspectral data now becoming available from aircraft sensors. As with atmospheric correction, other types of bio-optical inverse models have been developed by the hyperspectral airborne imaging community. A broad class of such algorithms uses spectrum matching of the atmospherically corrected Lw either to a pre-computed database of spectra or to a semi-analytic model. These spectrum matching approaches use both the radiance magnitude and shape to determine the environmental conditions that generate the best-fit spectrum, i.e., to determine quantities such as bottom depth and type, or water absorption and backscatter coefficients. Spectrum-matching makes full use of the available spectral information at all wavelengths and has proved successful in the inversion of airborne hyperspectral imagery (Mobley et al., 2005; Lesser and Mobley, 2007). These algorithms warrant further investigation by the broad ocean color community. Indeed, it is possible that spectrum matching to the TOA radiances could be used to effect a simultaneous atmospheric correction and bio-optical inversion, but this has been only briefly investigated for multispectral systems (Chomko and Gordon, 2001; Chomko et al., 2003; Stamnes, 2003). Furthermore, the potential for climate change and the complexity of the processes regulating the color of the ocean together raise questions about the suitability of empirical approaches in future oceans. Clearly, empirical band ratio algorithms are locked into the era during which these datasets are collected (Dierssen, 2010). Better assessment of ocean color in the future may come from creating advanced ocean color algorithms that assess independently the different dissolved and suspended materials that absorb and scatter light in the sea.
Similarly, improved atmospheric correction procedures may be required due to global change. Present atmospheric correction approaches assume fixed relationships between near-infrared and visible aerosol optical properties. Changes in the chemistry and light absorption characteristics of aerosols that are expected under future climate conditions would violate the assumption of constancy in aerosol optical properties. Present plans for the ACE ocean ecology spectrometer (OES) include high-quality satellite observations in the ultraviolet spectral region (as short as 345 nm). These planned observations will enable scientists to implement flexible atmospheric correction models that allow aerosol optical properties to vary, as would be anticipated in future climates.
Even a limited number of comprehensive datasets for selected water and atmospheric conditions would greatly advance ocean color remote sensing and environmental optics in general. A comprehensive dataset would have all the information needed to do a “round trip” radiative transfer (RT) calculation to propagate sunlight from the TOA, through the atmosphere to the sea surface, through the sea surface into the water, from the water back to the atmosphere, and finally through the atmosphere to the sensor (additional details about this round-trip radiative transfer calculation are in Appendix C). This RT process is the physical basis for all ocean color remote sensing and must be fully understood when evaluating the performance of any particular sensor
and the products it generates. To date, no entity has collected a truly comprehensive dataset that satisfies these RT needs.
Comprehensive oceanic and atmospheric datasets are needed for:
• development and validation of remote sensing inverse models and algorithms (i.e., TOA ocean color radiances → atmospheric correction algorithm → water-leaving radiance or remote sensing reflectance → bio-optical inversion algorithm → environmental products);
• identification of weaknesses in existing algorithms and guidance for the development of improvements; and
• validation of coupled oceanic and atmospheric RT forward models, which underlie the development of both sensors and algorithms, and of inverse models, which are the foundation of all remote sensing.
Field Data Standards and Standardized Sampling Protocols
The measurements from MOBY, the current U.S. vicarious calibration site off Lanai (Hawaiian Islands), meet strict National Institute of Standards and Technology (NIST) standards. However, users of field instruments that generate data for bio-optical algorithm development or validation do not always meet recommended protocols or use defined calibration standards. Future field observations need standardized, well-calibrated measurements that adhere to protocols to help ensure quantitative comparison of observations.10 For example, the chlorophyll concentration has long been the principal data product derived from satellite ocean color of interest to oceanographers. There are several different techniques for measuring chlorophyll but no accepted national or international standard protocol. To fill this need, NASA hosted a series of round-robin comparisons to evaluate different methods and to promote guidelines and procedures for measuring chlorophyll.11 Despite this effort, few research labs to date adhere to these standard procedures developed for chlorophyll. In addition, sky radiance measurements are necessary to characterize, for example, aerosol properties. Similar issues exist for the next-generation products, such as CDOM, particulate organic carbon (POC), particle size distribution, primary productivity, net community production, carbon export flux, etc. Future satellite ocean color missions need to take on the creation and community-wide adaptation of field measurement standards and field sampling protocols for future satellite ocean color data products.
