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Improving Soil Moisture Profile Reconstruction from Ground-penetrating Radar Data: a Maximum Likelihood Ensemble Filter Approach : Volume 17, Issue 7 (09/07/2013)

By Tran, A. P.

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Book Id: WPLBN0004010800
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Improving Soil Moisture Profile Reconstruction from Ground-penetrating Radar Data: a Maximum Likelihood Ensemble Filter Approach : Volume 17, Issue 7 (09/07/2013)  
Author: Tran, A. P.
Volume: Vol. 17, Issue 7
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Vanclooster, M., Lambot, S., & Tran, A. P. (2013). Improving Soil Moisture Profile Reconstruction from Ground-penetrating Radar Data: a Maximum Likelihood Ensemble Filter Approach : Volume 17, Issue 7 (09/07/2013). Retrieved from

Description: Environmental Sciences, Earth and Life Institute, Université catholique de Louvain, Croix du Sud 2, P.O. Box L7.05.02, 1348 Louvain-la-Neuve, Belgium. The vertical profile of shallow unsaturated zone soil moisture plays a key role in many hydro-meteorological and agricultural applications. We propose a closed-loop data assimilation procedure based on the maximum likelihood ensemble filter algorithm to update the vertical soil moisture profile from time-lapse ground-penetrating radar (GPR) data. A hydrodynamic model is used to propagate the system state in time and a radar electromagnetic model and petrophysical relationships to link the state variable with the observation data, which enables us to directly assimilate the GPR data. Instead of using the surface soil moisture only, the approach allows to use the information of the whole soil moisture profile for the assimilation. We validated our approach through a synthetic study. We constructed a synthetic soil column with a depth of 80 cm and analyzed the effects of the soil type on the data assimilation by considering 3 soil types, namely, loamy sand, silt and clay. The assimilation of GPR data was performed to solve the problem of unknown initial conditions. The numerical soil moisture profiles generated by the Hydrus-1D model were used by the GPR model to produce the observed GPR data. The results show that the soil moisture profile obtained by assimilating the GPR data is much better than that of an open-loop forecast. Compared to the loamy sand and silt, the updated soil moisture profile of the clay soil converges to the true state much more slowly. Decreasing the update interval from 60 down to 10 h only slightly improves the effectiveness of the GPR data assimilation for the loamy sand but significantly for the clay soil. The proposed approach appears to be promising to improve real-time prediction of the soil moisture profiles as well as to provide effective estimates of the unsaturated hydraulic properties at the field scale from time-lapse GPR measurements.

Improving soil moisture profile reconstruction from ground-penetrating radar data: a maximum likelihood ensemble filter approach

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