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An airborne instrument for determining ocean color and measurements made with the instrument are discussed. It was concluded that a clear relationship exists between the chlorophyll concentration and the color of the water. High altitude measurements from 50,000 feet are described and the effects of atmospheric scattering on the energy reaching the sensor are examined. The measured spectrum of ocean color at high and low altitudes is plotted.
Ocean color and infrared imagery from U2 aircraft and satellite sensors are used to study upwelling interaction between Gulf Stream and continental shelf waters in the Georgia Bight. The photographic data are combined with in situ measurements of currents, chlorophyll, temperature, salinity, coastal winds, and sea-level in observations of five different upwelling events including a near-short wind-driven upwelling caused by topographic effects, three filament-induced upwellings in the Gulf Stream, and a possible meander-induced upwelling event in the Gulf Stream. Chlorophyll distributions are used to trace the circulation and propagation of filaments along the advective routes by which the water moves offshore. Photographic and mooring array measurements of temperature time series are found to provide nearly identical results for the phase speeds of each event. Field measurements of surface pigments, and Nimbus/7 coastal zone color scanner (CZCS) estimates are found to agree well over the range of concentrations 0.1 to 0.7 mg/m to the third. Examples of U2/Ocean Color Scanner and Nimbus 7 CZCS photographs are provided.
The apparent color of the ocean is determined by the interactions of incident light with substances or particles present in the water. The most significant constituents are free-floating photosynthetic organisms (phytoplankton) and inorganic particulates. Phytoplankton contain chlorophyll, which absorbs light at blue and red wavelengths and transmits in the green. Particulate matter can reflect and absorb light, which reduces the clarity (light transmission) of the water. Substances dissolved in water can also affect its color. Observations of ocean color from space, utilizing sensors specially designed to detect the small amount of light radiating from the sea surface, provide a global picture of the patterns of biological productivity in the world's oceans. For that reason, ocean color remote sensing data is a vital resource for biological oceanography. Unlike the limited area of the ocean that can be investigated from a research ship, data from a satellite sensor covers a large region and provides a comprehensive view of the marine environment.
Reported here for the first time is the use of a single airborne instrument to make concurrent measurements of oceanic chlorophyll concentration by (1) laser-induced fluorescence, (2) passive upwelling radiance, and (3) solar-induced chlorophyll fluorescence. Results from field experiments conducted with the NASA airborne oceanographic lidar (AOL) in the New York Bight demonstrate the capability of a single active-passive instrument to perform new and potentially important ocean color studies related to (1) active lidar validation of passive ocean color in-water algorithms, (2) chlorophyll a in vivo fluorescence yield variability, (3) calibration of active multichannel lidar systems, (4) effect of sea state on passive and active ocean color measurements, (5) laser/solar-induced chlorophyll fluorescence investigations, and (6) subsequent improvement of satellite-borne ocean color scanners. For validation and comparison purposes a separate passive ocean color sensor was also flown along with the new active-passive sensor during these initial field trials.
Ocean primary production (PP), representing the uptake of inorganic carbon through photosynthesis, supports marine life and affects carbon exchange with the atmosphere. It is difficult to ascertain its magnitude, variability, and trends due to our inability to measure it directly at large scales. Yet it is paramount for understanding changes in marine health, fisheries, and the global carbon cycle. Using assimilation of ocean color satellite data into an ocean biogeochemical model, we estimate that global net ocean PP has experienced a small but significant decline −0.8 PgC/y (−2.1%) per decade (P < 0.05) in the 18-year satellite record from 1998 to 2015. This decline is associated with shallowing surface mixed layer depth (−2.4% per decade) and decreasing nitrate concentrations (−3.2% per decade). Relative contributions to PP by various types of ocean phytoplankton have changed, with decreases in production by intermediate-sized phytoplankton represented by chlorophytes (−14.3% per decade). This is partially compensated by increases from the unique, more nutrient-efficient, coccolithophores (8.4% per decade). Geographically, the North and Equatorial Indian Oceans are responsible for much of the decline in PP, falling 0.16 and 0.69 PgC/y per decade, respectively. Reduced production by large, fast-growing diatoms along with chlorophytes characterizes the decline here. In contrast, increases in PP are found in the North and North Central Pacific. The increases here are led by chlorophytes in the North Pacific and the small cyanobacteria in the North Central Pacific. These results suggest that the multi-decadal satellite observational record, coupled with an underlying representation of marine biodiversity in a model, can monitor the uptake of carbon by phytoplankton and that changes, although small, are occurring in the global oceans.
