## 5 Results
### Temporal Single Regression Analysis
A limited number of _quasi_- and significant correlations were found at C and X frequencies even if these were characterized by lower \(r\) values. Several very strong and significant \(\sigma^{\circ}\) vs. \(F_{\rm v}\) correlations (Table 5) were found over maize (at C\({}_{\rm HV}\), L\({}_{\rm HH}\) and L\({}_{\rm HV}\)) and sugar (all channels except L\({}_{\rm HH}\) and L\({}_{\rm HV}\)). These plants are both characterized by bigger leaf dimensions compared to those characterizing other cultivars. Noticeable, \(\sigma^{\circ}\) vs. \(F_{\rm v}\) regressions showed higher \(r\) than those characterizing \(\sigma^{\circ}\) vs. \(m_{\rm v}\) (best \(r\sim\)0.95 for \(\sigma^{\circ}\) vs. \(F_{\rm v}\) at L\({}_{\rm HV}\) over maize plot), thus confirming that: (i) \(\sigma^{\circ}\) is mainly controlled by geometric and dielectric properties of the vegetation; (ii) the canopy limits the assessment of the properties of the underlying soil. Although several significant correlations were found for other cultivars, these were characterized by weak (0.2 \(<\)\(r\)\(<\) 0.4) or moderate (0.4 \(<\)\(r\)\(<\) 0.6) \(r\) values. The highest and significant \(\sigma^{\circ}\) vs. \(LAI\) correlations were found again for sugar beet and maize; whereas, no significant correlations were found at X and C\({}_{\rm HH}\) channels. Several significant correlations were found for other cultivars; diversely than \(\sigma^{\circ}\) vs. \(F_{\rm v}\), most of these exhibited a strong correlation (Table 6). For some channels, \(\sigma^{\circ}\) resulted significantly correlated with both \(LAI\) and \(F_{\rm v}\). Although \(\sigma^{\circ}\) at L\({}_{\rm HH}\) resulted also correlated to \(m_{\rm v}\) the higher \(p\)-value indicates a weaker significance of this correlation. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\sigma^{\circ}\) vs. \(m_{\rm v}\) & \multicolumn{2}{c}{**Sugar**} & \multicolumn{2}{c}{**Barley**} & \multicolumn{2}{c}{**Maze**} & \multicolumn{2}{c}{**Rape**} & \multicolumn{2}{c}{**Wheat**} \\ \cline{2-10}  & \(r\) & \(p\)**-Value** & \(r\) & \(p\)**-Value** & \(r\) & \(p\)**-Value** & \(r\) & \(p\)**-Value** & \(r\) & \(p\)**-Value** \\ \hline \(\mathbf{X_{\rm HH}}\) & 0.32 & \(5.9\times 10^{-1}\) & - & - & 0.54 & \(2.7\times 10^{-1}\) & 0.37 & \(8.4\times 10^{-2}\) & 0.25 & \(2.6\times 10^{-1}\) \\ \(\mathbf{X_{\rm VV}}\) & 0.34 & \(5.7\times 10^{-1}\) & - & - & 0.55 & \(2.6\times 10^{-1}\) & 0.46 & \(2.7\times 10^{-2}\) & - & - \\ \(\mathbf{C_{\rm HH}}\) & 0.23 & \(7.2\times 10^{-1}\) & 0.38 & \(7.4\times 10^{-2}\) & 0.39 & \(4.4\times 10^{-1}\) & 0.19 & \(4.0\times 10^{-1}\) & 0.32 & \(1.3\times 10^{-1}\) \\ \(\mathbf{C_{\rm HV}}\) & - & - & 0.35 & \(9.8\times 10^{-2}\) & - & - & - & - & - & 0.46 & \(2.6\times 10^{-2}\) \\ \(\mathbf{C_{\rm VV}}\) & - & - & - & - & 0.59 & \(2.1\times 10^{-1}\) & 0.27 & \(2.1\times 10^{-1}\) & - & - \\ \(\mathbf{L_{\rm HH}}\) & - & - & 0.46 & \(2.8\times 10^{-2}\) & 0.75 & \(8.4\times 10^{-2}\) & 0.46 & \(2.6\times 10^{-2}\) & 0.51 & \(1.3\times 10^{-2}\) \\ \(\mathbf{L_{\rm HV}}\) & -0.52 & \(3.7\times 10^{-1}\) & 0.52 & \(1.2\times 10^{-2}\) & 0.72 & \(1.1\times 10^{-1}\) & 0.39 & \(6.6\times 10^{-2}\) & **0.66** & \(\mathbf{6.6\times 10^{-4}}\) \\ \(\mathbf{L_{\rm VV}}\) & - & - & 0.53 & \(9.4\times 10^{3}\) & 0.28 & \(5.9\times 10^{-1}\) & 0.40 & \(5.0\times 10^{-2}\) & 0.55 & \(7.0\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(r\) of \(\sigma^{\circ}\) vs. \(m_{\rm v}\) for plots covered by vegetation. _Quasi_-significant correlations (i.e., \(0.05<p\)-value \(<0.1\)) are outlined; significant correlations (i.e., \(p\)-value \(<0.05\)) are highlighted in light-grey; strong and significant correlations are also bolded; very weak correlations (\(r<0.19\)) are not reported.