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This work addresses the possibility of estimating soil moisture values starting from remotely sensed data in the microwave domain. The inversion approach is developed in a Bayesian framework, in order to merge point measurement derived from different sensors. The results indicate that the best combination to obtain reliable estimates of soil moisture is the case when backscattering coefficients and brightness temperature are considered with different polarisations. Furthermore, the introduction of prior information helps the inversion procedure to resolve the unavoidable ambiguities present in such problems.