Articles | Volume 21, issue 7
https://doi.org/10.5194/hess-21-3267-2017
https://doi.org/10.5194/hess-21-3267-2017
Research article
 | 
04 Jul 2017
Research article |  | 04 Jul 2017

Multi-source hydrological soil moisture state estimation using data fusion optimisation

Lu Zhuo and Dawei Han

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Revised manuscript not accepted

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Cited articles

Al-Bitar, A., Leroux, D., Kerr, Y. H., Merlin, O., Richaume, P., Sahoo, A., and Wood, E. F.: Evaluation of SMOS soil moisture products over continental US using the SCAN/SNOTEL network, IEEE T. Geosci. Remote, 50, 1572–1586, 2012.
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Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, 2005.
Berg, A. A. and Mulroy, K. A.: Streamflow predictability in the Saskatchewan/Nelson River basin given macroscale estimates of the initial soil moisture status, Hydrolog. Sci. J., 51, 642–654, 2006.
Berthet, L., Andréassian, V., Perrin, C., and Javelle, P.: How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments, Hydrol. Earth Syst. Sci., 13, 819–831, https://doi.org/10.5194/hess-13-819-2009, 2009.
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Short summary
Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from remote sensing and land surface modelling. The result shows a significant improvement of the soil moisture state accuracy; the method can be easily applied in other catchments.