The ocean is a fundamental component of Earth’s biosphere. Because the ocean is so vast and difficult for humans to explore, satellite remote sensing of ocean color is currently the only way to observe and monitor the biological state of the surface ocean globally on time scales of days to decades.
The ocean covers roughly 70 percent of Earth’s surface and plays a pivotal role in the cycling of life’s building blocks such as nitrogen, carbon, oxygen, and sulfur. The ocean also contributes to regulating the climate system. For example, the land and ocean together removed 57 percent of all anthropogenic carbon dioxide (CO2) emissions from 1958 to 2009,1 with the ocean accounting for about half of this. By removing CO2 from the atmosphere, the ocean moderates the rate of human-induced climate change. In addition, the CO2 dissolving in the ocean produces carbonic acid, which is causing the ocean to become more acidic. Moreover, the ocean has absorbed approximately 90 percent of the increased heat associated with climate change (Lyman et al., 2010). As the ocean grows warmer and more acidic, these changes may have adverse effects on whole groups of marine organisms (NRC, 2010). Additional stressors—such as overfishing, nutrient pollution from land runoff, coastal development, and invasive species—further jeopardize the health of the ocean and the vital functions it provides (NRC, 2004a).
Monitoring the health of the ocean and its productivity is critical to understanding and managing the ocean’s essential functions and living resources. Phytoplankton are microscopic organisms responsible for most of the primary production2 in the ocean, are ubiquitous in the surface ocean, and form the base of the marine food web. Tracking changes in phytoplankton in the vast expanse of the ocean requires a perspective that can be gained only from satellite measurements (NRC, 2008a). Ocean color measurements from space have revolutionized our understanding of the ocean on every scale, from local to global and from days to decades.
Ocean color measurements reveal a wealth of ecologically important characteristics including: chlorophyll concentration (a proxy for the biomass of marine plants or phytoplankton), the rate of phytoplankton photosynthesis, sediment transport, dispersion of pollutants, and responses of oceanic biota to long-term climate changes (IOCCG, 2008). Many scientists and operational users, such as managers of coastal resources and fisheries, rely on these measurements for research, ecosystem monitoring, and resource management.
Deriving biological parameters from ocean color measurements is a multi-stage process. Ocean color radiometric sensors measure the upwelling radiance at the top of the atmosphere (LTOA). As illustrated in Figure 1.1, LTOA is the total radiances from three sources: water-leaving radiance (Lw) radiance reflected from the sea surface (surface-reflected radiance), and radiance scattered into the viewing direction by the atmosphere along the path between the sensor and sea surface (atmospheric path radiance).
Of these three radiance sources, the desired measurement is Lw, referred to in this report simply as ocean color. Lw carries information about the biological and chemical constituents in the near-surface waters. To obtain Lw, it is necessary to deduce and remove the contributions of surface reflection and atmospheric path radiance from the measured total, a process known as atmospheric correction. This is difficult because Lw is no more than 10 percent of LTOA, as illustrated in Figure 1.2.
There are four levels of processing of satellite data:
Level 0: Raw data as measured directly from the spacecraft in engineering units (e.g., volts or digital counts).
Level 1: Level 0 data converted to TOA radiance using pre-launch sensor calibration and characterization information adjusted during the life of the mission by vicarious calibration and stability monitoring (for details see Chapter 3). For scientific applications, and in particular to generate
1 See http://www.globalcarbonproject.org/carbonbudget/09/hl-full. htm#naturalSinks; accessed 1/7/2011.
2 Primary production or photosynthesis converts carbon dioxide and water into carbohydrates and oxygen in the presence of light.
