These lessons are designed for middle or high school instruction. They
feature ocean color data, but can be easily adapted to any Earth data type.
These lessons have been presented at three sessions of the American
Meteorological Society's annual Maury Project Summer
Teacher Workshop, held at the U.S. Naval Academy in Annapolis Maryland for
the past five years. Maury participant comments on these lessons have
been very favorable. Following this year's presentation and the comments
I received , I believe these lessons are now classroom-ready, so I have
decided to make them available on the internet.
A Teacher's Guide to these lessons is also available.
A PDF
format file is also availabe (176K size).
Rebecca Farr
August, 1998
Learn how satellites 'see' the Earth
Background:
Earth-sensing satellites do not have cameras, but rather they use
electronic instruments (sensors) to measure reflected light (radiances).
These electronic instruments convert photons falling on their
light-sensitive elements into electrical signals that are assigned
numerical, or digital, values. The spacecraft converts the numerical
values into a binary data stream that is then transmitted down to receiving
stations on the ground.
A satellite usually has several sensors sensitive to different
wavelengths (also called bands) of light, allowing the satellite to detect
different colors. Computers on the ground translate the stream of numbers
from the different sensors back into their original values and combine them
to reconstruct the images. Equations are used to calculate geophysical
parameters, such as chlorophyll concentration values, from the satellite
data by comparing values from different bands.
The following activities will show you how colors are recognized and
parameters are calculated using satellite data from different color bands.
The demonstration data used here are from the Coastal Zone Color Scanner
(CZCS) instrument which operated from 1978 - 1986. For more
information about ocean color data, go to the NASA Goddard Distributed
Active Archive Center Ocean
Color Data and Resources Website.
Lesson 1. Color Recognition:
Objective:
To show how satellites use monochromatic sensors to collect data on the
color of objects on the Earth's surface.
Materials:
Slide projector
Color filters: red, green and blue.
Various colored objects (fruit, paper cutouts, color photographs,
plastic toys, etc.)
Dark room
Background:
An remote sensing satellite collects images of the Earth's surface in
various colors. The satellite is equipped with a set of detectors designed
to be sensitive to only a narrow color range, so that each detector records
only one color and together they collect all data in some color range.
A numerical value is assigned to the number of photons received by each
sensor. This number corresponds to the brightness of the light falling on
each pixel and is used to generate the image 'seen' by each sensor. The
numbers range from 0 to 255. This range yields 256 shades of grey ranging
from black (0) to white (255). The grey-scale images in each of the colors
are transmitted to Earth as a series of binary numbers, one image from each
sensor.
The Strait of Gibraltar as it looked to each of the 6 Coastal Zone Color
Scanner (CZCS) sensors, shows how some details are brighter in one color
than in others:
| Channel 1: blue |
Channel 2: green |
Channel 3: yellow |
| Channel 4: red |
Channel 5: far red |
Channel 6: infra-red |
This activity demonstrates the color imaging process used by remote
sensing Earth satellites. By examining various objects in red, green, and
then blue light, you will note that the brightness varies with the
illuminating wavelengths. Using colored light is equivalent to observing
the objects through colored filters or using narrow band sensors. The way
each object appears relates to its "real" colors as seen in normal light.
By noting subtle differences in the brightness of known colors in each
of the three colored lights, the unknown colors of other objects can be
deduced.
Exercise:
The instructor will give you a closed box of toys. Do not look at them
until the room has been darkened.
1. Darken the classroom and cover the lens of the slide projector with
the red filter. Turn on the slide projector light.
2. Open the box and place the toys in a row. Compare their relative
brightness. Compared to each other, do they look lighter or darker in the
red light?
3. Place the toys in order of relative lightness, from lightest to
darkest in the red colored light. Translate your observations into bar
chart histograms in the table below, (brightest: value =5; darkest:
value=1).
4. Turn off the light and replace the red filter with the green. Turn
on the projector light and repeat with the same objects. Repeat again, but
this time use the blue filter. Record your rankings in the table below.
Complete your histogram bar charts for the three bands.
5. Turn on the room lights and verify the actual colors of the objects.
6. Try to identify the colors of 5 new objects. You will not be able to
guess their color based on their shape. Look at the unknown objects in the
red, green, and then blue lights. Create histograms for these unknown
objects and compare to the histograms of the known colors to deduce the
actual colors of the unknown objects:
7. Turn on the room lights and verify the actual colors of the unknown
objects.
Were you right? Why or why not?
-
-
-
-
For Further Research:
- Take home pieces of colored acetate and look through them at a
variety of colored objects. How does a show on your color television look
through the filters?
-
-
-
-
- Is anyone in your family color-blind? Interview them and show them
several colored objects. Write down how they describe the colors.
-
-
-
-
Lesson 2. Calculating Chlorophyll Concentration:
Objectives:
To calculate chlorophyll values by comparing data from two different
color bands.
