On The Retrievals Of Surface Soil Moisture From Simulated Smos And Amsr Brightnesstemperatures

Figure 4 compares the retrieved SM from the combined 6.9 GHz observation and 1D L-band observation at 30 degree with the corresponding reference. The retrieved SM is improved upon either the 1D L-band observation mode or the integrated 2 AMSR frequency observation mode by different degrees although its quality is slightly worse than the 2D L-band mode. The performance of the combined 10.7 GHz and 1D L-band observation mode is similar to the Fig. 1 Retrieved SM from the integrated 2 AMSR frequency mode versus the reference. Fig. 2 Retrieved SM for the 1D L-band mode at 30 degree versus the reference combined 6.9 GHz and 1D L-band observation mode. We notice that there is almost no difference in the correlation or RMSE between the two cases. That is, 6.9 and 10.7 GHz observations add approximately equal values onto the SMOS (1D L-band) observations for sensing SM. This statement remains effective when we combine either 6.9 or 10.7 GHz observation with 2D L-band observations with angular differences of 10 degrees. For simplicity, the results from the other cases of combined AMSR and SMOS multiple dimensional observations are not presented.



Fig. 3 Retrieved SM for the 2D L-band mode at 20-30 degree versus the reference.



Fig. 4 Retrieved SM from the combined 6.9 GHz and 1D L-band (at 30 degree) mode versus the reference.

V. Conclusions
The capability of the SMOS or the AMSR, and of their integration to measure SM from the space have been studied in this study by incorporating the LSP/R model into the newly Fig. 3 Retrieved SM for the 2D L-band mode at 20-30 degree versus the reference. Fig. 4 Retrieved SM from the combined 6.9 GHz and 1D L-band (at 30 degree) mode versus the reference developed EPLBP algorithm. For realization of the concerned problem, the input nodes of the algorithm are added by Gaussian distributed noises with 2 K standard deviations. We have shown that the EPLBP algorithm can manage the nonlinear mapping from microwave brightness temperatures to SM very well. In addition, it has been demonstrated that the EPLBP algorithm is robust since the quality of the retrieved SM remains high even when the Gaussian distributed noise with 2 K standard deviation is imposed upon the input nodes of the retrieval algorithm.

Acknowledgments
We appreciate much the National Science Council grant NSC 89-2111-M-008- 025-AP3.

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