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1 Introduction The ocean is a fundamental component of Earth’s biosphere. Because the ocean is so vast and difficult for humans to explore, satellite remote sensing of ocean color is currently the only way to observe and monitor the biological state of the surface ocean globally on time scales of days to decades. T he ocean covers roughly 70 percent of Earth’s surface phytoplankton), the rate of phytoplankton photosynthesis, and plays a pivotal role in the cycling of life’s building sediment transport, dispersion of pollutants, and responses of blocks such as nitrogen, carbon, oxygen, and sulfur. oceanic biota to long-term climate changes (IOCCG, 2008). The ocean also contributes to regulating the climate system. Many scientists and operational users, such as managers For example, the land and ocean together removed 57 percent of coastal resources and fisheries, rely on these measure- of all anthropogenic carbon dioxide (CO2) emissions from ments for research, ecosystem monitoring, and resource 1958 to 2009,1 with the ocean accounting for about half management. of this. By removing CO2 from the atmosphere, the ocean moderates the rate of human-induced climate change. In DERIVING OCEAN PROPERTIES FROM OCEAN addition, the CO2 dissolving in the ocean produces carbonic COLOR RADIANCE acid, which is causing the ocean to become more acidic. Moreover, the ocean has absorbed approximately 90 percent Deriving biological parameters from ocean color mea- of the increased heat associated with climate change (Lyman surements is a multi-stage process. Ocean color radiometric et al., 2010). As the ocean grows warmer and more acidic, sensors measure the upwelling radiance at the top of the these changes may have adverse effects on whole groups of atmosphere (LTOA). As illustrated in Figure 1.1, LTOA is the marine organisms (NRC, 2010). Additional stressors—such total radiances from three sources: water-leaving radiance as overfishing, nutrient pollution from land runoff, coastal (Lw) radiance reflected from the sea surface (surface-reflected development, and invasive species—further jeopardize the radiance), and radiance scattered into the viewing direction health of the ocean and the vital functions it provides (NRC, by the atmosphere along the path between the sensor and sea 2004a). surface (atmospheric path radiance). Monitoring the health of the ocean and its productiv- Of these three radiance sources, the desired measure- ity is critical to understanding and managing the ocean’s ment is Lw, referred to in this report simply as ocean color. essential functions and living resources. Phytoplankton are Lw carries information about the biological and chemical microscopic organisms responsible for most of the primary constituents in the near-surface waters. To obtain Lw, it is production2 in the ocean, are ubiquitous in the surface ocean, necessary to deduce and remove the contributions of surface and form the base of the marine food web. Tracking changes reflection and atmospheric path radiance from the measured in phytoplankton in the vast expanse of the ocean requires a total, a process known as atmospheric correction. This is perspective that can be gained only from satellite measure- difficult because Lw is no more than 10 percent of LTOA, as ments (NRC, 2008a). Ocean color measurements from space illustrated in Figure 1.2. have revolutionized our understanding of the ocean on every There are four levels of processing of satellite data: scale, from local to global and from days to decades. Level 0: Raw data as measured directly from the space- Ocean color measurements reveal a wealth of eco - logically important characteristics including: chlorophyll craft in engineering units (e.g., volts or digital counts). Level 1: Level 0 data converted to TOA radiance using concentration (a proxy for the biomass of marine plants or pre-launch sensor calibration and characterization informa- 1 tion adjusted during the life of the mission by vicarious cali - S ee http://www.globalcarbonproject.org/carbonbudget/09/hl-full. htm#naturalSinks; accessed 1/7/2011. bration and stability monitoring (for details see Chapter 3). 2 Primary production or photosynthesis converts carbon dioxide and For scientific applications, and in particular to generate water into carbohydrates and oxygen in the presence of light. 8
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9 INTRODUCTION Satellite Measured TOA radiance Atmospheric path radiance Surface-reflected radiance Water-leaving radiance Atmosphere Ocean surface Surface layer of the ocean FIGURE 1.1 Qualitative illustration of the contributions of water-leaving radiance Lw surface glint, and atmospheric path radiance to the measured TOA radiance. SOURCE: Adapted from http:www.gps.gov/multimedia/images/. 1.1.eps Climate Data Records (CDRs), it is essential to archive Level 0 data, pre-launch calibration and characterization informa- tion, and post-launch calibration and stability monitoring data to enable periodic reprocessing of the raw data. Note: CDRs have been defined as “time-series of measurements of sufficient length, consistency, and continuity to determine climate variability and change,” in the NRC report on CDRs from Environmental Satellites (NRC, 2004b). Level 2: Level 2 data are generated from Level 1 data following atmospheric correction that are in the same satel- lite viewing coordinates as Level 1 data (i.e., the data have not been mapped to a standard map projection or placed on a grid). Level 2 data include Lw and derived products. Satellite viewing angles and other information are used to FIGURE 1.2 Quantitative1.2.eps of the contributions of illustration map any single Level 2 scene to a standard map projection water-leaving, surface-reflected, and atmospheric path radiance (see definition of Level 3 data). Lw or ocean color radiance bitmap to the measured TOA radiance. The water-leaving radiance—the is generated from Level 1 radiance following atmospheric signal—is at most 10 percent of the TOA radiance (simulations correction. Atmospheric correction for optically deep water3 by the HydroLight and Modtran radiative transfer models using requires sensor measurements at near and short wave infrared typical oceanic and atmospheric properties and 10-nm wavelength wavelengths, ancillary measurements such as sea-level atmo- resolution). 3 Optically deep water refers to water that is deep enough that the bottom reflectance does not contribute to the water-leaving radiance.