A collaborative measurement program for evaluating satellite-derived ocean color has been developed based on ships of opportunity (merchant, oceanographic) and specific instrumentation, the SIMBAD radiometer. The purpose of the measurement program is to complement, in a cost-effective way, dedicated evaluation experiments at sea, which are expensive, cannot be carried out over the full range of expected oceanic and atmospheric conditions, and generally provide a few match-ups. Ships participate in the program on a volunteer basis or at a very small cost, and measurement procedures do not interfere with other ship activities. The SIMBAD radiometer is a portable, easy-to-operate instrument that measures the basic ocean color variables, namely aerosol optical thickness and water-leaving radiance, in typical spectral bands of ocean-color sensors, i.e., 443, 490, 560, 670, and 870 nm. Measuring these variables at the time of satellite overpass is usually sufficient to verify satellite-derived ocean color and to evaluate atmospheric correction algorithms. Any ordinary crew can learn quickly how to make measurements. Importantly, the ship is not required to stop, making it possible to collect data along regular routes traveled by merchant ships in the world's oceans.
In late 1978, the National Aeronautics and Space Administration (NASA) launched the Nimbus-7 satellite with the Coastal Zone Color Scanner (CZCS) and several other sensors, all of which provided major advances in Earth remote sensing. The inspiration for the CZCS is usually attributed to an article in Science by Clarke et al. who demonstrated that large changes in open ocean spectral reflectance are correlated to chlorophyll-a concentrations. Chlorophyll-a is the primary photosynthetic pigment in green plants (marine and terrestrial) and is used in estimating primary production, i.e., the amount of carbon fixed into organic matter during photosynthesis. Thus, accurate estimates of global and regional primary production are key to studies of the earth's carbon cycle. Because the investigators used an airborne radiometer, they were able to demonstrate the increased radiance contribution of the atmosphere with altitude that would be a major issue for spaceborne measurements. Since 1978, there has been much progress in satellite ocean color remote sensing such that the technique is well established and is used for climate change science and routine operational environmental monitoring. Also, the science objectives and accompanying methodologies have expanded and evolved through a succession of global missions, e.g., the Ocean Color and Temperature Sensor (OCTS), the Seaviewing Wide Field-of-view Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS), and the Global Imager (GLI). With each advance in science objectives, new and more stringent requirements for sensor capabilities (e.g., spectral coverage) and performance (e.g., signal-to-noise ratio, SNR) are established. The CZCS had four bands for chlorophyll and aerosol corrections. The Ocean Color Imager (OCI) recommended for the NASA Pre-Aerosol, Cloud, and Ocean Ecosystems (PACE) mission includes 5 nanometers hyperspectral coverage from 350 to 800 nanometers with three additional discrete near infrared (NIR) and shortwave infrared (SWIR) ocean aerosol correction bands. Also, to avoid drift in sensor sensitivity from being interpreted as environmental change, climate change research requires rigorous monitoring of sensor stability. For SeaWiFS, monthly lunar imaging accurately tracked stability at an accuracy of approximately 0.1% that allowed the data to be used for climate studies [2]. It is now acknowledged by the international community that future missions and sensor designs need to accommodate lunar calibrations. An overview of ocean color remote sensing and a review of the progress made in ocean color remote sensing and the variety of research applications derived from global satellite ocean color data are provided. The purpose of this chapter is to discuss the design options for ocean color satellite radiometers, performance and testing criteria, and sensor components (optics, detectors, electronics, etc.) that must be integrated into an instrument concept. These ultimately dictate the quality and quantity of data that can be delivered as a trade against mission cost. Historically, science and sensor technology have advanced in a "leap-frog" manner in that sensor design requirements for a mission are defined many years before a sensor is launched and by the end of the mission, perhaps 15-20 years later, science applications and requirements are well beyond the capabilities of the sensor. Section 3 provides a summary of historical mission science objectives and sensor requirements. This progression is expected to continue in the future as long as sensor costs can be constrained to affordable levels and still allow the incorporation of new technologies without incurring unacceptable risk to mission success. The IOCCG Report Number 13 discusses future ocean biology mission Level-1 requirements in depth.
