Integrating satellite soil moisture estimates and hydrological model products over Australia M. Khaki a,1 , A. Zerihun b , J. Awange a , M. Gibberd b , A. Dewan a a School of Earth and Planetary Sciences, Spatial Sciences, Curtin University, Perth, Australia. b Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Australia. Abstract Accurate soil moisture monitoring is essential for water resource management and agricultural ap- 1 plications and recently it has undertaken using satellite remote sensing or terrestrial hydrological 2 models’ products. While both methods have limitations, e.g., the limited soil depth resolution 3 of space-borne data and data deficiencies in models, data assimilation techniques can provide an 4 alternative approach. Here, we use the recently developed data-driven Kalman-Takens approach 5 to integrate satellite soil moisture products with those of the Australian Water Resources Assess- 6 ment system Landscape (AWRA-L) model. This is done to constrain the model’s soil moisture 7 simulations over Australia with those observed from the Advanced Microwave Scanning Radiome- 8 ter - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) between 9 2002 and 2017. The main objective is to investigate the ability of the integration framework to 10 improve AWRA-L simulations of soil moisture. The improved estimates are then used to investi- 11 gate spatio-temporal soil moisture variations. The results show that the proposed model-satellite 12 data integration approach improves the continental soil moisture estimates by increasing their cor- 13 relation to independent in-situ measurements (∼ 10% relative to the non-assimilation estimates). 14 15 Keywords: Data assimilation, Data-driven, Hydrology, Kalman-Takens, Satellite soil moisture. Email address: Mehdi.Khaki@postgrad.curtin.edu.au (M. Khaki)