Soil-Adjusted Vegetation Index A transformation technique to minimize soil brightness from spectral vegetation indices involving red and near- infrared.

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Presentation transcript:

Soil-Adjusted Vegetation Index A transformation technique to minimize soil brightness from spectral vegetation indices involving red and near- infrared (NIR) wave lengths. The transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. Near InfraRed (NIR) Multispectral Arizona Fire - USA 21-JUN-2003

In general, most vegetation indices rely in the existence of a “soil line” in red and NIR wavelength space, i.e., there is a principal axis of soil spectral variation extending outward from the origin with increasing brightness. Since most of the soil spectra fall on or close o the soil line, and since the intercept of such a line is close to the origin, RVI and NDVI values of bare soils (ratios) will be nearly identical for a wide range in soil conditions.

Source: Huete, A.R. 1988

Figure 2 Source: Huete, A.R. 1988

Since the soil line has slope close to 1, the adjustment factors, l 1 and l 2, would be nearly equivalent. Shifting the red and NIR data equally (l 1 =l 2 ) and utilizing the NDVI format

Where L=l 1 +l 2 =2l. Thus a soil adjustment index(SAVI) would only involve an addition of a constant, L, to the denominator of the NDVI equation. However, in order to maintain the bounded cinditions ot the NDVI equation (NDVI can vary from –1 to +1), a multiplication factor (1+L) is needed in eq. 3

Figure 3 Source: Huete, A.R. 1988

Figure 4 Source: Huete, A.R. 1988

Figure 5 Source: Huete, A.R. 1988

MODIFIED-SAVI

Reference Huete, A.R “A soil-Adjusted Vegetation Index (SAVI)”, Remote Sensing of Environment, 25: Huete, A.R., Lui, H.Q “An Error and Sensitivity Analysis of the Atmospheric and Soil- Correctin Variants of the NDVI for he MODIS-EOS.” IEEE Transactions on Geoscience and Remote Sensing, 32(4),