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How Can Spectral Reflectance Be Used in Remote Sensing?
the spectral reflectance—the percentage of total energy per wavelength that
was reflected off a target—which makes its way toward the sensor and is what
is being utilized with remote sensing.
How Can Spectral Reflectance Be Used
in Remote Sensing?
All items on Earth’s surface reflect energy wavelengths differently. For
instance, green grass, bare soil, a parking lot, a sandy beach, and a large lake
all reflect portions of the visible, near-infrared, and middle-infrared energy
differently (these items emit thermal energy differently as well). In addition,
some things reflect energy differently depending on different conditions. As
an example of examining these remote sensing concepts, let’s look at something simple—measuring reflectance of energy from the leaves on a tree.
During late spring and summer, a tree canopy will be fuller, as the leaves stay
on the trees and are a healthy green color. The human eye sees healthy leaves
as green because the chlorophyll in the leaves is reflecting the green portion
of the spectrum and absorbing the red and blue portions. What can’t be seen
by the human eye (but can be seen by remote sensing instruments) is that
healthy leaves also very strongly reflect near-infrared energy. Thus, measurement of near-infrared energy is often used (in part) as an indicator about the
relative health of leaves and vegetation.
However, leaves aren’t always going to stay green. As autumn progresses,
leaves go through a senescence process in which they lose their chlorophyll.
As this happens, the leaves reflect less green energy and begin to absorb more
red and blue energy, causing the leaves to appear in other colors, such as
yellow, orange, and red. As the leaves fall off the trees and turn brown, they
have a lot more red reflection, causing their brownish appearance (and there
will also be less reflectance of near-infrared energy from the tree).
If a sensor is able to measure all of these energy wavelengths simultaneously for different objects, it would give a person examining the data the
capability to tell objects apart by examining their reflectance values in all of
these wavelengths. If you were to chart the spectral reflectance values against
the wavelengths being measured for each item, you would find that each of
these things would have a different line on the chart. This is referred to as an
item’s spectral signature (also called a spectral reflectance curve), as each set
of charted measurements will be unique to that item, just like your handwritten signature on a piece of paper is different from other people’s signatures.
Remote sensing analysts can use these spectral signatures to distinguish items
in an image or use them to tell the difference between different types of plants
or minerals (see Figure 10.5 on page 304 for an example of various spectral
signatures compared to one another).
An application of remotely sensed imagery is its use in assessing the health
of green vegetation (such as fields, grass, and leaves on trees). When vegetation is very healthy, it will have a strong reflection of near-infrared energy and
spectral reflectance
the percentage of the
total incident energy
that was reflected from
that surface.
spectral signature a
unique identifier for
a particular item,
generated by charting
the percentage of
reflected energy per
wavelength against
a value for that
wavelength.
How Can Spectral Reflectance Be Used in Remote Sensing?
the spectral reflectance—the percentage of total energy per wavelength that
was reflected off a target—which makes its way toward the sensor and is what
is being utilized with remote sensing.
How Can Spectral Reflectance Be Used
in Remote Sensing?
All items on Earth’s surface reflect energy wavelengths differently. For
instance, green grass, bare soil, a parking lot, a sandy beach, and a large lake
all reflect portions of the visible, near-infrared, and middle-infrared energy
differently (these items emit thermal energy differently as well). In addition,
some things reflect energy differently depending on different conditions. As
an example of examining these remote sensing concepts, let’s look at something simple—measuring reflectance of energy from the leaves on a tree.
During late spring and summer, a tree canopy will be fuller, as the leaves stay
on the trees and are a healthy green color. The human eye sees healthy leaves
as green because the chlorophyll in the leaves is reflecting the green portion
of the spectrum and absorbing the red and blue portions. What can’t be seen
by the human eye (but can be seen by remote sensing instruments) is that
healthy leaves also very strongly reflect near-infrared energy. Thus, measurement of near-infrared energy is often used (in part) as an indicator about the
relative health of leaves and vegetation.
However, leaves aren’t always going to stay green. As autumn progresses,
leaves go through a senescence process in which they lose their chlorophyll.
As this happens, the leaves reflect less green energy and begin to absorb more
red and blue energy, causing the leaves to appear in other colors, such as
yellow, orange, and red. As the leaves fall off the trees and turn brown, they
have a lot more red reflection, causing their brownish appearance (and there
will also be less reflectance of near-infrared energy from the tree).
If a sensor is able to measure all of these energy wavelengths simultaneously for different objects, it would give a person examining the data the
capability to tell objects apart by examining their reflectance values in all of
these wavelengths. If you were to chart the spectral reflectance values against
the wavelengths being measured for each item, you would find that each of
these things would have a different line on the chart. This is referred to as an
item’s spectral signature (also called a spectral reflectance curve), as each set
of charted measurements will be unique to that item, just like your handwritten signature on a piece of paper is different from other people’s signatures.
Remote sensing analysts can use these spectral signatures to distinguish items
in an image or use them to tell the difference between different types of plants
or minerals (see Figure 10.5 on page 304 for an example of various spectral
signatures compared to one another).
An application of remotely sensed imagery is its use in assessing the health
of green vegetation (such as fields, grass, and leaves on trees). When vegetation is very healthy, it will have a strong reflection of near-infrared energy and
spectral reflectance
the percentage of the
total incident energy
that was reflected from
that surface.
spectral signature a
unique identifier for
a particular item,
generated by charting
the percentage of
reflected energy per
wavelength against
a value for that
wavelength.