SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) was a satellite-borne sensor designed to collect global ocean biological data. Active from September 1997 to December 2010, its primary mission was to quantify chlorophyll produced by marine phytoplankton (microscopic plants).
SeaWiFS was the only scientific instrument on GeoEye's OrbView-2 (AKA SeaStar) satellite, and was a follow-on experiment to the Coastal Zone Color Scanner on Nimbus 7. Launched August 1, 1997 on an Orbital Sciences Pegasus small air-launched rocket, SeaWiFS began scientific operations on September 18, 1997 and stopped collecting data on December 11, 2010, [1] far exceeding its designed operating period of 5 years. [2] The sensor resolution is 1.1 km (LAC, "Local Area Coverage") and 4.5 km (GAC, "Global Area Coverage"). The sensor recorded information in the following optical bands:
Band | Wavelength |
---|---|
1 | 402–422 nm |
2 | 433–453 nm |
3 | 480–500 nm |
4 | 500–520 nm |
5 | 545–565 nm |
6 | 660–680 nm |
7 | 745–785 nm |
8 | 845–885 nm |
The instrument was specifically designed to monitor ocean characteristics such as chlorophyll-a concentration and water clarity. It was able to tilt up to 20 degrees to avoid sunlight from the sea surface. This feature is important at equatorial latitudes where glint from sunlight often obscures water colour. SeaWiFS had used the Marine Optical Buoy for vicarious calibration.
The SeaWiFS Mission is an industry/government partnership, with NASA's Ocean Biology Processing Group at Goddard Space Flight Center having responsibility for the data collection, processing, calibration, validation, archive and distribution. The current SeaWiFS Project manager is Gene Carl Feldman.
Chlorophyll concentrations are derived from images of the ocean's color. Generally speaking, the greener the water, the more phytoplankton are present in the water, and the higher the chlorophyll concentrations. Chlorophyll a absorbs more blue and red light than green, with the resulting reflected light changing from blue to green as the amount of chlorophyll in the water increases. Using this knowledge, scientists were able to use ratios of different reflected colors to estimate chlorophyll concentrations.
Many formulas estimate chlorophyll by comparing the ratio of blue to green light and relating those ratios to known chlorophyll concentrations from the same times and locations as the satellite observations. The color of light is defined by its wavelength, and visible light has wavelengths from 400 to 700 nanometers, progressing from violet (400 nm) to red (700 nm). A typical formula used for SeaWiFS data (termed OC4v4) divides the reflectance of the maximum of several wavelengths (443, 490, or 510 nm) by the reflectance at 550 nm. This roughly equates to a ratio of blue light to green light for two of the numerator wavelengths, and a ratio of two different green wavelengths for the other possible combination.
The reflectance (R) returned by this formula is then plugged into a cubic polynomial that relates the band ratio to chlorophyll. [3]
This formula, along with others, was derived empirically using observed chlorophyll concentrations. To facilitate these comparisons, NASA maintains a system of oceanographic and atmospheric data called SeaBASS (SeaWiFS Bio-optical Archive and Storage System). This data archive is used to develop new algorithms and validate satellite data products by matching chlorophyll concentrations measured directly with those estimated remotely from a satellite. These data can also be used to assess atmospheric correction (discussed below) that also can greatly influence chlorophyll concentration calculations.
Numerous chlorophyll algorithms were tested to see which ones best matched chlorophyll globally. Various algorithms perform differently in different environments. Many algorithms estimate chlorophyll concentrations more accurately in deep clear water than in shallow water. In shallow waters reflectance from other pigments, detritus, and the ocean bottom may cause inaccuracies. The stated goals of the SeaWiFS chlorophyll estimates are "… to produce water leaving radiances with an uncertainty of 5% in clear-water regions and chlorophyll a concentrations within ±35% over the range of 0.05–50 mg m-3.". [2] When accuracy is assessed on a global scale, and all observations are grouped together, then this goal is clearly met. [5] Many satellite estimates range from one-third to three times of those directly recorded at sea, though the overall relationship is still quite good. [4] Differences arise when examined by region, though overall the values are still very useful. One pixel may not be particularly accurate, though when averages are taken over larger areas, the values average out and provide a useful and accurate view of the larger patterns. The benefits of chlorophyll data from satellites far outweigh any flaws in their accuracy simply by the spatial and temporal coverage possible. Ship-based measurements of chlorophyll cannot come close to the frequency and spatial coverage provided by satellite data.
