# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 3 Results
### Inversion of Winter Wheat Water Content Using Sentinel-1 Data  
38 randomly selected sample points (the modeling set) were used to establish a water content inversion model for winter wheat using data from the Sentinel-1 satellite. The Levenberg-Marquardt and global optimization algorithm was used to estimate the fitting coefficients of the water cloud model. The fitting coefficients and model accuracy are shown in Table 5. Model accuracy was R = 0.471, RMSE = 0.022 and nRMSE = 19.98%. 20 sample points were used to verify model accuracy and the results (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%) were similar to those for model accuracy. The distribution of sample points in the verification set is shown in Figure 5.  
The verification step demonstrated that the model accurately estimated the water content of wheat crop. Model-estimated crop water content for wheat crop sample points located in the middle of the ground-measured crop water content interval. For both high and low values for crop water content, the model-estimated values for crop water content tended to be lower and higher than ground-measured values. This demonstrates that the inversion effect of the model for high or low values for crop water content is not particularly good. The reason for this phenomenon may be that the fitting accuracy of model coefficients is not good. Because the model set was randomly selected, some errors exist in the correction of model coefficients in the sample points, leading to large deviations in the verification results for extreme values in the verification set. The radar polarization index, which is limited to the dual polarization of the Sentinel-1 image data, generally does not correlate well with soil water content in vegetation-covered areas. Furthermore, the overall inversion accuracy of the model is reduced. The model exhibits large error when inverting high and low values for crop water content. However, model error was within an acceptable error range. These results indicate that Sentinel-1 radar data have the potential to detect the water content of wheat crop. Figure 5: Verification results of the water content inversion model for wheat crops. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Coefficient** & **Value** & **Precision Index** & **Value** \\ \hline A & \(-5.7113\) & R & 0.471 \\ B & 0.0417 & RMSE & 0.022 \\ C & 0.3545 & nRMSE & 19.98\% \\ D & 0.0005 & F-statistics & 2.648 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model accuracy and the fitting coefficients of the water cloud model. Mapping the Water Content of Winter Wheat Crops Using Remote Sensing Data from Sentinel Series Satellites  
In this study, two models for monitoring winter wheat crop water content based on data from the Sentinel-1 and Sentinel-2 satellites were proposed. The models based on the satellite data to estimate the water content of wheat crops in Gaocheng district. Thematic maps of wheat crop water content are presented in Figure 6. Sentinel-1 imagery was available for the entire study site, whereas some Sentinel-2 imagery was missing for parts of the study area. Gaocheng District's wheat planting regions are mainly concentrated in the south and north. Winter wheat water content was highest in central Gaocheng District on 25 May 2017, followed by the north. The southern region exhibited the lowest wheat water content and was also the largest by land area. The reason is that the wheat crops in central Gaocheng District receive plentiful irrigation due to the superior infrastructure that accompanies the region's high population concentration and degree of urbanization. Remote monitoring estimates of wheat crop water content were largely consistent with traditional field survey results. The water content estimates based on SAR and optical remote sensing data were relatively consistent with each other, suggesting that Sentinel-1 imagery can be used for wheat crop water content monitoring in large areas when Sentinel-2 imagery is unavailable or impacted by weather conditions.