The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
1348 
Figure 4. Schematic diagram shows the partition of the H- 
polarized brightness temperature (TbH) for the Roscommon 
area. The corresponding V-polarized brightness temperatures 
and the ground sampling volumetric soil moisture data are also 
obtained together with the TbH data. 
No. 
Backpropagation Algorithm 
RMSE 
(%v/v) 
1. 
Batch Gradient with Momentum 
4.86 
2. 
Gradient Descent with Adaptive Learning 
Rate 
5.34 
3. 
Gradient descent with momentum and 
adaptive learning rate 
4.88 
4. 
Resilient backpropagation 
4.93 
5. 
Conjugate gradient backpropagation with 
Fletcher-Reeves updates 
*fmish : epochs = 2 
4.82 
6. 
Conjugate gradient backpropagation with 
Polak-Ribiere updates *fmish : epochs = 2 
4.83 
7. 
Conjugate gradient backpropagation with 
Powell-Beale restarts *finish : epochs = 2 
4.83 
8. 
Scaled conjugate gradient backpropagation 
5.77 
9. 
Quasi-Newton Algorithm : BFGS 
3.93 
10. 
Quasi-Newton Algorithm :One step Secant 
Algorithm 
5.51 
11. 
Levenberg-Marquardt 
4.04 
Table 2. Root Mean Square Error (RMSE) of soil moisture 
retrieval of various backpropagation training algorithms 
7. DISCUSSION 
Results obtained for RMSE are between the ranges of 3.93%v/v 
to 5.77%v/v. The globally accepted accuracy requirement for 
soil moisture retrieval is less than or equal to 4%v/v RMSE. 
With the same backpropagation NN architecture (same number 
of hidden layer, number of neurons in the hidden layer and 
same bias and weights value), it is shown that the Broyden, 
Fletcher, Goldfarb, and Shanno (BFGS) Quasi-Newton 
algorithm is the only method able to retrieve the soil moisture 
with the required accuracy although the Levenberg-Marquardt 
method was close at 4.04%v/v. The scaled-conjugate algorithm 
has the worst result at 5.77%v/v. Interestingly, all of the 
methods produced results of a similar order of magnitude. The 
standard deviation of the ranges of accuracy obtained is 0.55, 
which is considered to be quite small, but for soil moisture 
retrieval this figure is quite significant. This means that the 
training algorithm when using backpropagation NN for soil 
moisture needs to be checked carefully before deciding on the 
algorithm to be used. The number of epochs required for each 
of the different backpropagation ANN to converge varies. The 
Conjugate Gradient backpropagation training algorithm 
converged in 2 epochs with an average RMSE of 4.83%v/v. 
8. CONCLUSIONS 
This paper has described initial results on the best 
backpropagation ANN to be used to model the relationship 
between airborne microwave measurements of brightness 
temperature and soil moisture content. A number of different 
backpropagation ANN methods have been explored with the 
main criterion for analysis being the RMSE between predicted 
soil moisture content and ground truth. Only one method 
produced results that meet the required 4%v/v criterion 
although a second method was close. Examining the standard 
deviation of the results across different training sets for the 
same ANN method reveals significant variations. The next step 
in the research is to explore the best ANN methods for 
additional areas within the NAFE test site and to investigate 
other available data that can be used to enhance the 
performance of the ANNs for the prediction of soil moisture 
content. 
ACKNOWLEDGEMENT 
The National Airborne Field Experiments have been 
made possible through recent infrastructure (LE0453434 
and LE0560930) and research (DP0557543) funding 
from the Australia Research Council, and the 
collaboration of a large number of scientists from 
throughout Australia, United States and Europe. 
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