The minimum set of measurements that would need to be made, and for which standards and protocols need to be developed and followed (when appropriate), can be summarized as follows:
• Sea level pressure, temperature, humidity, and wind speed
• Cloud condition and sea state
• Sun photometer measurements
• Direct and diffuse spectral irradiance incident onto the sea surface
• Above-surface upwelling spectral radiance in the direction needed to determine the water-leaving radiance
• Spectral absorption, beam attenuation, and backscatter coefficients
• Measurements of CDOM and CDOM spectral and fluorescence characteristics
• Concentrations of dissolved organic carbon (DOC), POC, and particulate inorganic carbon (PIC)
• Phytoplankton carbon biomass and pigment concentrations
• Net primary production, net community production, and carbon export rates
• Phytoplankton fluorescence and fluorescence quantum yields and taxonomic groups
• Particle size distribution
• Downwelling (and preferably also upwelling) spectral plane irradiance
• Upwelling spectral radiance
• Bottom depth and spectral reflectance in optically shallow waters
Extending Satellite Ocean Color Products to the Vertical Dimension
Ocean color remote sensing provides information only about the surface of the ocean, at spatial scales ranging from kilometers to global. The vertical dimension is only partially explored, from the surface to about 15-20 m in the clearest waters, and from the surface to only a few tens of centimeters in turbid waters (i.e., the “penetration depth” from which originate 90 percent of the photons exiting the water).
However, phytoplankton grow throughout the lit upper layer of the ocean to depths of 100 m or more. Therefore, ocean color sensors miss much of the information of interest; this is particularly true for the Navy, which is interested in optical properties that extend from the surface to the bottom of the ocean.
Determining the total amount of biomass or primary productivity in the ocean currently requires assumptions about how phytoplankton cells are distributed with depth: either homogeneously when the upper ocean is well mixed or with a vertical structure. This vertical structure results from intermingled physical and biological effects and often exhibits a deep maximum whose depth precisely depends
on the balance between physical and biological forcings. The vertical structure is presently reconstructed using statistical relationships between the satellite-derived values and the vertical profile established from in situ information (e.g., Morel and Berthon, 1989; Uitz et al., 2006) or using the concept of biogeographical provinces to which a given shape is assigned (Longhurst, 1998). These techniques use a small number of measured vertical profiles. Consequently, they perform poorly when applied to satellite pixels that include by, definition, all possible cloud-free areas in the ocean.
New autonomous-profiling floats and gliders equipped with optical instrumentation are now available, which provide vertical profiles of quantities such as chlorophyll fluorescence or the particulate backscattering coefficient (Boss et al., 2008; Boss and Behrenfeld, 2010). In the case of gliders, radiance and irradiance sensors are now routinely integrated for subsurface profiles (Schofield et al., 2007). A large deployment of these floats could enhance the satellite data record by providing the missing vertical dimension. Such arrays were initially designed and deployed for physical oceanography (the “Argo” array, e.g., Roemmich and Owens, 2000). These floats sample the water column between the surface and about 2,000 m, covering horizontal scales from ~1 m to ~1,000 km and temporal scales from one day to several years. Interestingly, the intersection between the spatio-temporal domains covered by both remote sensing and profiling floats encompasses the mesoscale oceanic processes as well as the seasonal cycle of mixed layer dynamics and its impact on biomass cycles. These phenomena are fundamental to understanding the impact of physical forcing on ocean biology and biogeochemical cycles.
Such Bio-Argo floats would be an ideal addition to an integrated observing system that includes ocean color satellites, optically equipped gliders, and ship-board studies. These floats would make vertical data available independent of cloud coverage and with good temporal resolution (Claustre et al., 2010). The optical Argo floats could be used to refine the satellite algorithms, improve global primary production estimates, estimate particulate organic carbon in the water, and even estimate the total sinking carbon flux (Bishop and Wood, 2009).
Recommendation: An array of “bio-geochemical floats” should be implemented and progressively expanded.
