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United States Department of Agriculture

Agricultural Research Service

1 - Introduction
2 - Data Sources
3 - Signal-to-Noise
4 - Imaging Theory
5 - Data Quality
6 - Spectral Bands
7 - Conclusions
8 - Resources
9 - Disclaimer
10 - Acknowledgements
Spectral Bands
Remote Sensing Basics
 

I. Sources of Imagery

II. Signal-to-Noise Ratio (S/N)

III. Imaging Theory: What to Expect

IV. Simple Tools for Checking Image Data Quality

V. Choice of Spectral Bands

VI. Final Thoughts and Suggestions

VII. Resources and References

Disclaimer

Acknowledgements

 

V. Choice of Spectral Bands

There is a rich heritage of knowledge from over 20 years of electro-optical remote sensing research. Draw upon this and use it as a starting point when approaching a data set. Fundamental relationships between specific spectral bands and plants and soils are well documented. Look at the relationships between red and NIR and various plant characteristics. Then try NDVI. When specifying spectral bands for analysis, channels with band centers close to the band centers of Landsat ETM+ are a good choice:

Band Number Spectral Range (nm)
1 450 to 515
2 525 to 605
3 630 to 690
4 750 to 900
5 1550 to 1750
6 1040 to 1250
7 2090 to 2350
Panchromatic 520 to 900

Terra satellite MODIS or the EO-1 satellite Hyperion instrument spectral band configurations are also reasonable choices.

Note the potential pitfalls of specifying narrow spectral bandwidths below 500 nm and above 1000 nm as explained in the section on Silicon Detector Sensitivity. Another factor to consider is the bandwidth of the system. The AISA, like many low-cost airborne scanner systems are "band-width limited", meaning that only so much data can be handled at a time. This forces users to balance the need for many spectral bands with the need for high spatial resolution ground instantaneous field of view (GIFOV). As the GIFOV increases, the number of available spectral bands will decrease.

HYPErspectral or Multispectral?

Despite over 20 years of electro-optical multispectral and hyperspectral image research, NDVI is still the most widely used approach for analysis. Hyperspectral exploitation has lagged, presumably because of the lack of widespread data availability to the remote sensing research community, and because of a lack of pressing scientific justification. One can better understand this by addressing why the HIRES instrument was deleted from the NASA EOS/Terra satellite. Thus, spectral vegetation index (SVI) approaches are still very appropriate.

Land cover classification accuracies have been shown to improve from the use of hyperspectral imagery. Recent work on the value of improved sensitivity of narrow spectral bandwidths to vegetation characteristics when using SVI analysis is further evidence of the potential of hyperspectral imaging. Red Edge analysis also relies on hyperspectral data as does spectral unmixing.


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Last Modified: 8/18/2010
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