After the end of the SeaWiFS mission in 2010 and the MERIS mission in 2012, the ocean color products of the MODIS on Aqua are the only remaining source to continue the ocean color climate data record until the VIIRS ocean color products become operational (expected for summer 2013). The MODIS on Aqua is well beyond its expected lifetime, and the calibration accuracy of the short wavelengths (412nm and 443nm) has deteriorated in recent years_ Initially, SeaWiFS data were used to improve the MODIS Aqua calibration, but this solution was not applicable after the end of the SeaWiFS mission_ In 2012, a new calibration methodology was applied by the MODIS calibration and support team using desert sites to improve the degradation trending_ This presentation presents further improvements to this new approach. The 2012 reprocessing of the MODIS Aqua ocean color products is based on the new methodology.
Sensor design and mission planning for satellite ocean color measurements requires careful consideration of the signal dynamic range and sensitivity (specifically here signal-to-noise ratio or SNR) so that small changes of ocean properties (e.g., surface chlorophyll-a concentrations or Chl) can be quantified while most measurements are not saturated. Past and current sensors used different signal levels, formats, and conventions to specify these critical parameters, making it difficult to make cross-sensor comparisons or to establish standards for future sensor design. The goal of this study is to quantify these parameters under uniform conditions for widely used past and current sensors in order to provide a reference for the design of future ocean color radiometers. Using measurements from the Moderate Resolution Imaging Spectroradiometer onboard the Aqua satellite (MODISA) under various solar zenith angles (SZAs), typical (L(sub typical)) and maximum (L(sub max)) at-sensor radiances from the visible to the shortwave IR were determined. The Ltypical values at an SZA of 45 deg were used as constraints to calculate SNRs of 10 multiband sensors at the same L(sub typical) radiance input and 2 hyperspectral sensors at a similar radiance input. The calculations were based on clear-water scenes with an objective method of selecting pixels with minimal cross-pixel variations to assure target homogeneity. Among the widely used ocean color sensors that have routine global coverage, MODISA ocean bands (1 km) showed 2-4 times higher SNRs than the Sea-viewing Wide Field-of-view Sensor (Sea-WiFS) (1 km) and comparable SNRs to the Medium Resolution Imaging Spectrometer (MERIS)-RR (reduced resolution, 1.2 km), leading to different levels of precision in the retrieved Chl data product. MERIS-FR (full resolution, 300 m) showed SNRs lower than MODISA and MERIS-RR with the gain in spatial resolution. SNRs of all MODISA ocean bands and SeaWiFS bands (except the SeaWiFS near-IR bands) exceeded those from prelaunch sensor specifications after adjusting the input radiance to L(sub typical). The tabulated L(sub typical), L(sub max), and SNRs of the various multiband and hyperspectral sensors under the same or similar radiance input provide references to compare sensor performance in product precision and to help design future missions such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission and the Pre-Aerosol-Clouds-Ecosystems (PACE) mission currently being planned by the U.S. National Aeronautics and Space Administration (NASA).