International collaboration is ongoing in order to build from the experience of the Argo network (e.g., Johnson et al., 2009). The IOCCG has also set up a working group on this topic and will issue a report in 2011.12
Long-Term Mission and Budget Planning
Long-term planning is necessary to provide continuity between satellite missions and to ensure sensor overlap. In general, it takes six to eight years to move an ocean color mission from conception to launch. This time frame can be much longer in a shrinking budget environment or when other problems arise. For example, the SeaWiFS concept was developed in 1986, but launch did not occur until 1997. Furthermore, mission and budget plans need to include provisions for all mission requirements throughout the mission’s life span and beyond. SeaWiFS was successful in part because mission planning and budget, through support from the EOS/MODIS program, included provisions not only for the sensor and satellite, but also for many of the essential elements such as vicarious calibration, stability monitoring, collection of in situ validation data, and a mission team with the tools to handle data processing and reprocessing. In addition, SeaWiFS experienced a long launch delay, which provided sufficient time to make sure the necessary infrastructure was in place for the calibration and validation effort. Less than a year from the launch date, it remains unclear who will fund and conduct the vicarious calibration for VIIRS on NPP and who will be processing and reprocessing the data. In addition, planning for in situ data collection has begun only recently. These missing elements of the VIIRS/NPP mission contribute significantly to the uncertainty about the data quality of its ocean color radiance and ocean color products, and if unresolved, might jeopardize the success of the mission.
Conclusion: A satellite mission that does not include planning and budgeting for all essential elements of a mission (e.g., vicarious calibration, stability monitoring, in situ data collection and archiving, algorithm development, data processing and reprocessing; see Chapter 3 for additional details), jeopardizes the success of the mission for many uses, especially for climate assessments.
Recommendation: To ensure success, a mission should include long-term planning and budgeting for all requirements of the mission.
Long-Term Planning for Data Stewardship
As discussed in previous chapters, producing high-quality ocean color data is complex and requires a concerted effort. As information becomes available about the sensor’s behavior, the continuous vicarious calibration effort and data reprocessing at regular intervals are vital to generate high-quality products. Therefore, plans for product development and data access and archiving need to be in place well in
advance of the sensor launch. Plans also need to specify how efforts carry over from one mission to the next to preserve data continuity.
Currently, NASA’s Ocean Color Biology Processing Group (OBPG) at Goddard Space Flight Center (GSFC) is internationally recognized as a leader in producing well-calibrated, high-quality ocean color data products from multiple satellite sensors. For example, processing and reprocessing from Level 0 to Level 2 imagery require the most skill and resources, including access to pre- and post-launch calibration data, models, ancillary data and significant computational resources for production, archiving, and distribution. In effect, these production steps determine the quality of the final data products. Access to all raw, processed, and metadata are critical if long-term time-series of ocean color products are to be constructed across different mission datasets (à la Antoine et al., 2005).
The Ocean Color Biology Processing Group at NASA GSFC currently provides this service for CZCS, SeaWiFS, and MODIS data. As new algorithms are developed, the OBPG has repeatedly reprocessed all ocean color data from SeaWiFS and MODIS to ensure a continuous, intercalibrated dataset of climate-quality data. To be able to routinely reprocess the data, the raw data, radiance at the TOA, and water-leaving radiance needs to be archived and available for the long term. In particular, the most recent version of the water-leaving radiance needs to be readily available to ensure users can generate their own derivative products.
The OBPG currently provides broad access to ocean color data by creating and deploying SeaWiFS Data Analysis System (SEADAS), an image-processing software that can be installed on many different computer platforms. The OBPG has developed the necessary modules to make data easily accessible and has built the necessary structure to archive the data, including the radiance at TOA and the water-leaving radiance. NOAA is currently building capacity but does not have the know-how to provide these comprehensive services. Although NOAA’s National Climate Data Center (NCDC) plans to archive a climate-level13 radiance data record, it is unclear how accessible the data will be. Easy access will be an important factor in contributing to the successful application of the ocean color data.
Conclusion: Because of the potential need to reprocess the raw data years after collection, the committee concludes that the water-leaving radiance and the radiance at the TOA need to be archived together with the metadata for the long term. In addition, the most recent version of the water-leaving radiance needs to be readily accessible to all users.
Conclusion: A permanent archive for repackaged Level 0 data and metadata is required to allow subsequent reprocessing and merging of individual mission data into sustained climate data records.
