Chapter 11: On the Level From Radiation to Scientific Imagery
The concept of using a sensor, mounted on a satellite, to observe the
Earth - called "space-based" or "orbital" remote sensing to
distinguish it from other types of remote sensing - seems simple enough.
These sensors consist of two principal components:
a detector component which receives radiation from the Earth, and a data
processing component which translates detected radiation into digital data.
The digital data can then be transmitted from the satellite to a
receiving station on Earth.
CZCS Image of Baja California, November 12, 1981
This image was created by combining several different wavelengths of light using an algorithm developed
to determine chlorophyll levels in ocean water.
If that introduction makes the concept sound uncomplicated, it must be
noted that the actual process requires advanced technology to convert
the "raw" signal transmitted by the satellite into processed data and images
that are meaningful and accurate. "Meaningful" implies that the data and
images are realistic and interpretable, while "accurate" means that real
scientific information can be gained from analysis of this information.
Insuring that the data is both meaningful and accurate is the primary
challenge of space-based remote sensing.
This challenge has already been successfully met by
numerous types of satellites and sensors. However, scientists and engineers
are always striving to make the data more meaningful and more accurate. By
improving data accuracy and content, the data can be employed in analyses and
modeling that will in turn become more precise.
The goal of this discussion is to explain the basics of orbital
radiometry, paying particular attention to ocean color radiometry. In
the course of the discussion, the difficulties inherent in the
process, as well as the advantages and disadvantages of
this type of remote sensing, should be evident. Radiometry means the
measurement of emitted radiation in specific ranges of the electromagnetic
spectrum.
Starting at the beginning means starting at the Sun. The type of
remote sensing discussed here detects visible light (VIS) or infrared thermal
radiation (IR) being emitted from the surface of the Earth. The actual surface
of the Earth consists of numerous different surfaces, all of which can
interact with solar radiation. In some instances, solar radiation will be
absorbed by a surface. Another type of interaction is reflection, where the
incoming radiation is virtually identical to the outgoing radiation. In most
cases, however, the radiation interacts with a surface such that modified
radiation will be emitted from the surface. (Note: the general term for this
type of remote sensing is passive remote sensing, to distinguish it
from active remote sensing, where a signal is beamed from a satellite
at the Earth and the reflected signal is detected.)
As an example, consider the leaves of a tree. Leaves contain
chlorophyll, a pigment that uses light energy to produce carbon (the process
of photosynthesis). The most common form of chlorophyll (chlorophyll
a) absorbs in the blue and red regions of the visible spectrum and
reflects in the green, so that the leaves of most trees appear green. Light
from the Sun that hits the surface of a green leaf will be modified; the red
and blue wavelengths are absorbed and the green wavelengths are reflected.
In the case of the thermal region of the spectrum, light may be
absorbed by a surface and then radiated from the surface as heat, which is
radiation in the far infrared portion of the electromagnetic spectrum.
Detection of IR radiation can therefore be used to indicate the temperature of
a surface. Variations in temperature can be mapped, which can provide more
information. The temperature of the sea surface is variable, and this
variability can provide information on current patterns, so maps of sea
surface temperature are used by oceanographers to observe ocean currents
(when clouds aren't in the way, of course).
Quite a bit happens to solar radiation when it enters the atmosphere
and then impinges on the surface of the Earth. The next few paragraphs describe
various paths of photons from the Sun within the Earth's environment.
The main reason to refer to photons in this discussion is due to
the fact that the detector systems of remote sensing instruments detect photons
of various energies. What will be done here is to follow the possible paths
taken by various photons from the Sun, to and from the Earth, and eventually
to the detector of a satellite instrument. This diagram illustrates many
of these possible paths.