Materials:
Table of values
Calculator
Colored pencils
Background:
Because images collected by spacecraft are digital, scientists can use
computers to manipulate them and to do calculations using the data. This
manipulation is roughly analogous to the adjustment of color, brightness,
and contrast controls on a television set. Data collected from different
sensors can be combined in an equation to calculate specific values, such
as chlorophyll concentration in the sea or dust content of the air. You
were employing this principle in Lesson 1A by relating your observations of
the brightness of various objects in three different colors to their actual
color.
To measure ocean color from space, the blue and green images of a scene
are compared to calculate chlorophyll values. This is done by substituting
pixel values of a scene collected in the two colors into an equation, or
algorithm. Algorithms are used to translate pixel values into real
geophysical parameters, such as "milligrams ofchlorophyll per cubic meter".
This is an example of a simple equation used to calculate chlorophyll
concentration values by relating the blue and the green values for each
pixel:
This is called an empirical equation because it was derived from
measurements matching ocean color seen from a satellite with actual amounts
of chlorphyll pigment measured in water samples collected by a ship at the
same place and time. The numbers "1.1298" and "1.71" are put into the
equation so that it will give the correct answer for places where there is
no ship data.
Once a chlorophyll amount has been calculated for each pixel, a color
scale is assigned to cover the range of grey values and the image is
redrawn to look like this:
Now each pixel indicates a certain concentration of chlorophyll, and we
can begin to study what the data are telling us about the oceanography and
the biology of the area shown in the scene. For the CZCS color scale shown
here, chlorophyll pigment concentrations were assigned as follows:
| Greyscale |
Value |
Color |
Pigment Concentration (mg/m3) |
| 1. |
200-252 |
bright red |
>10.00 |
| 2. |
156-199 |
dark red |
3.00-10.00 |
| 3. |
131-144 |
orange-brown |
1.50-2.99 |
| 4. |
117-130 |
orange |
1.00-1.49 |
| 5. |
116-116 |
gold |
0.90-0.99 |
| 6. |
109-112 |
yellow-gold |
0.80-0.89 |
| 7. |
104-108 |
yellow |
0.70-0.79 |
| 8. |
98-103 |
yellow-green |
0.60-0.69 |
| 9. |
92-97 |
light green |
0.50-0.59 |
| 10. |
88-91 |
green |
0.45-0.49 |
| 11. |
84-87 |
green-blue |
0.40-0.44 |
| 12. |
79-83 |
light blue-green |
0.35-0.39 |
| 13. |
73-78 |
blue-green |
0.30-0.34 |
| 14. |
66-72 |
lightest blue |
0.25-0.29 |
| 15. |
58-65 |
light blue |
0.20-0.24 |
| 16. |
48-57 |
grey-blue |
0.15-0.19 |
| 17. |
33-47 |
blue |
0.10-0.14 |
| 18. |
23-32 |
blue-purple |
0.08-0.09 |
| 19. |
8-22 |
purple |
0.05-0.07 |
| 20. |
1-7 |
lavender |
< 0.05 |
Execise:
1. The table below contains pixel value ratios from an actual CZCS
scene. The ratio is calculated by dividing the blue channel pixel value by
the green channel pixel value, B/G. These ratios values are dimensionless.
Pixel Ratio Values (blue pixel value/green pixel value)
.7237
.5606
.5987
.5987
.5606
.6564
.5606
.4782
.5606
.6564
.5987
.7237
.5987
.5206
.4782
.5606
.5206
.4782
.5206
.4782
.5987
.5606
.5606
.5606
.4782
.5606
.5206
.5206
.4782
.4330
.5606
.5606
.5606
.5606
.5606
.4782
.5206
.5606
.4330
.4330
.5987
.5606
.5206
.4782
.4782
.4782
.4782
.4330
.4330
.4330
.5206
.4782
.5606
.4782
.4782
.4330
.4330
.4330
.4330
.4330
.5206
.5606
.4782
.4782
.4330
.4330
.4330
.3841
.3841
.3841
.4782
.5606
.4782
.4330
.4782
.3841
.3841
.4330
.4330
.4782
.5206
.5206
.4782
.3841
.3841
.3841
.4782
.4330
.4330
.4782
.4782
.5206
.4782
.4330
.3841
.3841
.3841
.4330
.3841
.3841
2. Plug the pixel ratio values from the table above into this equation
to calculate a chlorophyll value for each pixel in the image.
Write the calculated chlorophyll values for each pixel in this table:
Chlorophyll concentration (mg/m3)
3. For each chlorophyll concentration value, fill in the appropriate
color in the corresponding box on the grid below. (Each box contains a
number corresponding to the 20 color scale above. Your calculated
chlorophyll concentration values should fall within the range of that
color.)
Ocean Color Image
4. WOW! That was a lot of work! Can you recognize anything in your
ocean color image?
-
-
-
Why or why not?
-
-
-
-
-
-
Author and Responsible NOAA official:
Rebecca Farr
Ground Data Operations Manager
NOAA NESDIS
Posted August, 7 1998
Privacy Policy and Important Statements
