24 research outputs found
Use of in-situ and remotely sensed observations for the optimization of hydrologic model results
Get PDFAssimilation of active microwave measurements for soil moisture profile retrieval under laboratory conditions
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Development and testing of a soil moisture inversion algorithm based on hydrological modeling and remote sensing through advanced filtering techniques
No full textAssimilation of active microwave measurements for soil moisture profile retrieval under laboratory conditions
No full textDeveloping and testing of a soil moisture inversion algorithm based on hydrological modelling and remote sensing through advanced filtering techniques
No full textOn the improvement of discharge predictions through assimilation of ERS-based soil moisture data
No full textSoil moisture inversion from ERS and SIR-C imagery at the Zwalm catchment, Belgium
No full textSeveral models have been presented in the recent past which should allow soil moisture inversion from bare soil radar backscattering. Three widely used models (i.e. the IEM [l], the models of Dubois et al. [2, 3] and of Oh et al. [4]) have been applied to ERS and SIR-C data obtained over the Zwalm catchment in Belgium. Results show an inability of the models to estimate surface soil moisture accurately through direct inversion. However, applying the method of effective roughness parameters [5] on the multi-temporal ERS tandem mission data resulted in a significant improvement of the soil moisture retrieval. The same method applied to multi-frequency SIR-C data was not able to improve the estimations. It is believed that the inversions suffer from the high soil roughness sensitivity of the backscattering at higher incidence angles
The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation.
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