Both NASA and NOAA support ocean color applications, with NASA focused primarily on research and development and NOAA focused on operational uses. Because both agencies have a strong interest in climate and climate impacts, they share a common interest in climate data records (CDRs).
As previously discussed, NOAA currently lacks the demonstrated capacity to readily produce high-quality ocean color products. Moreover, the committee anticipates major challenges to generating high-quality products from the VIIRS/NPP data. However, a recent NOAA report (NOAA, 2010) makes a recommendation to build the in-house capacity for end-to-end data processing/reprocessing. If NOAA builds its own data processing/reprocessing group, two independent federal groups will be developing ocean color products. Having two groups independently process ocean color data would provide the benefit of products that could be tailored to the respective user-group. However, it results in some redundancy and potential questions about merging datasets for building long-term climate records. Given NASA’s experience with end-to-end data processing, NOAA can draw on that agency’s expertise to build its own capacity.
Conclusion: NOAA would greatly benefit from initiating and pursuing discussions with NASA for an ocean color mitigation partnership that would build on lessons learned from SeaWiFS and MODIS, in particular.
A near-term option for the partnership could be a real or virtual “center” involving NOAA and NASA personnel, with contributions from the academic research community. An important step in any research-to-operations transition is for researchers to work directly with the people developing operational capabilities. Thus, such a virtual center’s activities could include: research and development related to ocean color products that serve research and operational users; and processing/reprocessing of data from U.S. and foreign ocean color missions to ensure a sustained time-series of calibrated imagery to identify long-term trends and calibration and validation activities. These involve for example, a NOAA-operated MOBY-like site, among other activities.
Recommendation: To move toward a partnership, NASA and NOAA should form a working group14 to determine the most effective way to satisfy each agency’s need for ocean color products from VIIRS and to consider how to produce, archive, and distribute products of shared interest,
13 Climate-level means repackaged data so they look like a MODIS granule and metadata repackaged accordingly to ease the reprocessing of the Level 0 data.
14 The committee was informed in March 2011 that NOAA and NASA have formed a new ocean color working group with a composition and charge that encompasses many of the recommendations listed above.
such as CDRs, that are based on data from all U.S. ocean color missions. This group should be composed of agency representatives and also include outside experts from the ocean color research and applications community.
This working group could be the focal point for U.S. contributions to the Ocean Colour Radiometry Virtual Constellation (OCR-VC), for articulating U.S. needs for specific data from international missions, and for helping negotiate how those needs would be met. As is currently done by the Ocean Color Group at GSFC, the new working group would be the ideal mechanism to routinely interact with ocean color experts in the national and international academic community. Subcommittees of existing federal advisory committees—the Earth Science Subcommittee of the NASA Advisory Committee and NOAA’s Scientific Advisory Board (SAB)—could provide oversight. To the extent possible, the working group could develop and maintain centralized teams with all necessary skills to evaluate product quality and to take appropriate measures to improve quality for the long term. These teams would provide avenues for stakeholders to engage and would enable the research community to continuously demonstrate its need for, and ensure the existence of, overlapping programs that can maintain the essential requirements (i.e., missions and accompanying infrastructure) to generate multi-decadal climate-quality data records. To achieve a multi-decadal climate-quality data record will necessitate a permanent planning function at the international level (see discussion below).
Recommendation: This working group should engage with the scientific community, develop a unified and coordinated voice, provide long-term vision and oversight, and engage with the international community.
Building and Maintaining the Ocean Color Workforce
Developing and using high-quality ocean color research products requires a highly specialized and trained workforce with a diverse set of technical skills. Maintaining the viability of the field requires experts who know how to design and build a sensor; test and calibrate it; design and operate vicarious calibration sites; conduct validation and calibration efforts; process, reprocess, and archive ocean color data; and make these products easily available to users. In addition, data users need to be trained in satellite oceanography.
Building and Maintaining the Expertise in Government Agencies
As we learned from the SeaWiFS/MODIS experience, a team with the right mix of skills and knowledge is essential to advancing the quality of ocean color products. A group of experts needs to take responsibility for acquisition, processing, reprocessing, calibration, validation, model implementation, and distribution for satellite ocean color data. Ideally, such a group will be flexible and able to respond to and resolve a wide range of issues; the OBPG at GSFC responsible for SeaWiFS and MODIS ocean color data products provides a good model. The team has all required scientific, engineering, and technical skills to interact, for example, with competitively selected NASA science teams for individual missions.
