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United States Department of Agriculture

Agricultural Research Service

Remote Sensing
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1 - Introduction
2 - Calibration and Signal Processing
3 - Algorithm and Model Development
4 - Field Experiments and Inter-Disciplinary Programs
5 - Data Archive
Calibration and Signal Processing

Sensor calibration

Reflectance factor retrieval

Bidirectional reflectance
distribution function

Sensor calibration

 Sensor calibration

University of Arizona scientists making atmospheric measurements with a solar radiometer (upper) and ARS scientist making surface reflectance measurements (lower) in support of satellite sensor calibrations at White Sands, New Mexico

Arizona ARS scientists have conducted in-flight calibrations of airplane and satellite-based sensors in cooperation with scientists from the University Of Arizona Optical Sciences Center. The results of over ten years of work have shown that:

  1. over time, sensor sensitivity degrades up to 27%
  2. sensor degradation depends on type and filter
  3. in-flight sensor calibration uncertainty is less than 5%, and
  4. regular calibration result in high-quality images

These results show that in-flight sensor calibration is both accurate and essential for proper interpretation of remote sensing images for monitoring temporal changes in crop and soil conditions.

Suggested publication:

Slater, P. N., S. F. Biggar, R. G. Holm, R. D. Jackson, Y. Mao, M. S. Moran, J. M. Palmer and B. Yuan. Reflectance-and radiance-based methods for the in-flight absolute calibration of multispectral sensors, Rem. Sens. Environ. 22:11-37. Published 1987.


Reflectance factor retrieval
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 Reflectance illustration

An illustration of atmospheric attenuation of a solar ray passing through the atmosphere to the earth, reflecting off the earth's surface, and returning through the atmosphere to the satellite sensor

Remote sensing products becomes infinitely more valuable if the digital numbers (dn) can be converted to a value that is independent of atmospheric and insulation variations, and is thus comparable over time for monitoring seasonal crop and soil conditions. The reflectance factor (the ratio of reflected and incident radiation at the surface) is a such a value, and has become the basic measurement required for most remote sensing algorithms and models. Retrieval of surface reflectance from image radiance can be accomplished through complicated measurements of atmospheric conditions, and modeling of atmospheric radiative transfer. ARS scientists have also explored the use of canvas reference tarps that could be deployed during each overpass and on-farm targets such as landing strips or dirt roads that could be used to normalize the images to a common reference.

Suggested publications:

Moran, M.S., R.D. Jackson, T.R. Clarke, J. Qi, F. Cabot, K.J. Thome and B.N. Markham, Reflectance factor retrieval from Landsat TM and SPOT HRV data for bright and dark targets, Rem. Sens. Env. 52:218-230. 1995.

Moran, M.S., T.R. Clarke, J. Qi and P.J. Pinter Jr., MADMAC: A test of multispectral airborne imagery as a farm management tool, Proc. 26th Intl. Symp. on Rem. Sens. Environ. Vancouver, B.C. Canada, 25-29 March. p. 612-617. 1996.


Bidirectional reflectance distribution function
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An illustration of the shape of the bidirectional reflectance distribution function (BRDF) for a disturbed packed-earth site at Walnut Gulch Experimental Watershed with a solar zenith angle of 45 degrees.

Spectral images are affected by interactions of the illumination source of the image and the pixel location within the image due to the bidirectional nature of most natural targets. These are often referred to as "geometric effects" or "viewing geometry effects" because the effect is primarily related to geometric configuration of the sensing system. The need to correct for geometric effects has been recognized ever since remote sensing has been used for scientific, quantitative purposes, but only in the past decade have the computing tools been available to make the correction practical for large imagery. The bi-directional reflectance distribution function (BRDF) models that have been developed to correct for this effect tend to be complicated and often cannot be generalized over images acquired over different surface types. ARS scientists have been working to develop simplified methods for practical and operational corrections of geometric effect.

Suggested publications:

Qi, J., M. S. Moran, F. Cabot and G. Dedieu, Normalization of sun/view angle effects on vegetation indices with bidirectional reflectance function models, Rem. Sens. Env. 52:207-217. Published 1995.

Qi, J., F. Cabot, M. S. Moran, G. dedieu and K. J. Thome, Biophysical parameter retrievals using multidirectional measurements, Rem. Sens. Env. 54:71-83. Published 1995.

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Last Modified: 11/1/2005
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