Spectral Signature Concepts

Lab Exercise #4:  Spectral Signature Concepts


To provide exposure to: (1) acquisition of spectral radiometric data, (2) calibration to radiance values, (3) spectral signature interpretation, and (4) visualization of results in graphic and tabular formats. This will enable you to gain an awareness of several fundamental aspects of reflected radiation.



  • Data values previously collected with an Exotech 4 band radiometer with filters for Landsat Thematic Mapper bands 1 (blue), 2 (green), 3 (red) and 4 (NIR).
  • Spreadsheet program to perform calculations and construct graphs, or:
  • A basic calculator, data summary tables and graphing paper


Email your completed word document and excel spreadsheet to: kwarkentin@sdsu.edu with your names and lab 4 in the subject line.


A user may be able to choose the proper bands and filters for a given remote sensing task by  collecting and analyzing reflectance data for various targets, which have been or are to be imaged in a given scene, and then by identifying spectral bands which maximize class separability. Theoretically, the greater contrast in reflectance between two imaged objects, the easier it should be to distinguish between them. The easier objects are to distinguish, the greater is the potential for fast, accurate interpretation.


            Table 1.  Landsat Thematic Mapper & Exotech Radiometer Bands

Band           Spectral Bands (mm)   Exotech bands in bold

1                      0.45 – 0.52                   blue

                        2                      0.52 -0.60                    green

                        3                      0.63 -0 .69                   red

                        4                      0.76 -0 .90                   near IR

5                      1.55 – 1.75                   mid IR

7                      2.08 – 2.35                   mid IR

6                      10.4 – 12.5                   thermal


A four channel Exotech radiometer was used several years ago to collect raw spectral radiometric measurements for general surface material types around campus. The radiometer measures the radiation reflected from the surface as a voltage. You will be provided with radiant exitance values using the calibration data in Table 2.

The voltages recorded on the data sheets for each band of each target were multiplied by the calibration for the band, field-of-view (FOV) and sensor gains used in the conversion program.


                        Radiant exitance (Wm-2) = Voltage * Calibration factor * 10exponent

Calculate radiant exitance and enter below for each target.  A 15 degree FOV was used for all measurements.  Average the beginning and ending panel radiant exitance.

You may choose to do all table-based calculations in the Excel spreadsheet which is provided as part of this exercise, following the general workflow described here.


Table 2: Radiant Exitance (Wm-2)

Target Band 1 Band 2 Band 3 Band 4
average panel 140 152 95 154
asphalt 22 35 12 45
concrete 93 86 43 35
grass 6 11 5 103
shrub 1 3 1 72
soil 3 7 65 39
shaded grass 2 5 2 26
shaded concrete 49 21 13 8


The halon coated panel used to measure the incident radiation at the surface is not a perfect, i.e., neither 100% nor an isotropic (Lambertian) reflector. Therefore, an adjustment must be applied to the panel measurements.

The average panel radiant exitance that you calculated above must be divided by a factor taken from Table 3 below for the time that most closely matches when your observations were taken (around 2:30 PM).

The magnitude of the adjustment is dependent on the solar zenith angle which is a function of the time of day, day of the year and latitude. For your calculations, use the 2:30 PM adjustment factors.

                                                Table 3 – Halon Panel Adjustment Factors

Time (PDT)              Band 1            Band 2            Band 3            Band 4

2:00                 .935458           .939679           .934222           .919215

2:15                 .932881           .937191           .931803           .916819

2:30                 .92994             .934368           .929077           .914118

2:45                 .926623           .931202           .926039           .91111

3:00                 .922896           .927663           .922662           .907772

3:15                 .918706           .923701           .918895           .90406

3:30                 .913986           .919248           .91467             .899914

3:45                 .908651           .914222           .909903           .895261

4:00                 .902605           .90853             .904497           .890013


Enter the adjusted panel values below (you can use the excel spreadsheet for the calculations) :

Avg. Adjusted Panel Values = Average panel radiant exitance ¸ panel adjustment

(Divide the average panel radiant exitance by the correct panel adjustment factors)


  Band 1 Band 2 Band 3 Band 4
Average Adjusted Panel Values        


Now, calculate the spectral reflectance factor for each band of the seven targets (numbers and calculations can be stored and submitted in the spreadsheet). The spectral reflectance factor is the target radiant exitance divided by the adjusted average panel radiant exitance. Express the reflectance as a decimal fraction out to four significant digits beyond the decimal point.


Reflectance  = Target  exitance (Wm-2) ¸ Adj. avg. panel exitance (Wm-2)



Material   Band 1   Band 2   Band 3   Band 4
Shaded Grass        
Shaded Concrete        





  1. Plot reflectance for each target as a point with the bands on the X axis. Connect the points for each band. Create one graph for each target, and a final graph depicting all of the targets on one graph (you should create 8 graphs). You may find the graph more interpretable if you don’t use point markers, such that each target is depicted as a line. For the final graph, construct a colored bar graph, with bands 1-4 on the X-axis, and values for each target on the Y-axis, color-labelled by material type. Use colors that are reasonable for each target. Include axis titles on each graph.


Save the graphs in the excel spreadsheet. You do not need to copy them to the word doc.


  1. Using the matrix below, determine the bands of electromagnetic spectrum which allow the optimum discrimination of any one target from another (fill in the best 2 bands). (Use sunlit targets, i.e., not shaded, only for this analysis.) Only fill in the white boxes.
Optimum band selection for discriminating between target pairwise combinations.
  Asphalt Concrete Grass Shrub Soil


  1. Select the bands which are optimum for discriminating among the three general classes of vegetation, asphalt, and concrete/soil (fill in the best band or combination of bands in the blanks in the matrix). Only fill in the white boxes. Discuss the rationale for your decisions.


Optimum band(s) selection for discriminating between three general classes of targets
  Vegetation Asphalt Concrete/Soil




  1. Discuss the reflectance calculated for the two shaded targets. How do the shaded reflectance compare to one another and to the sunlit targets of the same material types?







  1. Why is it important to investigate the nature of wavelength reflectance from targets when planning remote sensing missions?







  1. Describe the utility of “multi-spectral” approaches for separating land cover types, i.e., potential advantages of using more than two spectral bands?





Last Updated on September 27, 2018 by EssayPro