Light reflected from the sub-surface ocean is called water-leaving radiance and is used to estimate chlorophyll concentrations. However, only about 5–10% of light at the top of the atmosphere is from water-leaving radiance. [6] [7] The remainder of light is reflected from the atmosphere and from aerosols within the atmosphere. In order to estimate chlorophyll concentrations this non-water-leaving radiance must be accounted for. Some light reflected from the ocean, such as from whitecaps and sun glint, must also be removed from chlorophyll calculations since they are representative ocean waves or the angle of the sun instead of the subsurface ocean. The process of removing these components is called atmospheric correction. [8]
A description of the light, or radiance, observed by the satellite's sensor can be more formally expressed by the following radiative transfer equation:
Where LT(λ) is total radiance at the top of the atmosphere, Lr(λ) is Rayleigh scattering by air molecules, La(λ) is scattering by aerosols in the absence of air, Lra(λ) is interactions between air molecules and aerosols, TLg(λ) is reflections from glint, t(Lf(λ) is reflections from foam, and LW(λ)) is reflections from the subsurface of the water, or the water-leaving radiance. [2] Others may divide radiance into some slightly different components, [8] though in each case the reflectance parameters must be resolved in order to estimate water-leaving radiance and thus chlorophyll concentrations.
Though SeaWiFS was designed primarily to monitor ocean chlorophyll a concentrations from space, it also collected many other parameters that are freely available to the public for research and educational purposes. These parameters aside from chlorophyll a include reflectance, the diffuse attenuation coefficient, particulate organic carbon (POC) concentration, particulate inorganic carbon (PIC) concentration, colored dissolved organic matter (CDOM) index, photosynthetically active radiation (PAR), and normalized fluorescence line height (NFLH). In addition, despite being designed to measure ocean chlorophyll, SeaWiFS also estimates Normalized Difference Vegetation Index (NDVI), which is a measure of photosynthesis on land.
SeaWiFS data are freely accessible from a variety of websites, most of which are government run. The primary location for SeaWiFS data is NASA's OceanColor website , which maintains the time series of the entire SeaWiFS mission. The website allows users to browse individual SeaWiFS images based on time and area selections. The website also allows for browsing of different temporal and spatial scales with spatial scales ranging from 4 km to 9 km for mapped data. Data are provided at numerous temporal scales including daily, multiple days (e.g., 3, 8), monthly, and seasonal images, all the way up to composites of the entire mission. Data are also available via ftp and bulk download.
Data can be browsed and retrieved in a variety of formats and levels of processing, with four general levels from unprocessed to modeled output. [9] Level 0 is unprocessed data that is not usually provided to users. Level 1 data are reconstructed but either unprocessed or minimally processed. Level 2 data contain derived geophysical variables, though are not on a uniform space/time grid. Level 3 data contain derived geophysical variables binned or mapped to a uniform grid. Lastly, Level 4 data contain modeled or derived variables such as ocean primary productivity .
Scientists who aim to create calculations of chlorophyll or other parameters that differ from those provided on the OceanColor website would likely use Level 1 or 2 data. This might be done, for example, to calculate parameters for a specific region of the globe, whereas the standard SeaWiFS data products are designed for global accuracy with necessary tradeoffs for specific regions. Scientists who are more interested in relating the standard SeaWiFS outputs to other processes will commonly use Level 3 data, particularly if they do not have the capacity, training, or interest in working with Level 1 or 2 data. Level 4 data may be used for similar research if interested in a modeled product.
NASA offers free software designed specifically to work with SeaWiFS data through the ocean color website. This software, entitled SeaDAS (SeaWiFS Data Analysis System), is built for visualization and processing of satellite data and can work with Level 1, 2, and 3 data. Though it was originally designed for SeaWiFS data, its capabilities have since been expanded to work with many other satellite data sources. Other software or programming languages can also be used to read in and work with SeaWiFS data, such as Matlab, IDL, or Python.
