Improving monitoring precision of soil moisture by assimilation of HYDRUS model and remote sensing-based data by ensemble Kalman filter

Abstract: Soil moisture as an important part of hydrology, atmosphere and land surface, is an essential link of surface water and groundwater, and it is also a key parameter to describe the exchange of energy for land, atmosphere and vegetation. Therefore, it is of great significance to accurately estimate soil moisture content in arid area due to its huge value for food security and water and soil conservation. This study investigated the feasibility of soil moisture estimation by assimilating HYDRUS model and remote sensing-based data using ensemble Kalman filter. The study area is located in the Weigan River and Kuqa River Delta in the southern Xinjiang region of Xinjiang Uygur Autonomous, developed by the Kuqa River and the Weigan River, which is the most representative arid oasis in the southern Xinjiang. Temperature-vegetation drought index (TVDI) was adopted as an observation operator, and ensemble Kalman filter (En-KF) method was applied to one-dimensional hydrological model (HYDRUS-1D) to simulate surface soil moisture. Soil samples from 39 points were collected for soil moisture measurement. The main conclusions included: 1) According to the TVDI feature space, the soil moisture was higher in the middle area (agricultural irrigation area) with high vegetation coverage, while in the oasis and desert transitional zone, soil moisture was low with low vegetation. In order to verify the error between the remote sensing image and the measured data, 10 samples were randomly selected from the 39 soil samples to simulate the soil moisture based on the TVDI feature space. The relative error between measured data and the remote sensing data was 13.06%, indicating that the soil moisture estimated by remote sensing was reliable and the estimated value could be considered as the measured data when the measured data were not available for some reasons; 2) Because the remote sensing inversion was mostly effective for the surface soil, the data for only 0-10 cm surface soil was used for the further assimilation analysis. The change in 0-10 cm soil moisture estimated by assimilation method and HYDRUS mod el from September 3rd to December 9th in 2013, a total of 98 days, showed that there was obvious difference between the HYDRUS model simulated results and the the measured data, especially before 18 day; 3）Verifying the assimilation results using the other 29 soil samples showed that the relative error between the assimilated results and measured results were 8% and that between the HYDRUS model simulated results with the measured results was 13%. The root mean square error between the measured results and assimilated and HYDRUS model simulated results was 9% and 10%, respectively. The accuracy of the assimilation result was higher than that of the HYDRUS model simulation. Compared with using HYDRUS-1D model alone, the estimating accuracy of surface soil moisture improved significantly by the integration of HYDRUS 1D model and Kalman Filter methods. The root mean square error and average relative error were decreased by 1 and 5 percent points, respectively. Thus, the En-KF algorithm can be used to simulate the dynamic changes of soil moisture in the model. Our experiments demonstrated the great potential of multi-source remote sensing data for the data assimilation of surface soil moistures. It is an effective method of improving the estimating accuracy of soil moisture in arid area.