## 4 Results
### Soil Moisture Index Maps  
After calibrated the full LME model using 727 soil moisture and backscattering coefficient data pairs, the day-specific intercepts and slopes could be estimated, which were applied to the time series backscattering images to derive soil moisture maps over the study area. It should be noted that, since the site-specific term was not available for each grid, it was removed from the calibrated LME model when predicting soil moisture maps. Based on the Equation (4), a total of 49 soil moisture index maps were finally produced from the predicted soil moisture maps. With meaningless soil moisture index values, the water bodies and urban areas on each map were extracted and masked  
Figure 5: Scatter plots of measured versus predicted soil moisture indices. (**a,c**) are model-fitting and cross-validation results for the LME model, respectively; (**b,d**) are model-fitting and cross-validation results for the linear fixed effects model, respectively. The solid black line and dashed red line represent the regression line and 1:1 line, respectively. individually, making use of their distinctive backscattering characteristics. The spatial mean and coefficient of variation (CV) of soil moisture index were then calculated for each map, respectively. Basically, the CV tends to increase with decreasing mean soil moisture index (Figure 6), which suggests the higher spatial variability of soil moisture index under the drier conditions. The trend of the CV can be fitted by a decreasing power function, with a high coefficient of determination (R\({}^{2}\) = 0.988). Similar decreasing trend has been found for the CV of absolute soil moisture by many studies that were carried out in humid climates, which is related to the parameters of the moisture retention characteristic and their spatial variability. [1; 57; 58]. As there are large areas of irrigated paddy fields around the Poyang Lake, the more pronounced spatial differences of soil moisture between irrigated and non-irrigated fields might also contribute to the higher CV under the drier conditions. Figure 6 also shows the distinctive seasonal differences of the CV. In winter and spring, the mean soil moisture index is higher and less variable, whereas in summer and autumn, both the mean and CV of soil moisture index are highly variable. Among the 49 soil moisture index maps, six were selected to show the spatial distribution of soil moisture index under different soil moisture conditions (Figure 7). Since very few rain events occurred during late August to early September in 2015, the mean soil moisture index decreased from 0.75 on August 16 (Figure 7a) to 0.45 on September 9 (Figure 7b). More significant spatial variability can be observed from the soil moisture index map on September 9 (Figure 7b), where the soil moisture indices of the alluvial plains surrounding the Poyang Lake, which are mainly used as cultivated cropland, are much lower than those of the peripheral areas that covered largely by forests. It might be attributed to the different water-holding capacities of cropland and forest. Due to the frequent rainfall during November 2015, both the inundation area and the soil moisture index increased significantly on November 20 (Figure 7d), compared with those on October 27 (Figure 7c). Almost no spatial difference can be found in the soil moisture index map on November 20 (Figure 7d), when the moisture content over the whole region was nearly saturated. The mean soil moisture index decreased sharply from 0.33 on August 10 (Figure 7e), to almost zero on August 22 (Figure 7f), which is supposed to be related to the constantly high temperature and low precipitation during August 2016. Most of the areas suffered  
Figure 6: Relationship between mean soil moisture index and the coefficient of variation (CV). Each point denotes an acquisition and the colors represent different seasons (Spring: MAM; Summer: JJA; Autumn: SON; Winter: DJF).