Estimating the amount of global or regional chlorophyll, and therefore phytoplankton, has large implications for climate change and fisheries production. Phytoplankton play a huge role in the uptake of the world's carbon dioxide, a primary contributor to climate change. A percentage of these phytoplankton sink to ocean floor, effectively taking carbon dioxide out of the atmosphere and sequestering it in the deep ocean for at least a thousand years. Therefore, the degree of primary production from the ocean could play a large role in slowing climate change. Or, if primary production slows, climate change could be accelerated. Some have proposed fertilizing the ocean with iron in order to promote phytoplankton blooms and remove carbon dioxide from the atmosphere. Whether these experiments are undertaken or not, estimating chlorophyll concentrations in the world's oceans and their role in the ocean's biological pump could play a key role in our ability to foresee and adapt to climate change.
Phytoplankton is a key component in the base of the oceanic food chain and oceanographers have hypothesized a link between oceanic chlorophyll and fisheries production for some time. [10] The degree to which phytoplankton relates to marine fish production depends on the number of trophic links in the food chain, and how efficient each link is. Estimates of the number of trophic links and trophic efficiencies from phytoplankton to commercial fisheries have been widely debated, though they have been little substantiated. [11] More recent research suggests that positive relationships between chlorophyll a and fisheries production can be modeled [12] and can be very highly correlated when examined on the proper scale. For example, Ware and Thomson (2005) found an r2 of 0.87 between resident fish yield (metric tons km-2) and mean annual chlorophyll a concentrations (mg m-3). [13] Others have found the Pacific's Transition Zone Chlorophyll Front (chlorophyll density of 0.2 mg m-3) to be defining feature in loggerhead turtle distribution. [14]
The reflectance of the surface of a material is its effectiveness in reflecting radiant energy. It is the fraction of incident electromagnetic power that is reflected at the boundary. Reflectance is a component of the response of the electronic structure of the material to the electromagnetic field of light, and is in general a function of the frequency, or wavelength, of the light, its polarization, and the angle of incidence. The dependence of reflectance on the wavelength is called a reflectance spectrum or spectral reflectance curve.
An algal bloom or algae bloom is a rapid increase or accumulation in the population of algae in freshwater or marine water systems. It is often recognized by the discoloration in the water from the algae's pigments. The term algae encompasses many types of aquatic photosynthetic organisms, both macroscopic multicellular organisms like seaweed and microscopic unicellular organisms like cyanobacteria. Algal bloom commonly refers to the rapid growth of microscopic unicellular algae, not macroscopic algae. An example of a macroscopic algal bloom is a kelp forest.
Phytoplankton are the autotrophic (self-feeding) components of the plankton community and a key part of ocean and freshwater ecosystems. The name comes from the Greek words φυτόν, meaning 'plant', and πλαγκτός, meaning 'wanderer' or 'drifter'.
In radiometry, photometry, and color science, a spectral power distribution (SPD) measurement describes the power per unit area per unit wavelength of an illumination. More generally, the term spectral power distribution can refer to the concentration, as a function of wavelength, of any radiometric or photometric quantity.
Photosynthetically active radiation (PAR) designates the spectral range of solar radiation from 400 to 700 nanometers that photosynthetic organisms are able to use in the process of photosynthesis. This spectral region corresponds more or less with the range of light visible to the human eye. Photons at shorter wavelengths tend to be so energetic that they can be damaging to cells and tissues, but are mostly filtered out by the ozone layer in the stratosphere. Photons at longer wavelengths do not carry enough energy to allow photosynthesis to take place.
