# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results  
### Comparison of \(\sigma^{\circ}\) SAR Backscattering at Plot and Grid Scale  
To remove the ambiguity between rainfall and irrigation events, this study proposes to compare between the SAR signal obtained at plot scale and the SAR signal obtained at 10 km grid scale. Figure 6 represents an example of the temporal behavior of \(\sigma^{\circ}\) values in VV polarization obtained at plot and grid scale for a non-irrigated plot (Figure 6a) and an irrigated plot (Figure 6b). In both figures, the green curve represents the \(\sigma^{\circ}\) temporal evolution at the plot scale and the red curve represents  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Equation** \\ \hline
**OA** & \(\frac{\text{TP+TN}}{\text{TP+TN+FN+FP}}\) \\
**EA** & \(\frac{\text{(TP+FN)+FN}+\text{(FN+TN)\times[FP+TN)}}{\text{(TP+TN+FN+FP)}^{ \text{T}}}\) \\ \multirow{2}{*}{**Kappa**} & \(\frac{\text{OA+EA}}{\text{2 TP}}\) \\
**F1 score** & \(\frac{\text{2 TP+FP+FN}}{\text{TP+FP}}\) \\
**Precision** & \(\frac{\text{TP+FP}}{\text{TP+FN}}\) \\
**Recall** & \(\frac{\text{TP+FN}}{\text{TP+FN}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy indices calculated for each classifier. TP is the number of the irrigated plots truly classified as irrigated, TN is the number non-irrigated plots truly classified as non-irrigated plots, FP is the number of non-irrigated plots falsely classified as irrigated, FN is the number of irrigated plots falsely classified as non-irrigated plots, N is the total number of plots, and EA is the expected accuracy.  
Figure 5: Architecture of the one dimensional (1D) CNN model (CNN1D) used for classification of irrigated/non-irrigated plots using SAR and optical data.  
the \(\sigma^{\circ}\) temporal evolution calculated at the 10 km grid cell. Precipitation data obtained from a local meteorological station is added to the figures to better understand the relation between grid scale \(\sigma^{\circ}\) variations and the existence or absence of rainfall events. For the non-irrigated plot in Figure 6a, the \(\sigma^{\circ}\) SAR values at plot scale and grid scale attain the same behavior for almost all the dates. Following a rainfall event, both curves increase and then decrease following a dry period. The consistency between both curves at both scales for the non-irrigated plot during the whole period indicates that the plot did not receive any water supplement other than rainfall events. For the irrigated crop in Figure 6b, the temporal behavior of the \(\sigma^{\circ}\) SAR temporal series at plot and grid scales are coherent between September 2017 and April 2018. Both \(\sigma^{\circ}\) values for both scales increase following rainfall event, and decrease with the absence of rainfall. However, between May 2018 and September 2018 (black dashed circle in Figure 6b) the irrigated plot shows a frequent change in \(\sigma^{\circ}\) values accompanied with the absence of rainfall events due to possible irrigation events occurring. On the other hand, the 10 km grid cell shows low and constant values of \(\sigma^{\circ}\) during the same period indicating very dry soil conditions.