## 6 Conclusions  
The objective of this study was to generate surface soil moisture products from multi-temporal Sentinel-1 images. To this end, a linear mixed effects model was developed to establish the relationship between soil moisture ground measurements and Sentinel-1 backscattering coefficients over Poyang Lake ungauged zone. The major results of this work are summarized as follows.  
(1) Three linear mixed effects models were developed using backscattering coefficients of VV polarization, VH polarization, and VV + VH polarizations, respectively. All of the three models meet the ESA accuracy requirement (\(\leq\)5%), while the VV polarized model achieves the best performance, with an overall R\({}^{2}\) of 0.89 and RMSE of 2.53%.  
(2) The full linear mixed effects model was compared with the model without the site-specific term. Results show that site effect plays an important role in the soil moisture and backscattering coefficient relationship. Nevertheless, both of the models characterize the temporal variations of soil moisture moisture rather well, and a slightly higher temporal R\({}^{2}\) of 0.64 is obtained by the full linear mixed effects model.  
(3) A relative soil moisture index was proposed to correct for site effect. Both the fixed and mixed effects models were applied to predict the soil moisture indices, which were then validated by the measured soil moisture indices. The R\({}^{2}\) value of the mixed effects model at each observation site ranges from 0.52 to 0.88, totally exceeding that of the fixed effects model, which ranges from 0.44 to 0.81. Moreover, with the overall R\({}^{2}\) and cross-validated R\({}^{2}\) being 0.68 and 0.64, respectively, the linear mixed effects model shows a better and more robust performance in predicting the temporal dynamics of soil moisture, when compared with the fixed effects model.  
Time series soil moisture product with appropriate spatial resolution is invaluable for watershed scale hydrological applications, especially over ungauged basins. The linear mixed effects model that is developed in this work shows the potential for estimating soil moisture from time series Sentinel-1 data in a simple and robust way. The model can be easily transferred to other watersheds, since the Sentinel-1 constellation provides C-band radar data globally, while soil moisture ground measurements are also needed for the calibration and validation of the model. Model prediction uncertainties mainly come from the limited ground observations and the assumptions of a linear soil moisture and backscattering coefficient relationship and backscattering coefficient as the single predictor. Further studies can be investigating the impact of more explanatory factors, such as vegetation and soil parameters, to have a better understanding of site effect.  
This study was supported by grants from National Natural Science Funding of China (NSFC, 41331174), Major Science and Technology Program for Water Pollution Control and Treatment (2013 ZX07105-005), and Surveying & Mapping and Geo-information Research in the Public Interest (No. 201512026). The authors would like to thank Copernicus Open Access Hub for providing Sentinel-1 data and China Meteorological Data Sharing Service System for the meteorological data used in the research. We are grateful for the instructive comments and suggestions provided by the two anonymous reviewers.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] contributed the data analysis; [PERSON] helped to process the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.