High-nutrient, low-chlorophyll (HNLC) regions are regions of the ocean where the abundance of phytoplankton is low and fairly constant despite the availability of macronutrients. Phytoplankton rely on a suite of nutrients for cellular function. Macronutrients are generally available in higher quantities in surface ocean waters, and are the typical components of common garden fertilizers. Micronutrients are generally available in lower quantities and include trace metals. Macronutrients are typically available in millimolar concentrations, while micronutrients are generally available in micro- to nanomolar concentrations. In general, nitrogen tends to be a limiting ocean nutrient, but in HNLC regions it is never significantly depleted. Instead, these regions tend to be limited by low concentrations of metabolizable iron. Iron is a critical phytoplankton micronutrient necessary for enzyme catalysis and electron transport.
MEdium Resolution Imaging Spectrometer (MERIS) was one of the main instruments on board the European Space Agency (ESA)'s Envisat platform. The sensor was in orbit from 2002 to 2012. ESA formally announced the end of Envisat's mission on 9 May 2012.
The coastal zone color scanner (CZCS) was a multi-channel scanning radiometer aboard the Nimbus 7 satellite, predominately designed for water remote sensing. Nimbus 7 was launched 24 October 1978, and CZCS became operational on 2 November 1978. It was only designed to operate for one year (as a proof-of-concept), but in fact remained in service until 22 June 1986. Its operation on board the Nimbus 7 was limited to alternate days as it shared its power with the passive microwave scanning multichannel microwave radiometer.
Colored dissolved organic matter (CDOM) is the optically measurable component of dissolved organic matter in water. Also known as chromophoric dissolved organic matter, yellow substance, and gelbstoff, CDOM occurs naturally in aquatic environments and is a complex mixture of many hundreds to thousands of individual, unique organic matter molecules, which are primarily leached from decaying detritus and organic matter. CDOM most strongly absorbs short wavelength light ranging from blue to ultraviolet, whereas pure water absorbs longer wavelength red light. Therefore, water with little or no CDOM, such as the open ocean, appears blue. Waters containing high amounts of CDOM can range from brown, as in many rivers, to yellow and yellow-brown in coastal waters. In general, CDOM concentrations are much higher in fresh waters and estuaries than in the open ocean, though concentrations are highly variable, as is the estimated contribution of CDOM to the total dissolved organic matter pool.
Ocean color is the branch of ocean optics that specifically studies the color of the water and information that can be gained from looking at variations in color. The color of the ocean, while mainly blue, actually varies from blue to green or even yellow, brown or red in some cases. This field of study developed alongside water remote sensing, so it is focused mainly on how color is measured by instruments.
The deep chlorophyll maximum (DCM), also called the subsurface chlorophyll maximum, is the region below the surface of water with the maximum concentration of chlorophyll. The DCM generally exists at the same depth as the nutricline, the region of the ocean where the greatest change in the nutrient concentration occurs with depth.
The marine optical buoy (MOBY) measures light at and very near the sea surface in a specific location over a long period of time, serving as part of an ocean color observation system. Satellites are another component of the system, providing global coverage through remote sensing; however, satellites measure light above the Earth's atmosphere, becoming subject to interference from the atmosphere itself and other light sources. The Marine Optical Buoy helps alleviate that interference and thus improve the quality of the overall ocean color observation system.
Gene Carl Feldman has been an oceanographer at NASA Goddard Space Flight Center (GSFC) since 1985. His primary interest has been to try to make the data that NASA gathers from its spaceborne fleet of Earth observing instruments, especially those monitoring the subtle changes in ocean color, as scientifically credible, readily understandable and as easily available to the broadest group of people possible. He has been involved in a number of past and present NASA missions including the Coastal Zone Color Scanner (CZCS), the Sea-Viewing Wide Field Sensor (SeaWiFS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) and along with the NASA Ocean Biology Processing group which he co-leads, been given the responsibility for designing, implementing and operating the data processing and mission operations component of ocean salinity mission called Aquarius, a space mission developed by NASA and the Space Agency of Argentina - Comisión Nacional de Actividades Espaciales (CONAE) that was successfully launched in June 2011 and began routine operations on December 1, 2011 and completed its prime mission in June 2015.
Water Remote Sensing is the observation of water bodies such as lakes, oceans, and rivers from a distance in order to describe their color, state of ecosystem health, and productivity. Water remote sensing studies the color of water through the observation of the spectrum of water leaving radiance. From the spectrum of color coming from the water, the concentration of optically active components of the upper layer of the water body can be estimated via specific algorithms. Water quality monitoring by remote sensing and close-range instruments has obtained considerable attention since the founding of EU Water Framework Directive.