NASA's Ocean Color Experiment (OCE) was designed to map ocean features with an eight-channel scanning radiometer installed on the second flight of the Space Shuttle Columbia. Digital computer enhancement and band-ratio techniques were applied to radiometrically corrected spectral data in order to emphasize chlorophyll distribution patterns. Such a pattern was evident in the Yellow Sea between Korea and China, and the effects of discharge from the Yangtze and other rivers were also observed. Two scenes, from orbits 30 and 32, revealed the movement of plankton patches in the Gulf of Cadiz. Geometric correction of the images permitted ocean current velocities to be deduced, and water depth variability over the Grand Bahama Bank was estimated by means of OCE's blue-green channel. Bottom-reflected sunlight produced a sensor signal that was inversely related to water depth.
Satellite ocean color missions require accurate (< 1% uncertainties) system vicarious calibrations (SVC) in order to retrieve the relatively small remote sensing reflectance (Rrs, sr-1) from the at-sensor radiance. However, current atmospheric correction and SVC procedures do not include calibration of the “long” near infrared band (NIRL–869nm for MODIS), partially because earlier studies indicated that accuracy in the retrieved Rrsis insensitive to moderate changes in the NIRLvicarious gain (g). However, the sensitivity of ocean color data products to g(NIRL) has not been thoroughly examined. Here, we first derived 10 SVC ‘gain configurations’ (vicarious gains for all visible and NIR bands) for MODIS/Aqua using current operational NASA protocols, each time assuming a different g(869). From these, we derived a suite of ~1.4E6 unique gain configurations with g(869) ranging from 0.85 to 1.2. All MODIS/A data for 25locations within each of five ocean gyres were then processed using each of these gain configurations. Such time series show substantial variability inthe dominant Rrs(547) patterns in response to changes in g(869). Overall gyre mean Rrs(547) generally decreased with increasing g(869), while the standard deviations around those means show gyre-specific minima for 0.97 < g(869) < 1.02. Following these sensitivity analyses, we assess the potential to resolve g(869) using suchtime series, finding g(869) = 1.025 most closely comports with expectations. This approach is broadly applicable to other ocean color sensors, and highlights the importance of rigorous cross-sensor calibration of the NIRLbands, with implications on consistency of merged-sensor datasets.
The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depth-integrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 C, and chlorophyll concentration exceeding 1mg Chlm-3. Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate.
The investigation s main objective is to collect from platforms of opportunity (merchant ships, research vessels) concomitant normalized water-leaving radiance and aerosol optical thickness data over the world s oceans. A global, long-term data set of these variables is needed to verify whether satellite retrievals of normalized water-leaving radiance are within acceptable error limits and, eventually, to adjust atmospheric correction schemes. To achieve this objective, volunteer officers, technicians, and scientists onboard the selected ships collect data from portable SIMBAD and Advanced SIMBAD (SIMBADA) radiometers. These instruments are specifically designed for evaluation of satellite-derived ocean color. They measure radiance in spectral bands typical of ocean-color sensors. The SIMBAD version measures in 5 spectral bands centered at 443, 490, 560, 670, and 870 nm, and the Advanced SIMBAD version in 11 spectral bands centered at 350, 380, 412, 443, 490, 510, 565, 620, 670, 750, and 870 nm. Aerosol optical thickness is obtained by viewing the sun disk like a classic sun photometer. Normalized water-leaving radiance, or marine reflectance, is obtained by viewing the ocean surface through a vertical polarizer in a specific geometry (nadir angle of 45o and relative azimuth angle of 135deg) to minimize direct sun glint and reflected sky radiation. The SIMBAD and SIMBADA data, after proper quality control and processing, are delivered to the SIMBIOS project office for inclusion in the SeaBASS archive. They complement data collected in a similar way by the Laboratoire d'Optique Atmospherique of the University of Lille, France. The SIMBAD and SIMBADA data are used to check the radiometric calibration of satellite ocean-color sensors after launch and to evaluate derived ocean-color variables (i.e., normalized water-leaving radiance, aerosol optical thickness, and aerosol type). Analysis of the SIMBAD and SIMBADA data provides information on the accuracy of satellite retrievals of normalized water-leaving radiance, an understanding of the discrepancies between satellite and in situ data, and algorithms that reduce the discrepancies, contributing to more accurate and consistent global ocean color data sets.
