A neural network approach was used to develop acccurate algorithms for
inverting a complex forest backscatter model. The model combines a fo
rest growth model with a radar backscatter model. The forest growth mo
del captures natural variations of forest stands (e.g., growth, regene
ration, death, multiple species and competition for light). This model
was used to produce vegetation structure data typical of transitional
/northern boreal hardwood forests in Maine. These data supplied inputs
to the radar backscatter model which simulated the polarimetric radar
backscatter (C, L, P, X bands) above the forests. Using these simulat
ed data, various neural networks were trained with inputs of different
backscatter bands and output parameters of above ground biomass, tota
l number of trees, mean tree height and mean tree age. These trained n
eural networks act as efficient algorithms for inverting the complex f
orest backscatter model. The accuracies (r.m.s. and R-2 values) for in
ferring various parameters from radar backscatter were above ground bi
omass (1.6 kg m(-2), 0.94), number of trees (48 ha(-1), 0.94), tree he
ight (0.47 m, 0.88) and tree age (24.0 years, 0.83). The networks that
used only AIRSAR bands (C, L, P) had a high degree of accuracy. The i
nclusion of the X band with the AIRSAR bands did not seem to increase
significantly the accuracy of the networks. The networks that used onl
y the C and L bands still had a relatively high degree of accuracy for
all forest parameter (R-2 values from 0.75 to 0.91). Modest accuracie
s (R-2 values from 0.65 to 0.84) were obtained with networks that used
only the L band and poor accuracies (R-2 values from 0.36 to 0.46) we
re obtained with networks that used only the C band. Several networks
were shown to be relatively insensitive to the addition of random nois
e to radar backscatter. The results demonstrate that complex, forest b
ackscatter models can be efficiently inverted using neural networks th
at use only radar backscatter data.