SAVI Algorithm Specification | ![]() |
The Soil Adjusted
Vegetation Index algorithm was
introduced by Huete (1988).
This index attempts to be a hybrid between the ratio-based indices
and the perpendicular indices.
The reasoning behind this index acknowledges that the
isovegetation lines are not parallel, and that they do not all
converge at a single point.
The initial construction of this index was based on measurements
of cotton and range grass canopies with dark and light soil
backgrounds,
and the adjustment factor L was found by trial and error until a
factor that gave equal vegetation index results for the dark and
light soils was found.
The result is a ratio-based index where the point of convergence
is not the origin.
The convergence point ends up being in the quadrant of negative
NIR and RED values, which causes the isovegetation lines to be more
parallel in the region of positive NIR and RED values than is the
case for RVI, NDVI, and IPVI.
Huete (1988) does present a theoretical basis for this index based
on simple radiative transfer, so SAVI probably has one of the
better theoretical backgrounds of the vegetation indices.
However, the theoretical development gives a significantly
different correction factor for a leaf area index of 1 (0.5)
than resulted from the empirical development for the same leaf
area index (0.75).
The correction factor was found to vary between 0 for very high
densities to 1 for very low densities.
The standard value typically used in most applications is 0.5
which is for intermediate vegetation densities.
The SAVI results from the following equation:
SAVI = (1 + L) * (IR_factor * near_IR - red_factor * red) /
(IR_factor * near_IR + red_factor * red + L)
where: L is a correction factor which ranges from
0 for very high vegetation cover to 1 for very low
vegetation cover.
The most typically used value is 0.5 which is for
intermediate vegetation cover.
Not all soils are alike. Different soils have different
reflectance spectra.
All of the vegetation indices assume that there is a soil line,
where there is a single slope in RED-NIR space.
However, it is often the case that there are soils with different
RED-NIR slopes in a single image.
Also, if the assumption about the isovegetation lines (parallel or
intercepting at the origin) is not exactly right,
changes in soil moisture (which move along isovegetation lines)
will give incorrect answers for the vegetation index.
The problem of soil noise is most acute when vegetation cover is
low.
The following group of indices attempt to reduce soil noise by
altering the behavior of the isovegetation lines.
All of them are ratio-based, and the way that they attempt to
reduce soil noise is by shifting the place where the isovegetation
lines meet.
WARNING: These indices reduce soil noise at the cost of
decreasing the dynamic range of the index.
These indices are slightly less sensitive to changes in vegetation
cover than NDVI (but more sensitive than PVI) at low levels of
vegetation cover.
These indices are also more sensitive to atmospheric variations
than NDVI (but less so than PVI). (See Qi et al. (1994) for
comparisons.)
Also the processor computes an additional flags band called 'savi_flags' with the following bit coding:
Bit Position | Description |
---|---|
Bit 0 | The computed value for SAVI is NAN or is Infinite |
Bit 1 | The computed value for SAVI is less than -1 (minus one) |
Bit 2 | The computed value for SAVI is greater than 1 (one) |