World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

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.

Click here to view

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
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

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 http://hawaiilibrary.net/


Description
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.

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

Excerpt
Crow, W. T., Kustas, W. P., and Prueger, J. H.: Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model, Remote Sens. Environ., 112, 1268–1281, 2008.; Dagenbach, A., Buchner, J. S., Klenk, P., and Roth, K.: Identifying a parameterisation of the soil water retention curve from on-ground GPR measurements, Hydrol. Earth Syst. Sci., 17, 611–618, doi:10.5194/hess-17-611-2013, 2013.; Das, N. N. and Mohanty, B. P.: Root zone soil moisture assessment using remote sensing and vadose zone modeling, Vadose Zone J., 5, 296–307, 2006.; Das, N. N., Mohanty, B. P., Cosh, M. H., and Jackson, T. J.: Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04, Remote Sens. Environ., 112, 415–429, 2008.; De Lannoy, G. J. M., Houser, P. R., Pauwels, V. R. N., and Verhoest, N. E. C.: State and bias estimation for soil moisture profiles by an ensemble Kalman filter: effect of assimilation depth and frequency, Water Resour. Res., 43, W06401, doi:10.1029/2006WR005100, 2007.; Draper, C. S., Reichle, R. H., De Lannoy, G. J. M., and Liu, Q.: Assimilation of passive and active microwave soil moisture retrievals, Geophys. Res. Lett., 39, L04401, doi:10.1029/2011GL050655, 2012.; Ferré, P. A., Rudolph, D. L., and Kachanoski, R. G.: Spatial averaging of water content by time domain reflectometry: implications for twin rod probes with and without dielectric coatings, Water Resour. Res., 32, 271–279, 1996.; Galagedara, L. W., Parkin, G. W., Redman, J. D. P., and Endres, A. L.: Field studies of the GPR ground wave method for estimating soil water content during irrigation and drainage, J. Hydrol., 301, 182–197, 2005.; Grote, K., Hubbard, S. S., and Rubin, Y.: Field-scale estimation of volumetric water content using GPR ground wave techniques, Water Resour. Res., 39, 1321, doi:10.1029/2003WR002045, 2003.; Hoeben, R. and Troch, P. A.: Assimilation of active microwave observation data for soil moisture profile estimation, Water Resour. Res., 36, 2805–2819, 2000.; Huang, C., Li, X., Lu, L., and Gu, J.: Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter, Remote Sens. Environ., 112, 888–900, 2008.; Huisman, J. A., Hubbard, S. S., Redman, J. D., and Annan, A. P.: Measuring soil water content with ground penetrating radar, Vadose Zone J., 2, 476–491, 2003.; Jadoon, K., Lambot, S., Slob, E., and Vereecken, H.: Uniqueness and stability analysis of hydrogeophysical inversion for time-lapse proximal ground penetrating radar, Water Resour. Res., 44, W09421, doi:10.1029/2007WR006639, 2008.; Jadoon, K. Z., Lambot, S., Slob, E. C., and Vereecken, H.: Analysis of Horn Antenna Transfer Functions and Phase-Center Position for Modeling Off-Ground GPR, IEEE T. Geosci. Remote, 49, 1649–1662, doi:10.1109/TGRS.2010.2089691, 2011.; Jadoon, K. Z., Weihermuller, L., Scharnagl, B., Kowalsky, M. B., Bechtold, M., Hubbard, S. S., Vereecken, H., and Lambot, S.: Estimation of Soil Hydraulic Parameters in the Field by Integrated Hydrogeophysical Inversion of Time-Lapse Ground-Penetrating Radar Data, Vadose Zone J., 11, 4, doi:10.2136/vzj2011.0177, 2012.; Kowalsky, M. B., Finsterle, S., Peterson, J., Hubbard, S., Rubin, Y., Majer, E., Ward, A., and Gee, G.: Estimation of field-scale soil hydraulic and dielectric parameters through joint inversion of GPR and hydrological data, Water Resour. Res., 41, W11425, doi:

 

Click To View

Additional Books


  • Large-scale Runoff Generation – Parsimon... (by )
  • Estimation of Evapotranspiration in the ... (by )
  • The Role of Diagenisis in the Hydrogeolo... (by )
  • The Influence of Precipitation and Tempe... (by )
  • Multimodel Evaluation of Twenty Lumped H... (by )
  • The Effects of Riparian Forestry on Inve... (by )
  • Ground-penetrating Radar Insight Into a ... (by )
  • Similarity Between Runoff Coefficient an... (by )
  • Deriving Global Flood Hazard Maps of Flu... (by )
  • Hydrological Model Parameter Dimensional... (by )
  • Attribution of High Resolution Streamflo... (by )
  • On the Lack of Robustness of Hydrologic ... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.