In this figure, several different light pathways in the atmosphere
are illustrated: a) The light path of the water-leaving radiance. b) Shows the attenuation
of the water-leaving radiance. c) Scattering of the water-leaving radiance
out of the sensor's FOV. d) Sun glint (reflection from the water surface).
e) Sky glint (scattered light reflecting from the surface. f) Scattering of
reflected light out of the sensor's FOV. g) Reflected light is also
attenuated towards the sensor. h) Scattered light from the sun which
is directed toward the sensor. i) Light which has already been scattered by
the atmosphere which is then scattered toward the sensor. j) Water-leaving
radiance originating out of the sensor FOV, but scattered toward the sensor.
k) Surface reflection out of the sensor FOV which is then scattered toward the
sensor. Lw Total water-leaving radiance. Lr Radiance
above the sea surface due to all surface reflection effects
within the IFOV. Lp Atmospheric path radiance.
(This figure is adapted from Robinson, I.S., 1983: Satellite
observations of ocean colour, Philo. Trans. Royal Soc. of London, Series
A, Volume 309, 338-347.)
The simplest path a photon can take is the most direct one. The photon
enters the atmosphere, hits a surface, is reflected, and bounces right
back out into space, where it encounters the detector of a remote-sensing
instrument. For ocean color remote sensing, this path presents a problem.
Imagine the way that sunlight sparkles on the surface of water on a lake.
Those sparkles are the direct reflections of light from the Sun into
your eyes. Though such reflections are pretty, reflected light doesn't
provide any information on what is actually in the water. When there is
too much direct reflection, no information on what is in the water can be
derived. For that reason, areas with too much reflection (called sun
glint) are masked out of the data. Most ocean color sensors are designed
to be tilted so that fewer directly-reflected photons will find their way to
the detector.
The next path is the main one of interest to science. A photon enters
the atmosphere, encounters a surface, is modified in some way, and then is
radiated up to the detector in space. The example of a tree leaf was already
given above. In water, photons enter the ocean, and some wavelengths are
absorbed while others reflect off particles suspended in the water. The
most important path in the ocean is the absorption of specific
wavelengths of light by the chlorophyll present in phytoplankton cells, so
that the remaining radiation is an indication of how much light was absorbed.
The net result is that a only small percentage of the light that enters the
water (the downwelling irradiance) is redirected back toward the
surface (the upwelling radiance). If the upwelling radiance actually
leaves the surface and heads toward space - even though all of it doesn't get
there - it is termed the water-leaving radiance. Water-leaving
radiance is what ocean color sensors are specifically designed to measure.
Those two paths are fairly direct. However, quite a bit of the
light that enters the atmosphere and ocean is scattered, because it
interacts with air molecules, dust and other substances suspended in the
atmosphere, or substances and particles in the ocean. Light scattering
(particularly the preferential scattering of higher frequency light) is what
causes the light blue color of the sky, or the intense blue of very clear,
deep water. There are numerous scattering paths that photons can take. The
path a photon takes before encountering a surface isn't important;
the important path is the one it follows after leaving the surface.
For example, many of the water-leaving radiance photons will be scattered by
the atmosphere and never make it to the detector on the satellite. Many more
photons won't even reach the ocean, but will be scattered by the atmosphere
back to the detector (or they will reflect off clouds). Aerosols or haze in
the atmosphere will also partially interfere with the photons radiating
toward the detector, perhaps modifying their wavelength by absorption and
re-radiation, or by scattering the light even more.
The net result of all these interactions is that for an orbital sensor
aimed directly at the ocean, about 10% of the total light it detects is
water-leaving radiance. The other 90% of the light is due to atmospheric
effects. (Since land is more reflective than the ocean, a
sensor aimed at land receives a greater percentage of light from land
surfaces and a lesser percentage from the atmosphere.) Corrections must be
applied to the data to remove this atmospheric radiance, allowing accurate
measurement of the amount and color of light exiting the ocean surface.
This is where data processing comes in. An optical model of the atmosphere
above the ocean can be formulated, using such inputs as the surface pressure
and the transmission of light through the atmosphere at certain wavelengths.
Using this optical model, the radiance the satellite "sees" can be
corrected for the influence of the atmosphere, theoretically leaving only the
water-leaving radiance!
