# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Random Forest Classifier
#### 4.2.2 WT-RF  
The linear combination of all the levels of the WT allows the reconstruction of the real SAR signal using the obtained 64 wavelet coefficients. Figure 8 presents an example of the reconstruction of the \(\sigma^{o}\) SAR temporal series of an alfalfa irrigated plot using the linear combination of consecutive decomposing levels (functions) of the wavelet transformation. The first father scale function consists of two coefficients (level 1). The mother scale functions of levels two, three, four, five and six consist of two, four, eight, 16, and 32 coefficients respectively. The linear combination of the father level with additive mother levels allows the reconstruction of the real SAR signal (Figure 8). The obtained 64 wavelet coefficients are used in a random forest classifier in order to classify irrigated plots. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Class** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{5}{*}{**PC-RF 328 Variables**} & **Irrigated** & 0.95 & 0.79 & 0.86 \\  & **Non-Irrigated** & 0.90 & 0.98 & 0.94 \\  & **OA** & & 91.2\% & \\  & **Kappa** & & 0.79 & \\  & **F-score** & & 0.91 & \\ \hline \multirow{5}{*}{**PC-RF 15 Important Variables**} & **Irrigated** & 0.92 & 0.81 & 0.86 \\  & **Non-Irrigated** & 0.90 & 0.96 & 0.93 \\ \cline{1-1}  & **OA** & & 90.7\% & \\ \cline{1-1}  & **Kappa** & & 0.79 & \\ \cline{1-1}  & **F-score** & & 0.91 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The values of the overall accuracy, Kappa, F-score, precision, and recall obtained from the RF classifier using the 328 SAR PC variables and 15 important SAR PC variables. Figure 8: Reconstruction of SAR signal in VV polarization at plot scale through the linear combinations of the ‘Haar’ wavelet coefficients using (**a**) 2 coefficients, (**b**) 4 coefficients, (**c**) 8 coefficients, (**d**) 16 coefficients, (**e**) 32 coefficients, and (**f**) 64 coefficients. The same process applied for the RF classification using the PC values is also performed using the Haar wavelet coefficients. The RF classifier was first trained using the 256 values (discussed in Section 3.5) of the wavelet coefficients. When validating the obtained classifier, the overall accuracy of the classification reached 91.4% with a 0.81 kappa coefficient and a 0.91 F-score. The F-score of the irrigated class (0.87) is slightly lower than that of the non-irrigated class (0.94) (Table 4). The significant wavelet coefficients that most contributed in the classification were also determined using the threshold value on the increase in the mean square error of the predictions (%IncRMSE >1%). This also reduced the number of variables from 256 coefficients to 18. Figure 9 similarly shows the scatter plot of 2000 irrigated and 2000 non-irrigated plots (randomly selected) with different combinations of the obtained important WT coefficients. As shown with the PC values, Figure 9 also clearly shows that irrigated and non-irrigated plots can be separated using the WT coefficients as they distinctly appear in two different point clouds. The validation of the RF classifier trained using important data only (18 variables) produces slightly lower accuracy than that using the full dataset (Table 4). The overall accuracy decreased by 1.3% only. The F-score of the irrigated class decreased from 0.87 to 0.83 while that of the non-irrigated class decreased from 0.94 to 0.92. The kappa value also decreased from 0.81 to 0.75. Although the accuracies reported using the 18 important WT coefficients show lower values compared to those obtained using the whole dataset, the proposed RF classifier keeps a very good performance.