The committee judges it to be most efficient to establish an interagency team to be involved with all ocean color missions over the long term and to interact with competitively selected science teams for individual missions. This would minimize the loss of institutional memory.
Conclusion: Long-term planning is needed that focuses on building a long-term data record, instead of focusing on building individual satellite missions. A working group that is maintained across missions is the best choice to coordinate this planning.
Specialized technical and scientific expertise in various aspects of ocean color radiometry exists within the civil service at NASA, NOAA, NIST, Office of Naval Research (ONR), and the Naval Research Laboratory (NRL), and is concentrated at NASA’s GSFC. This expertise includes sensor design and calibration, aerospace engineering, systems engineering, hydrological optics, physics, atmospheric physics, biogeochemistry, etc. Maintaining strong technical expertise within the civil service allows the agencies to tackle technical questions quickly, to provide technical oversight of major instrument contracts, and to support the vigorous exchange of scientific ideas with the academic community and other agencies.
Recommendation: NASA and NOAA should ensure sufficient levels of staffing in areas critical to the continuation of ocean color research and climate data collection.
The committee also encourages NASA and NOAA to expand the use of academic researchers in Intergovernmental Personnel Act15 positions both in managerial and technical roles. This will expand the influx of ideas from academia into the federal agencies and will increase the number of academic researchers familiar with government procedures and policies. Similarly, NASA and NOAA could enter into agreements to facilitate temporary exchange of scientists and engineers to encourage sharing of ideas and understanding of each agency’s missions, strengths, and weaknesses.
15 Through the IPA program, NASA or NOAA can temporarily bring individuals from academia and state and local governments to the agency to provide scientific, administrative, and managerial expertise.
Building and Maintaining the Expertise in Academia
Experience from all NASA ocean satellite missions demonstrates the importance of supporting a competitive research program associated with each mission. Researchers help improve data products, advance ocean color applications, and train the new workforce.
Scientists have contributed to significant, and in some cases, immediate improvements to data products by conducting research on topics such as in-water and atmospheric algorithms, sensor performance, and regional validation of in-water and atmospheric correction algorithms. This research can provide critical feedback to the mission if there is clear communication with the project team, via open meetings and workshops where results are discussed and their meaning debated.
For example, application scientists were among the first to note that initial SeaWiFS processing had yielded anomalous water-leaving radiance spectra with unusually low radiance in the blue bands (in fact, negative radiances in many coastal waters). It required several years of interaction between applications scientists and the SeaWiFS project, and several reprocessings, to fix the problem.
In addition, NASA, NSF, and NOAA have supported investigators who use ocean color imagery for basic research. The applications described in Chapter 2 lead to better understanding of ocean processes and make important societal contributions.
Further, competitive research programs often result in new applications for or new approaches to using ocean color data; these new applications add value to the mission. Lastly, research projects naturally integrate graduate students into the ocean color community, which is critical to maintain a capable workforce in the private sector, at NASA and at NOAA, with continuity through missions.
Training and Recruitment Through Summer Courses
Many scientists in academia and government, and several federal agency program managers, are graduates of summer courses such as those at the University of Maine or Cornell University. For example, the intensive summer course in optical oceanography and remote sensing at the University of Maine was first taught in the mid-1980s by experts from around the world. It is held every two or three years and has achieved an international reputation as a career-molding course. Applications always far outnumber the 12 to 15 seats available. The course has evolved with the science to include lectures and extensive hands-on laboratory and field work, using many instruments of optical oceanography and vicarious calibration. NASA and other federal agencies have provided substantial funding for the course.
The IOCCG is currently organizing a recurring summer lecture series, “Frontiers in Ocean Optics and Ocean Colour Science.” This class would build on and complement other courses. While the course at the University of Maine focuses on hands-on training with a strong laboratory and field component, the IOCCG course would focus on current critical issues and emerging topics of optical oceanography and ocean color remote sensing research.
The large number of applications for ocean color products demonstrates the demand for these intensive summer courses. The limitation in both cases is funding to cover faculty salaries, travel, laboratory and field costs, and student per diem expenses. The committee considers such costs to be small compared to the benefits of maintaining a highly trained and skilled workforce.
