# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 5 Conclusions  
In this paper, the results showed that the wheat crop water content can be effectively estimated based on water cloud model and enhanced radar polarization index by using Sentinel-1 dual-polarized radar data in the absence of optical data. However, the wheat crop water content inversion accuracy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Enhanced Radar Polarization Index** & **Stem-Leaf Water Content** & **Ear Water Content** & **Crop Water Content** & **Soil Water Content** & **Original Radar Polarization Index \& Soil Water Content** \\ \hline \(\sqrt{\psi_{\text{Ram}}}/\psi_{\text{mean}}\) & 0.150 & 0.001 & 0.195 & \(-\)0.355 * & \(-\)0.033 \\ \((\psi_{\text{ln}}-\sqrt{\psi_{\text{max}}})/(\psi_{\text{ln}}+\sqrt{\psi_{ \text{max}}})\) & 0.151 & 0.011 & 0.167 & \(-\)0.279 * & 0.063 \\ \(\sqrt{\psi_{\text{ln}}\alpha^{2}+\psi_{\text{max}}}^{2}/\psi_{\text{max}}\) & 0.286 * & 0.016 & 0.186 & \(-\)0.293 * & 0.015 \\ \hline \hline \end{tabular} Note. * Model significant at the 0.05 probability level (\(p<0.05\)).  
\end{table}
Table 6: Correlation analysis of the enhanced radar polarization indices and the original radar polarization indices.  
based on Sentinel-1 data is lower than that of based on Sentinel-2 data. This indicates that Sentinel-1 data can be used as a supplementary data source for the inversion of the wheat crop water content.  
Here, two inversion models for winter wheat crop water content based on Sentinel-1 SAR and Sentinel-2 optical images were established. 58 ground sample points were used for modeling and verification. For Sentinel-1 data, 23 enhanced radar polarization indices were firstly constructed and then selected most sensitive index to the measured soil water content as an input parameter to the water cloud model to retrieve the wheat crop water content. The verification accuracy of the inversion model based on Sentinel-1 data was R = 0.433, RMSE = 0.026 and nRMSE = 21.24%. For Sentinel-2 data, the gray relational analysis was used for several optical vegetation indices, and three vegetation indices were selected for regression modeling to retrieve the wheat crop water content. The verification accuracy of the inversion model based on Sentinel-2 data was R = 0.632, RMSE = 0.021 and nRMSE = 19.65%. The inversion accuracy of the Sentinel-2-based model was also higher than that of the Sentinel-1-based model. Because SAR can penetrate the vegetation canopy, the Sentinel-1 SAR-based method has the advantage of being able to estimate wheat crop water content during bad weather, such as cloud cover, which negatively affects optical data quality. Based on study results, the use of Sentinel-1 SAR data for the estimation of water content in wheat crops deserves further study. In addition, SAR data and optical data may be combined to explore more effective inversion methods for crop water content.  
[PERSON] provided toward creating the general idea of this paper; [PERSON] analyzed the data and wrote the draft of the manuscript; [PERSON], [PERSON] and [PERSON] helped edit the draft and provided critical comments to improve the paper; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] collected the data; [PERSON], [PERSON] and [PERSON] supervised the process of data handling.  
This research was funded by the National Key Research and Development Program of China (2017 YFE0122500), and the Special Funds for Technology innovation capacity building sponsored by the Beijing Academy of Agriculture and Forestry Sciences (KJCX20170423).  
The authors declare no conflict of interest.