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000021233 084__ $$2WoS$$aGeochemistry & Geophysics
000021233 1001_ $$0P:(DE-Juel1)VDB95614$$aJonard, F.$$b0$$uFZJ
000021233 245__ $$aAccounting for soil surface roughness in the inversion of ultrawideband off-ground GPR signal for soil moisture retrieval
000021233 260__ $$aTulsa, Okla.$$bSEG$$c2012
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000021233 440_0 $$02250$$aGeophysics$$v77$$x0016-8033$$y1
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000021233 500__ $$aThis work was supported in part by the Transregional Collaborative Research Centre 32 funded by the DFG (Deutsche Forschungsgemeinschaft) and in part by the FNRS (Fonds National de la Recherche Scientifique, Belgium). The authors are particularly grateful to Khan Zaib Jadoon of the Agrosphere Institute, Forschungszentrum Julich, for his help during the project. Finally, we also thank the anonymous reviewers for their constructive comments.
000021233 520__ $$aWe combined a full-waveform ground-penetrating radar (GPR) model with a roughness model to retrieve surface soil moisture through signal inversion. The proposed approach was validated under laboratory conditions with measurements performed above a sand layer subjected to seven different water contents and four different surface roughness conditions. The radar measurements were performed in the frequency domain in the range of 1-3 GHz and the roughness amplitude standard deviation was varied from 0 to 1 cm. Two inversion strategies were investigated: (1) Full-waveform inversion using the correct model configuration, and (2) inversion focused on the surface reflection only. The roughness model provided a good description of the frequency-dependent roughness effect. For the full-waveform analysis, accounting for roughness permitted us to simultaneously retrieve water content and roughness amplitude. However, in this approach, information on soil layering was assumed to be known. For the surface reflection analysis, which is applicable under field conditions, accounting for roughness only enabled water content to be reconstructed, but with a root mean square error (RMS) in terms of water content of 0.034 m(3) m(-3) compared to an RMS of 0.068 m(3) m(-3) for an analysis where roughness is neglected. However, this inversion strategy required a priori information on soil surface roughness, estimated, e.g., from laser profiler measurements.
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000021233 7001_ $$0P:(DE-Juel1)VDB17057$$aWeihermüller, L.$$b1$$uFZJ
000021233 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b2$$uFZJ
000021233 7001_ $$0P:(DE-Juel1)VDB54976$$aLambot, S.$$b3$$uFZJ
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