Collaborating and Coordinating Internationally to Sustain Ocean Color Observations
Two international committees—the IOCCG and the OCR-VC16 organized under the Committee on Earth Observing Satellites (CEOS)—support international cooperation for ocean color research and operations. The IOCCG is a committee of users and space agency representatives. Among the primary IOCCG products are monographs on a wide range of topics that provide essential consensus advice to those planning and operating satellite ocean color missions. The OCR-VC is a consortium of representatives from agencies that operate satellite ocean color missions. It seeks to coordinate activities related to post-launch calibration and validation, data merging, and sharing of essential pre-launch characterization and calibration information. OCR-VC also promotes training programs and outreach.
The activities of the IOCCG and OCR-VC are increasingly important to U.S. users of satellite ocean color products because neither NASA nor NOAA will provide all required data products for at least a decade. For example, the United States currently has no geostationary mission in orbit and limited availability of high spatial resolution imagery (250-to 300-m pixels). Second, non-U.S. missions have the potential to provide essential backup for global imagery in the event of a failure during the launch or in the early stages of a U.S. mission. Finally, merging data from multiple sensors significantly enhances global coverage. For these reasons, it is essential that NASA and NOAA continue to support the activities of the IOCCG and the OCR-VC.
The production of a climate-quality long-term record of ocean color requires international collaboration. As discussed above, establishing a climate-quality long-term data record exceeds the capacity and mandate of a single U.S. agency. In general, it is difficult to maintain such long-term commitments due to budget uncertainties. International collaboration to establish a long-term record can be a good hedge against the uncertain funding from any single space agency or nation.
Examples of successful international collaboration already exist in several domains. The Centre National d’Etudes Spatiales (CNES) and NASA have established a fruitful partnership to prepare, implement, and exploit altimeter missions (Jason series and Topex/Poseidon), leading to long-term, global sea-level data records that demonstrate a gradual increase in sea level (IPCC, 2007). The Group for High-Resolution Sea Surface Temperature (GHRSST) has brought together many space agencies involved in measuring sea surface temperature from space in order to establish standards, perform comparisons of products and algorithms, and produce global datasets by merging data from different satellites.
These efforts involved not only space agencies but also the science community, which has to be organized in order to have a strong and single voice. The ocean color community still lacks this mechanism, but the creation of the OCR-VC represents a step forward. The OCR-VC provides a framework for agencies to strengthen collaboration and leverage individual efforts. The IOCCG is considering options for how to make such a virtual constellation a reality for the ocean color community.
Another promising development is ESA’s Climate Change Initiative (CCI) to produce Essential Climate Variables (ECV; CDRs in NOAA/NASA terminology). ESA selected ocean color as one of the first 10 ECV projects that began in summer 2010. The goals for the ocean color ECV are: generate the most complete multi-sensor global satellite data products for climate research and modeling that meet Global Climate Observing System (GCOS) ocean color ECV requirements; quantify pixel uncertainties for different regions; and assess the applicability and impact of ocean color ECV products on ecosystem and climate models. Another key goal is to form teams of observational scientists and climate modelers to ensure that ECVs are correctly incorporated in climate models and models of climate impacts.
ESA recognizes the importance of international cooperation for this project and lists NASA, NOAA, JAXA, and the IOCCG as external partners. Further, a closer NASA/NOAA-led partnership with ESA would be a major contribution to the OCR-VC and could stimulate participation by other agencies (e.g., JAXA, ISRO), potentially bringing other expertise and satellite datasets into the project. From users’ perspective, this international partnership could go a long way toward providing high-quality ocean color data from many different missions and for many applications.
Prototype products of the first 10 ECVs will be available in 2011-2012. Complete time-series (from multiple sensors) will be available beginning in mid-2013. The objective for the ocean color time-series is to reduce bias among sensors (MERIS, SeaWiFS, and MODIS) to less than 1 percent, an ambitious goal that will require reprocessing of all satellite datasets.
A NASA/NOAA-led project, similar to NASA’s SIMBIOS program, could help meet this goal and complement the ESA effort. Essential components would include revisiting calibration factors and how they change over time for multiple sensors; updating in situ databases for product validation; evaluating quality of in situ data; generating match-up datasets (satellite and in situ) to confirm accuracy; incorporating VIIRS/NPP data into the time-series; and other activities.