In order for remote-sensing data to be useful, the data is processed
through several "levels". The definitions of the data levels were agreed upon
by the National Academy of Sciences Committee on Data Management, Archiving,
and Computing (CODMAC). For precisely worded definitions, read
EOS Data Product Levels.
(Note that the definitions are not specific to the Earth Observing System (EOS),
but are applicable to all types of remote sensing data.)
However, before there has been any data processing, the data is termed
raw data. Raw data simply consists of the electronic signal that is
produced when photons of light are detected by the instrument. Depending on
how the instrument looks at the Earth (which is determined by the way the
instrument works), the signals are assigned to picture elements, or
pixels, the basic pieces of a remote sensing image.
The first level above raw data is level 0. Navigational data and
other relevant information from the satellite are assigned to the detected
signal. This data insures that the corresponding region on Earth that was
being scanned from space is known. The electronic signal has not yet been
converted to measured radiances.
Level 1 Data 700-800 nm
Level 2 Data 440 nm
Level 2 Data 520 nm
Level 2 Data 550 nm
Level 2 Data 670 nm
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To produce level 1 data, the electronic signal from the detector is
converted to radiances measured at the satellite, and information from
the satellite's onboard calibration routine is added to the data. There are
many different ways to maintain the accurate calibration of a satellite
instrument. One of the most common calibration methods has the sensor scan a
"source" possessing a known, consistent radiance. The Sea-viewing Wide
Field-of-view Sensor (SeaWiFS), NASA's ocean color instrument, scans a solar
(sunlight) diffuser possessing a known radiance, and the sensor will also do a
lunar calibration by viewing the Moon when it is at a certain phase.
(SeaWiFS, like most other remote-sensing instruments, was accurately
calibrated before launch. Onboard calibration methods such as these strive to
insure that the calibration of the instrument is known throughout the mission.)
Once the radiances are determined, the navigation data can be used to generate
an image. However, more information must be used to make this image relevant
to conditions at the surface of the Earth.
The conversion from level 1 data to level 2 products applies
sensor calibration data and atmospheric correction to calculate Earth surface
radiances from the radiances measured at the satellite. The ongoing
calibration routines ensure that the radiances will always represent the same
absolute radiance, despite possible changes in the optical system of the
instrument. Other checks on the quality of the data will
also be applied here. Based on the radiances measured at the satellite,
masks indicating the presence of clouds, land, and
perhaps sea ice will be added to the data stream. Flags may also be
added to indicate unusual conditions or anomalous data.
Several different kinds of data are used to derive the most accurate
geophysical parameters. Data from sources other than the satellite itself
is termed ancilllary data. For ocean color, examples of ancillary data
are wind speed (used to calculate sun glint masks and the presence of
whitecaps), ozone (used for atmospheric correction, as ozone absorbs some
light), and atmospheric pressure.
Once the surface radiances are calculated, new analytical routines can
be applied that convert this information into different types of geophysical
parameters, or products. For land surfaces, the radiances may indicate
the different types of surfaces or the amount of land covered by vegetation.
For ocean color, the radiances can be used to calculate the concentration of
chlorophyll in the water, or the amount of suspended sediments. Thus, level 2
data includes both Earth surface radiances and calculated geophysical
parameters.
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The conversion of radiances to geophysical products employs algorithms
developed by painstaking research. Highly accurate measurements
of radiation are made, using either radiometers or spectrometers, to
characterize the radiative signature of a particular environment. For ocean
color data, such radiometers must be immersed in the open ocean, and they will
measure both the incoming (downwelling) and outgoing (upwelling) radiation.
The instruments measure the variability of light at
many different wavelengths (particularly those wavelengths that the sensor in
space has been designed to measure). At the same time, samples of the
environment, which may mean vegetation or soil on land, or water
samples from the ocean, are examined. In seawater, the concentrations of
phytoplankton and their chlorophyll will be analyzed, and these concentrations
will then be correlated with the measured radiances. As these measurements
are made, researchers hope to find consistent relationships between the
radiances and the surface variables that are being measured, which will allow
them to construct an algorithm. The algorithm will calculate a
specific variable, such as chlorophyll concentration, based solely on the
radiance data. Satellite data is then used in these algorithms to calculate
the geophysical parameters over large areas of the Earth.