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10 SUSTAINED OCEAN COLOR RESEARCH AND OPERATIONS spheric pressure and wind speed, and models of atmospheric been validated at various scales, from single images to global aerosol properties. The resulting measurement of ocean composites, and used for a broad array of applications. Nev- color radiance is a well-defined geophysical property whose ertheless, assumptions inherent to these algorithms need to measurement adheres to national and international standards. be continuously tested and updated in a changing ocean. Ocean color radiance is considered the fundamental product Particulate organic and inorganic carbon concentrations and from which all other ocean color products are derived. Colored Dissolved Organic Matter (CDOM) absorption char- N ote: A lthough there is community consensus on acteristics can also be derived from Lw spectra. A growing the meaning of ocean color radiance, there are multiple number of new primary products are being developed, such approaches and algorithms for generating various deriva- as inherent optical properties (e.g., phytoplankton absorption tive products such as measures of chlorophyll or primary and backscatter coefficients) and concentrations of other production. Thus, it is critical that the path from Level 0 to suspended material, including various components of the Level 2 be well understood and documented and that the particulate carbon and dissolved carbon pools in the ocean. Marine net primary production,4 a secondary product data to make these conversions be permanently archived. Periodic reprocessing begins with Level 0 data and uses (Figure 1.3), illustrates the utility of ocean color measure- knowledge of how sensor calibration has changed with ments when combined with high-quality in situ data. Esti- time, better ancillary information, improved algorithms, and mating net primary production requires ocean color measure- other lessons learned during the mission. Reprocessing is an ments as well as other sources of information such as sea essential mission requirement for generating quantitative surface temperature or mixed layer depths. The importance data products, particularly climate data records. Moreover, of in situ data to enhance ocean color remote sensing will standard atmospheric correction techniques for Level 2 be revisited in Chapter 5. Scientists also use ocean color processing are designed for open ocean waters and might measurements in combination with other data to learn about not perform well in turbid coastal water, optically shallow the composition of phytoplankton. They accomplish this water, and in coastal areas experiencing atmospheric pol - either by partitioning the total chlorophyll concentration into lutants and dust. major size classes (pico-, nano- and micro-phytoplankton) Level 3: Level 3 products are those that have been or into major phytoplankton functional groups (diatoms, mapped to a known cartographic projection or placed on a coccolithophores, blue-green algae, floating sargassum), or two-dimensional grid at known spatial resolution. Level 0, by identifying nuisance or harmful algal blooms. However, 1, and 2 products are expressed in satellite coordinates and some methods for retrieving phytoplankton functional types are not particularly useful to most applications of satellite are estimated directly from ocean color radiance (e.g., the data. Level 3 data products are often aggregated over time or diatom discrimination algorithm of Sathyendranath et al., space. These products are widely disseminated to scientific 2004; the algorithm of Alvain et al., 2005). and operational users. Level 4: Although gridded satellite data provide far bet- RATIONALE FOR THIS STUDY ter coverage in space and time than is possible with in situ data, most users want to validate such maps independently Over the past three decades, the oceanographic com- for their regions of study through comparisons with in situ munity has witnessed astounding growth in the capabilities data. Results derived from a combination of satellite data and of ocean color remote sensing. The Sea-viewing Wide Field- ancillary information, such as ecosystem model output, are of-view Sensor-Moderate Resolution Imaging Spectroradi- called Level 4 products. ometer (SeaWiFS-MODIS) era from 1997 to present has New and better algorithms and ocean color products provided scientists with a high-quality, well-calibrated Lw continue to emerge as technology and atmospheric cor- time-series from which to estimate chlorophyll concentra- rections improve. As the scientific understanding advances tion and primary production. As a result, for the first time, a climate-quality5 data record can be compiled to demonstrate regarding the relationships between ocean color radiance and the types of particulate and dissolved substances found the strong link between interannual climate variability and in water, new and improved algorithms and ocean color the marine biosphere during the El Niño to La Niña transi- products continue to emerge. Primary products (Figure 1.3) tion on ocean-basin scales. Many of these recent discoveries are derived with algorithms that rely exclusively on Lw and and accomplishments in biological oceanography have been its relationship to the desired product, such as chlorophyll described in Earth Observations from Space: The First 50 concentration. Secondary products require knowledge about their relationships to Lw as well as ancillary information 4 Net primary production quantifies the net conversion of carbon dioxide obtained from other sensors, in situ observations, or models. and water into carbohydrates and oxygen in the presence of light and Chlorophyll concentration, the best known and most represents the energy supply to the base of marine food webs. 5 To demonstrate long-term trends in a time-series with large natural commonly used ocean color product, is an example of a interannual variability, the data record requires very high accuracy. For primary product. The algorithms for determining it are well the ocean color climate record, accuracy requirements are discussed in developed, and satellite-derived chlorophyll values have Chapters 3-5.