Conclusion: The CCI initiative presents an opportunity for real progress toward establishing a seamless time-series for a climate-quality ocean color data record. Engaging experts at NASA and NOAA could significantly contribute to the success of this initiative with mutual benefits for the United States and the international community.
Conclusion: The IOCCG is the logical entity to lead the planning and to build international support for the establishment of a global climate-quality ocean color data record. NASA’s and NOAA’s support for and engagement with this group will be essential to the success of these efforts.
The expanding user community and the diversity of ocean color applications that fuel research and benefit ocean ecosystem health demonstrate the critical need to sustain and advance ocean color observations from satellites well into the future. As discussed in detail in Chapter 2, ocean color remote sensing is the only way to obtain a global view of the ocean biology. Ocean color data are essential to improving the understanding of the climate system, including global carbon fluxes. Ocean color satellite observations also are used to assess the health of the marine ecosystem and its ability to sustain important fisheries. Any interruption in the ocean color record would severely hamper the work of climate scientists, fisheries and marine resource managers, and an expanding array of other users, from the military to oil spill responders.
Increasing the spatial or temporal resolution, especially in coastal waters, would enable further advances in research and resource management. In particular, an ocean color satellite in geostationary orbit would fill an important gap in observational capability (Appendix D). High spectral imaging and a satellite in geostationary orbit would significantly improve the ability to monitor for example HABs and coral reef health. Similarly, the addition of Bio-Argo floats would turn the two-dimensional satellite ocean color imagery into dynamic three-dimensional depictions of the ocean biosphere. These and other enhancements described above would add tremendous value to oceanography, but they will be difficult to balance with the requirements to simply maintain the current capabilities, especially in the current budget environment. Thus, it will require careful and strategic long-term planning, in addition to international collaboration and coordination, to meet these diverse needs.
Mission planning proved the concept (CZCS) and led to the successful demonstration that water-leaving radiance can be of such high accuracy that climate trends can be quantified (SeaWiFS and MODIS). However, building a climate-quality time-series comes with stringent requirements—most notably the need for continuity and sensor inter-calibration. Now, planning will need to extend beyond the next mission and establish a strategy to sustain the climate-quality data record.
Conclusion: A data-oriented long-term planning approach will need to replace a mission-oriented approach for the global climate-quality ocean color record.
To develop this climate-quality time-series and to advance ocean remote sensing, strategic long-term planning and budgeting is required for all aspects of follow-on missions, including how data are reprocessed, accessed, and stored across the individual missions. In addition, the institutional memory and workforce need to be maintained and transitioned across individual missions to ensure some measure of consistency and to avoid inefficiencies. NOAA and NASA will continue to have mutual interests in the ocean color climate data record as well as in advances in remote sensing. Therefore, they would benefit from sharing in the development of these long-term plans.
Going forward, a national working group similar to the international IOCCG working group, with strong governance, clear mandate, and financial resources, is needed to guide the direction of ocean color remote sensing in the United States and to implement changes at the national level. This committee also would provide oversight and long-term vision for the development of U.S. ocean color missions and the delivery of ocean color products to users. The long-term vision also needs to ensure the next generation of satellite oceanographers is sufficiently trained in order to maintain the required expertise at every level, from sensor engineering to data processing and application.
In the long term, simply sustaining the current capabilities of ocean color remote sensing will fall short of supporting the array of ocean color applications described in Chapter 2. Many ocean color applications require a commitment to advancing current capabilities. Foremost, these advances need to include hyperspectral and active imaging capabilities and a sensor in geostationary orbit for coastal applications.
Therefore, the committee recommends above that NASA and NOAA form a working group to determine the most effective way to satisfy each agency’s need for ocean color products from VIIRS and future ocean color sensors. This working group could be the focal point for U.S. collaborations with the international community and for articulating U.S. needs for specific data from international missions, for helping to negotiate how those needs will be met and for advocating for advanced capabilities to support future ocean color applications.
Moreover, because the community will require distinct types of satellite sensors to meet all data product needs, no single nation will be able to develop or even maintain capabilities on its own. NASA and NOAA will need to continue to actively engage and contribute to the development of the international OCR-VC. Ocean color remote sensing needs to be an internationally shared effort.