L-band active-passive and L-C-X-bands passive data for soil moisture retrieval, two different approaches in comparison

In the context of the project HYDRO-POL, a study was carried out to test the efficiency of two different approaches: the use of L band active and passive data or the use of L-C-X bands passive data to retrieve soil moisture of bare soils. Simulated data are generated implementing classical superficial scattering models: IEM model for active L-band and L-C-band passive data, GO model for X-band passive data. Data are simulated considering different roughness conditions and moisture content. As the inversion problem is very complex, artificial feedforward backpropagation neural networks (NN) were employed. The best performing NNs are chosen to simulate a retrieval with a dataset artificially added with noise. In each case, the best retrieved parameter is the real part of the dielectric constant, while roughness parameters, especially autocorrelation length, is not very well retrieved. In many cases, retrieved values are out of range, so that the simulated values and targets appear unrelated. Applying a very generic filter that eliminates values very far from the proper range, correlation coefficients grow up. This filter cleans up the resulting data removing a small part of them. After this filtration, correlation coefficients relative to the real part of the dielectric constant surpass 0.82. In spite of the filtering process, roughness parameters retrieval is of inferior quality. On smooth soil, the three considered configurations work in an equivalent way, excellently retrieving the real part of the dielectric constant, without a need for filtration. On medium and rough soil, inversion results generally more difficult, so that performance gets worse. Active-passive approach results more efficient than the L-C-X one