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Imaging subsurface soil moisture dynamics using tomopgraphic profiling: observations and modelling

Zwieback, S., Hajnsek, I., Edwards-Smith, A. and Morrison, K. (2016) Imaging subsurface soil moisture dynamics using tomopgraphic profiling: observations and modelling. In: Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, 10-15 July 2016, Beijing, China, pp. 5256-5259,

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To link to this item DOI: 10.1109/IGARSS.2016.7730369


Depth-resolved radar imaging at L- to X-band has barely been applied to soils owing to limitations imposed by wave absorption within the soil and the resolutions attainable from air- or spaceborne platforms. Rather, soils are commonly studied using radar systems that cannot resolve the depth component. In this study, we adapt tomographic profiling to image the wetting and drying of sandy soil using a ground-based radar with a depth resolution of about 10 cm. The depth-resolving capabilities are achieved using synthetic aperture processing of the measurements obtained with downward pointing antennas operating at C-band with 2 GHz bandwidth. The observed subsurface backscatter appears to be governed by the local soil moisture content and the soil moisture content above (absorption). When the soil moisture content changes, the observed differential interferometric phase and coherence are consistent with the notion that the total depth-averaged interferometric return is governed by volume scattering and wave propagation within the soil. However, existing models of the depth-averaged interferometric coherence do not include variations in the volume scattering power induced by soil moisture changes, which the backscatter observations indicate exist. Besides improving our understanding of the radar backscatter from heterogeneous soils, depth-resolved imaging may in future also provide direct information about the spatial variability of soil properties and soil moisture dynamics.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:73525

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