# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 3 Results  
### Choosing the Optical Vegetation Index and SAR Polarization Index  
In this study, 28 optical vegetation indices were selected and 23 enhanced radar polarization indices were proposed. Table 3 shows the results of the correlation analysis between the 28 optical vegetation indices and the water content of winter wheat at the sample points. The correlation between the optical vegetation indices and the measured water content of wheat ear and crop is basically similar. The correlation analysis results showed that the optical vegetation indices generally had more higher correlation with measured wheat crop water content than the measured wheat ear water content. The correlation between the optical vegetation indices and the measured water content of wheat stem and leaf is generally low. The vegetation indices with the highest degree of correlation with the water content of winter wheat stem and leaf were SIWSI3 and MSI2 (Pearson correlation coefficients were 0.431 and \(-\)0.411, respectively). The vegetation indices with the highest correlation with the water content of winter wheat ears were NDWI, NDVI, and SIWSI3 (Pearson correlation coefficients were \(-\)0.513, 0.487 and 0.480, respectively). The vegetation indices with highest correlation with the water content of winter wheat crop were SIWSI3, MSI2 and NDVI2 (Pearson correlation coefficients were 0.607, \(-\)0.575 and 0.562, respectively). Correlation analysis revealed that the optical vegetation indices based on Sentinel-2 data generally correlated well with the water content of winter wheat crop. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vegetation**} & **Stem and** & **Ear Water** & **Crop Water** & **Enhanced Radar Polarization** & **Soil Water** \\  & **Leaf Water** & **Content** & **Content** & **Indices** & **Content** \\ \hline SRWI & 0.250 & 0.446 ** & 0.451 ** & _vvt\({}_{en}\)_ & 0.115 \\ MSI1 & 0.082 & 0.034 & 0.069 & _vt\({}_{en}\)_ & 0.064 \\ MSI2 & \(-\)0.411 ** & \(-\)0.45 ** & \(-\)0.575 ** & _vt\({}_{en}\)_ & 0.103 \\ MSI3 & \(-\)0.133 & 0.041 & \(-\)0.08 & _vt\({}_{en}\)_ & \(-\)0.012 \\ NDII1 & 0.217 & 0.414 ** & 0.402 ** & _vt\({}_{en}\)_ & 0.050 \\ NDII2 & \(-\)0.034 & 0.305 * & 0.154 & \((v{}_{len}-vv_{en})\)\(/(v{}_{len}+vv_{en})\) & 0.037 \\ NDII3 & \(-\)0.142 & 0.035 & \(-\)0.09 & _vt\({}_{en}\)_ & \({}^{2}+vv_{en}{}^{2}\) & 0.013 \\ NDVI1 & 0.309 & 0.487 ** & 0.516 ** & \((v{}_{len}\)\({}^{2}-vv_{en}{}^{2})\)\(/(v{}_{len}\)\({}^{2}+vv_{en}{}^{2})\) & 0.016 \\ NDVI1 & 0.288 & 0.419 ** & 0.459 ** & \(v{}_{len}\)\({}^{2}-vv_{en}{}^{2}\)\(/(v{}_{len}\)\({}^{2}+vv_{en}{}^{2})\) & 0.077 \\ NDVI2 & 0.362 & 0.460 ** & 0.562 ** & \(\sqrt{v{}_{len}}\)\(/vv_{en}\)\(/vv_{en}\) & \(-\)0.355 * \\ NDVI3 & 0.159 & \(-\)0.003 & 0.174 & \(\left(\sqrt{v{}_{len}}-vv_{en}\right)\)\(/\left(\sqrt{v{}_{len}}+vv_{en}\right)\) & \(-\)0.059 \\ NDWI & \(-\)0.316 & \(-\)0.513 ** & \(-\)0.523 ** & \(\left(\sqrt{v{}_{len}}-vv_{en}\right)\)\(/\left(\sqrt{v{}_{len}}+vv_{en}\right)\)\({}^{2}\) & \(-\)0.076 \\ NDWI1 & \(-\)0.178 & \(-\)0.399 ** & \(-\)0.347 ** & \(v{}_{len}\)\(/\sqrt{v{}_{len}}\)\(/\sqrt{v{}_{len}}\)\(/\)\  
The water content of winter wheat crop and the water content of winter wheat ear were closely same under different vegetation indices. In contrast, the vegetation indices did not correlate well with the water content of winter wheat stem and leaf. This is because optical remote sensing makes it easy to obtain crop canopy spectral reflectance information, but has limitations on the acquisition of internal information of crop (like stem). In the later stages of crop growth, vegetation biomass and canopy coverage are both high. Optical remote sensing cannot easily detect the water content of stems and leaves because of the poor permeability of the canopy. Nonetheless, optical remote sensing effectively detects the water content of winter wheat ears and canopy.