SAVI

Description

Calculates a Soil Adjusted Vegetation Index (SAVI).

Parameters

Name	Type	Length	Value range
Input1: Input atmospherically corrected RED sensor band *	Raster port	2 - 2
Input2: Input atmospherically corrected NIR sensor band *	Raster port	2 - 2
Output: Output raster channel(s)	Raster port	0 - 1

* Required parameter

Parameter descriptions

Input1: Input atmospherically corrected RED sensor band

Specifies the image channels in the input file that contains the atmospherically corrected RED sensor band reflectance images from ATCOR.

Input2: Input atmospherically corrected NIR sensor band

Specifies the image channels in the input file that contains the atmospherically corrected NIR sensor band reflectance images from ATCOR.

Output: Output raster channel(s)

Specifies the output channels to receive the output SAVI image. If the specified output file does not yet exist, the default output channel is 1.

Details

SAVI calculates the Soil Adjusted Vegetation Index(SAVI) image and stores it in the specified output file.

As input, SAVI requires atmospherically corrected RED and NIR sensor band channels created by ATCOR.

Algorithm

The Atmospheric Correction component was produced by PCI using algorithms developed by DLR, German Aerospace Research Establishment. The algorithms used in the various programs of the component are based on information in the papers listed in the References section.

References

F. Lanzl and R. Richter, (DLR, German Aerospace Institute). A Fast Atmospheric Correction Algorithm For Small Swath Angle Satellite Sensors. ICO Topical Meeting on Atmospheric Volume and Surface Scattering and Propagation, Florence, Italy, August 27-30, 1991. pp. 455-458.

R. Richter, (DLR, German Aerospace Institute). Error Bounds Of A Fast Atmospheric Correction Algorithm For The Landsat Thematic Mapper And Multispectral Scanner Bands. Applied Optics 30(30):4412-4417. 1991.

R. Richter, (DLR, German Aerospace Institute). Model SENSAT: A Tool For Evaluating The System Performance Of Optical Sensors. SPIE Propagation Engineering 1312:286-297. 1990.

F.J. Ahern, P.M. Teillet, and D.G. Goodenough, (CCRS). Transformation Of Atmospheric And Solar Illumination Conditions On The CCRS Image Analysis System. 1977 Machine Processing of Remotely Sensed Data Symposium.

R. Richter, (DLR, German Aerospace Research Establishment). A Fast Atmospheric Correction Algorithm Applied To Landsat TM Images. Int. J. Remote Sensing 11(1):159-166. 1990.