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11 INTRODUCTION Lunar Raw Data Calibration (e.g., volts) Vicarious Calibration LEVEL 0 Pre-launch Calibration Radiance at Top of the Atmosphere Atmospheric LEVEL 1 Correction Lw( λ ) Algorithms Primary Products (e.g., chlorophyll, fluorescence, CDOM, PAR, PIC, POC, K490, suspended sediments) LEVEL 2 Auxiliary Data Secondary Products (e.g., primary productivity, calcification, HAB likelihood, phytoplankton functional groups, Trichodesmium estimates, Cocolithophore estimates) Mapped Primary and Secondary Products LEVEL 3 Binned Primary and Secondary Products LEVEL 4 1.3.eps FIGURE 1.3 Ocean color radiance is used to derive products directly or indirectly. Secondary products are based on the primary products and ancillary data. These products are then used to address scientific and societal questions. Some satellite missions apply the vicarious calibration when processing Level 2 data. (CDOM: Colored Dissolved Organic Matter; PAR: Photosynthetically Available Radiance; PIC: Particulate Inorganic Carbon; POC: Particulate Organic Carbon; K490: diffuse attenuation coefficient at 490 nm; HAB: Harmful Algal Bloom). entitled Advanced Plan for the Ocean Biology and Biogeo- Years of Scientific Achievements (NRC, 2008a) and a recent chemistry Program (NASA, 2007); continued support for International Ocean Colour Coordinating Group report ocean color remote sensing is needed to improve computer- (IOCCG, 2008). based modeling of ecosystem dynamics. Ocean color remote To sustain and build on these achievements, the cli- sensing data are necessary to build accurate and useful mate research community requires access to uninterrupted models related to climate change, which will increase our climate-quality data records for the marine biosphere. Such understanding of variability of in situ organic and inorganic records are central to validating new, more sophisticated carbon constituents; continental shelf ecosystem dynamics climate models that incorporate biogeochemical processes, and variability of the mixed-layer thickness; and variability such as primary production, and validating and improving of particulates and aerosols in the ocean and atmosphere. the accuracy of products in a changing ocean. Furthermore, as the length of the climate-quality ocean color Moreover, as detailed in the research community’s plan
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12 SUSTAINED OCEAN COLOR RESEARCH AND OPERATIONS data record grows, its intrinsic value for recording long-term mism about the sensor’s performance (see Chapter 3 for a changes in the marine ecosystem also increases. detailed discussion). In more good news, planning has started It is important to note that, with the availability of rou- at NASA for a new ocean color mission, the Pre-Aerosol- tine measurements and with free and easy access to ocean Clouds-Ecosystem (PACE) mission, with a launch date of color data, the user community has expanded dramatically to 2019 or later. include state and federal coastal and fisheries resource man- In December 2010, the SeaWiFS mission ceased opera- agers who depend on the data for ecosystem monitoring (e.g., tion. Thus the ocean color community lost the sensor that had coral ecosystems and harmful algal blooms). Therefore, any become the gold standard for ocean color remote sensing. gap in the time-series—regardless of how short—would be Lastly, the Deep Water Horizon oil spill that began in detrimental not only to the ocean color and climate research April 2010 was a stark reminder of coastal communities’ community but also to resource managers. dependence on healthy marine ecosystems. The spill rein- forced the ways in which human activities can jeopardize those ecosystems and the communities that rely on them for THE STUDY’S TASK their livelihoods and survival. Remote sensing from both To assess the risk for losing access to high-quality Lw planes and satellites was critical in monitoring and projecting data and to identify mitigation options, the National Oce- the evolution of the oil slick and highlighted the importance anic and Atmospheric Administration (NOAA), NASA, the of ocean color remote sensing to an oil spill response. National Science Foundation (NSF), and the Office of Naval Research (ONR) asked the National Academy of Sciences REPORT ROADMAP (NAS) to convene an ad hoc committee. The committee was asked to assess the requirements to sustain global ocean color To address the task, this report identifies in Chapter 2 the research and operational applications (see Box 1.1 for the research and operational applications for ocean color prod- statement of task). ucts and the data specifications to generate them. Chapter 3 Since the task statement was written, significant changes evaluates lessons from past and current sensors and missions. related to ocean color remote sensing have occurred that Based on these lessons learned, the committee establishes have shifted the baseline for this study. Most significantly, the minimum