This is definitely a complex process, and it may be difficult to
visualize. Now examine this picture, which was taken from the Space Shuttle
along the northeastern coast of Brazil, north of the Amazon River delta. The
ocean currents in this region carry the muddy water of the Amazon along the
coast, and it can be easily distinguished from the clear blue water of the
ocean. It's obvious that the brownish water along the
coast has much different optical characteristics than the clear blue water
further out to sea. The goal of ocean color remote sensing algorithms is to
distinguish different types of water, and the constituents that determine a
particular color. Ideally, a useful algorithm would calculate the
concentration of suspended particulates in the muddy water, and the
concentration of chlorophyll in both turbid and clear water. [By the way,
oceanographers use the term Case 1 water for clear ocean water. Coastal
waters that may range from reddish-brown (sediments) to green
(phytoplankton chlorophyll), and numerous hues in between, are termed Case 2
waters.]
Now picture a forest composed of only one species of tree, or a part
of the ocean with one kind of phytoplankton. Both of these environments will
have a fairly uniform color. In these cases, a fairly simple relationship
exists between the color that the satellite observes and the density of either
trees or phytoplankton. Ratios of light intensity detected at
various wavelengths of the VIS/IR spectrum have been used in algorithms
to calculate vegetation density or chlorophyll concentration. However, if
you have an area with many different types of plants and soils, or water
with different species of phytoplankton as well as sediments, it
becomes much more difficult to find simple relationships between
optical properties and geophysical characteristics. However, that's still
the goal of algorithms which calculate level 2 geophysical products.
One other aspect of this topic is the fact that instruments in
space tend to change over time, and they usually can't be taken back to
the laboratory to be re-calibrated. Unfortunately, algorithms such as those
described above rely on very accurate measurements of radiance. So
another challenge of remote sensing is to make sure that the calibration
of the instrument is known to a very high level of precision. As mentioned
previously, several different ways of calibrating these sensors while the
satellite is in space have been devised. However, these methods don't always
work, and scientists have been forced to come up with clever ways to maintain
the quality of the data. The next paragraph describes one such situation.
The Coastal Zone Color Scanner (CZCS) carried lamps that were intended
for use as onboard radiance sources for calibration. However, the reliability
of these lamps became questionable. For that reason, and also due to
degradation in the sensitivity of the detectors in the CZCS over time,
scientists devised a way to calibrate the instrument based on the data it was
receiving. Their method relied on the fact that the amount of light leaving an
area of the ocean with very clear water is fairly constant. By knowing this
amount of light, the "clear-water radiance" of a certain pixel was used as
the reference for all of the other pixels composing a given CZCS image. Even
though this method is imperfect, partly because it relied on the "clearest"
water pixel in a given image, it was employed to produce a consistent data set
from all the data collected in the eight years of CZCS operation.
It should now be clear that utilizing photons observed by a satellite
sensor and converting them to meaningful geophysical information is a fairly
complex operation. That's why so much discussion was devoted to level 2
data. However, there is one more data level - level 3. Level 3 data is
accumulated data, collected according to the corresponding location on the
surface of the Earth. The Earth is divided up into cells on a grid, which are
called bins. All of the data for a grid cell collected daily, weekly,
monthly, or yearly is put into a bin. Collection of data in this systematic
manner allows the data to be treated statistically, and also allows data from
certain regions to be grouped together. A distinction is frequently made
between spatial bins and temporal bins, which means that data can be organized
according to either where the data was received from, or the time interval
during which it was collected.
So now you know the following about orbital remote sensing of the
Earth:
- what you see isn't necessarily what you want to know; and
- getting from what you see to what you want to know requires a
lot of careful work in between.
Chapter 12: Plankton Blooms
Chapter 10: River Plumes and Estuaries
Index: Classic CZCS scenes
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Atmos Composition
