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

Zhuo, Lu; Han, Dawei

doi:https://doi.org/10.5194/hess-21-3267-2017

Articles | Volume 21, issue 7

https://doi.org/10.5194/hess-21-3267-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-21-3267-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 7

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

Data sets

Products access Centre Aval de Traitement des Données SMOS (CATDS) http://www.catds.fr/Products/Products-access

MODIS/Terra Land Surface Temperature and Emissivity Daily L3 Global 0.05Deg CMG, MOD11C1 Land Processes Distributed Active Archive Center (LP DAAC) https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11c1

NLDAS-2 Forcing Download Information NASA, Land Data Assimilation Systems (LDAS) https://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing_download.php

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.