requirements for sustaining the capability to in February 2010, the White House ordered the restructuring obtain remotely sensed ocean color data. Chapter 4 assesses of the National Polar-orbiting Operational Environmental the gaps in meeting the requirements, evaluates current and Satellite System (NPOESS) program and a separation of the future capabilities of U.S. and foreign missions, and provides civilian programs from the Department of Defense (DOD) options to minimize the risk of a data gap in the near term. program. The civilian portion of the NPOESS program has Chapter 5 provides a long-term view; it describes challenges become the Joint Polar Satellite System (JPSS). In addition, in meeting all research and operational requirements and lists the latest Visible Infrared Imager Radiometer Suite (VIIRS) many existing opportunities for building on lessons learned characterization yielded positive results and cautious opti- and advancing current capabilities.
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13 INTRODUCTION Box 1.1 Statement of Task Continuity of satellite ocean color data and associated climate research products are presently at significant risk for the U.S. ocean color community. Temporal, radiometric, spectral, and geometric performance of future global ocean color observing systems must be considered in the context of the full range of research and operational/application user needs. This study aims to identify the ocean color data needs for a broad range of end users, develop a consensus for the minimum requirements, and outline options to meet these needs on a sustained basis. An ad hoc committee will assess lessons learned in global ocean color remote sensing from the SeaWiFS/MODIS era to guide planning for acquisition of future global ocean color radiance data to support U.S. research and operational needs. In particular, the committee will assess the sensor and system requirements necessary to produce high-quality global ocean color climate data records that are consistent with those from SeaWiFS/MODIS. The committee will also review the operational and research objectives, such as described in the Ocean Research Priorities Plan and Implementation Strategy, for the next generation of global ocean color satellite sensors and provide guidance on how to ensure both operational and research goals of the oceanographic community are met. In particular the study will address the following: 1. Identify research and operational needs, and the associated global ocean color sensor and system high-level require- ments for a sustained, systematic capability to observe ocean color radiance (OCR) from space; 2. Review the capability, to the extent possible based on available information, of current and planned national and international sensors in meeting these requirements (including but not limited to: VIIRS on NPP and subsequent JPSS spacecrafts; MERIS on ENVISAT and subsequent sensors on ESA’s Sentinel-3; S-GLI on JAXA’s GCOM-C; OCM-2 on ISRO’s Oceansat-2; COCTS on SOA’s HY-1; and MERSI on CMA’s FY-3); 3. Identify and assess the observational gaps and options for filling these gaps between the current and planned sensor capabilities and timelines; define the minimum observational requirements for future ocean color sensors based on future oceanographic research and operational needs across a spectrum of scales from basin-scale synoptic to local process study, such as expected system launch dates, lifetimes, and data accessibility; 4. Identify and describe requirements for a sustained, rigorous on-board and vicarious calibration and data validation program, which incorporates a mix of measurement platforms (e.g., satellites, aircraft, and in situ platforms such as ships and buoys) using a layered approach through an assessment of needs for multiple data user communities; and 5. Identify minimum requirements for a sustained, long-term global ocean color program within the United States for the maintenance and improvement of associated ocean biological, ecological, and biogeochemical records, which ensures continuity and overlap among sensors, including plans for sustained rigorous on-orbit sensor inter-calibration and data validation; algorithm development and evaluation; data processing, re-processing, distribution, and archiving; as well as recommended funding levels for research and operational use of the data. The review will also evaluate the minimum observational research requirements in the context of relevant missions outlined in previous NRC reports, such as the NRC “Decadal Survey” of Earth Science and Applications from Space. The committee will build on the Advance Plan developed by NASA’s Ocean Biology and Biogeochemistry program and comment on future ocean color remote sensing support of oceanographic research goals that have evolved since the publication of that report. Also included in the review will be an evaluation of ongoing national and international planning efforts related to ocean color measurements from geostationary platforms.