The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project is a large scale National Science Foundation funded research project based at Princeton University that started in September 2014. The project aims to increase the understanding of the Southern Ocean and the role it plays in factors such as climate, as well as educate new scientists with oceanic observation.
The North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) was a five-year scientific research program that investigated aspects of phytoplankton dynamics in ocean ecosystems, and how such dynamics influence atmospheric aerosols, clouds, and climate. The study focused on the sub-arctic region of the North Atlantic Ocean, which is the site of one of Earth's largest recurring phytoplankton blooms. The long history of research in this location, as well as relative ease of accessibility, made the North Atlantic an ideal location to test prevailing scientific hypotheses in an effort to better understand the role of phytoplankton aerosol emissions on Earth's energy budget.
Remote sensing in oceanography is a widely used observational technique which enables researchers to acquire data of a location without physically measuring at that location. Remote sensing in oceanography mostly refers to measuring properties of the ocean surface with sensors on satellites or planes, which compose an image of captured electromagnetic radiation. A remote sensing instrument can either receive radiation from the earth’s surface (passive), whether reflected from the sun or emitted, or send out radiation to the surface and catch the reflection (active). All remote sensing instruments carry a sensor to capture the intensity of the radiation at specific wavelength windows, to retrieve a spectral signature for every location. The physical and chemical state of the surface determines the emissivity and reflectance for all bands in the electromagnetic spectrum, linking the measurements to physical properties of the surface. Unlike passive instruments, active remote sensing instruments also measure the two-way travel time of the signal; which is used to calculate the distance between the sensor and the imaged surface. Remote sensing satellites often carry other instruments which keep track of their location and measure atmospheric conditions.
Ocean optics is the study of how light interacts with water and the materials in water. Although research often focuses on the sea, the field broadly includes rivers, lakes, inland waters, coastal waters, and large ocean basins. How light acts in water is critical to how ecosystems function underwater. Knowledge of ocean optics is needed in aquatic remote sensing research in order to understand what information can be extracted from the color of the water as it appears from satellite sensors in space. The color of the water as seen by satellites is known as ocean color. While ocean color is a key theme of ocean optics, optics is a broader term that also includes the development of underwater sensors using optical methods to study much more than just color, including ocean chemistry, particle size, imaging of microscopic plants and animals, and more.
The SeaWiFS Bio-optical Archive and Storage System (SeaBASS) is a data archive of in situ oceanographic data used to support satellite remote sensing research of ocean color. SeaBASS is used for developing algorithms for satellite-derived variables and for validating or “ground-truthing” satellite-derived data products. The acronym begins with “S” for SeaWiFS, because the data repository began in the 1990s around the time of the launch of the SeaWiFS satellite sensor, and the same data archive has been used ever since. Oceanography projects funded by the NASA Earth Science program are required to upload data collected on research campaigns to the SeaBASS data repository to increase the volume of open-access data available to the public. As of 2021 the data archive contained information from thousands of field campaigns uploaded by over 100 principal investigators.
Low-nutrient, low-chlorophyll (LNLC)regions are aquatic zones that are low in nutrients and consequently have low rate of primary production, as indicated by low chlorophyll concentrations. These regions can be described as oligotrophic, and about 75% of the world's oceans encompass LNLC regions. A majority of LNLC regions are associated with subtropical gyres but are also present in areas of the Mediterranean Sea, and some inland lakes. Physical processes limit nutrient availability in LNLC regions, which favors nutrient recycling in the photic zone and selects for smaller phytoplankton species. LNLC regions are generally not found near coasts, owing to the fact that coastal areas receive more nutrients from terrestrial sources and upwelling. In marine systems, seasonal and decadal variability of primary productivity in LNLC regions is driven in part by large-scale climatic regimes leading to important effects on the global carbon cycle and the oceanic carbon cycle.
{{cite book}}
: CS1 maint: multiple names: authors list (link)