The Marine Optical Buoy (MOBY) is the centerpiece of the primary ocean measurement site for calibration of satellite ocean color sensors based on independent in situ measurements. Since late 1996, the time series of normalized water-leaving radiances L(sub WN)(lambda) determined from the array of radiometric sensors attached to MOBY are the primary basis for the on-orbit calibrations of the USA Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Japanese Ocean Color and Temperature Sensor (OCTS), the French Polarization Detection Environmental Radiometer (POLDER), the German Modular Optoelectronic Scanner on the Indian Research Satellite (IRS1-MOS), and the USA Moderate Resolution Imaging Spectrometer (MODIS). The MOBY vicarious calibration L(sub WN)(lambda) reference is an essential element in the international effort to develop a global, multi-year time series of consistently calibrated ocean color products using data from a wide variety of independent satellite sensors. A longstanding goal of the SeaWiFS and MODIS (Ocean) Science Teams is to determine satellite-derived L(sub WN)(labda) with a relative combined standard uncertainty of 5 %. Other satellite ocean color projects and the Sensor Intercomparison for Marine Biology and Interdisciplinary Oceanic Studies (SIMBIOS) project have also adopted this goal, at least implicitly. Because water-leaving radiance contributes at most 10 % of the total radiance measured by a satellite sensor above the atmosphere, a 5 % uncertainty in L(sub WN)(lambda) implies a 0.5 % uncertainty in the above-atmosphere radiance measurements. This level of uncertainty can only be approached using vicarious-calibration approaches as described below. In practice, this means that the satellite radiance responsivity is adjusted to achieve the best agreement, in a least-squares sense, for the L(sub WN)(lambda) results determined using the satellite and the independent optical sensors (e.g. MOBY). The end result of this approach is to implicitly absorb unquantified, but systematic, errors in the atmospheric correction, incident solar flux, and satellite sensor calibration into a single correction factor to produce consistency with the in situ data.
The objective of the program is to develop and validate a procedure for ocean color data merging, which is one of the major goals of the SIMBIOS project. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances L((sub wN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) It works with single or multiple data sources regardless of their specific bands; 2) It exploits band redundancies and band differences; 3) It accounts for uncertainties in the L((sub wN)(lambda)) data; 4) It provides uncertainty estimates for the retrieved variables.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.
A key on-orbit calibration step for satellite remote sensing of ocean color is the vicarious calibration. This establishes the final gains for each spectral band on the sensor that minimize bias in the retrieved ocean color signal. The vicarious calibration is specific to the instrument and the atmospheric correction algorithm. The vicarious calibration gains for the Geostationary Ocean Color Imager (GOCI) are presented here, which were derived to optimize the performance ofNASA's standard atmospheric correction algorithm as implemented in the l2gen code and distributed through the SeaDAS open-source software package. Following NASA's protocols, the near-infrared(NIR) bands were calibrated first, and the visible bands were then calibrated relative to this fixed NIR calibration. The gain for the 745-nm NIR band was derived using a fixed aerosol model, which waschosen based on the Angstrom Coefficients derived from MODIS onAqua (MODISA). For the vicarious gains of the visible bands, twosources for the target water-leaving radiances were tested: matchupsfrom MODISA and climatological data from SeaWiFS. A validation analysis using AERONET-OC data shows an improvement in sensor performance when compared with results using the current vicarious gains and results using no vicarious calibration. Good agreement was found in vicarious gains derived using both concurrent MODISA and climatological SeaWiFS as vicarious calibration data sources. These results support the use of a concurrent sensor for the vicarious calibration when in situ data are not available and demonstrate that using climatology from a well-calibrated sensor like SeaWiFSfor the vicarious calibration is a valid alternative when it is not possible to use a concurrent sensor or in situ data. We recommend using the gains derived from concurrent GOCI matchups with MODISA for GOCI processing in SeaDAS/l2gen.
Remote airborne measurements of ocean color