## 4 Results  
This study applied the SVM-RFE method to obtain the ranked list of the features before classification. Therefore, original features, including Sentinel-1 bands (VV, VH), Sentinel -2 bands (B2, B3, B4, B8, B5, B6, B7, B8A, B11, and B12), spectral indices (NDVI, NDWI, and NDBI), and eight texture information derived from VV band (mean, dissimilarity, homogeneity, second moment, contrast, variance, entropy, and correlation) were used. Table 4 gives a summary of the most critical features after adopting SVM-RFE for different landscapes. Figure 2: An example of oversampling by the G-SMOTE algorithm. The RF, SVM, and ELM approaches using the obtained features (Table 4) were employed for generating LC maps in six different landscapes. The LC maps were generated with two sets of reference datasets to investigate the impact of class imbalance on the LC classification accuracy; first, without balancing samples and the scored one by adopting the G-SMOTE for balancing samples of classes. As shown in Tables 5 and 6, all three ML algorithms illustrated good performance in LC mapping. For example, all generated maps obtained OA above 0.83, ranging from 0.85 to 0.93 for RF, 0.83 to 0.94 for SVM, and 0.84 to 0.92 for ELM. Our analysis also showed that adopting the G-SMOTE method to rebalance reference datasets substantially improved UA and PA accuracies of minority classes. As illustrated in Figure 3, RF-G-SMOTE showed the best performance in four landscapes, namely coastal, cropland, desert, and semi-arid. In comparison, the SVM-G-SMOTE obtained higher accuracies for the remaining two landscapes, including plain and mountain. \begin{table}
\begin{tabular}{c \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & & & **SVM** & & & **RF** & & & **ELM** & \\ \hline
**Sites** & **Class** & **UA** & **PA** & **OA** & **UA** & **PA** & **OA** & **UA** & **PA** & **OA** \\ \hline \multirow{4}{*}{Coastal} & Barren & 0.85 & 0.88 & & 0.87 & 0.83 & & 0.85 & 0.85 & \\  & Built-up & 0.88 & 0.76 & & 0.84 & 0.80 & & 0.84 & 0.75 & \\  & **Cropland** & **0.91** & **0.85** & 0.91 & **0.80** & **0.81** & 0.92 & **0.77** & **0.83** & 0.88 \\  & Forest & 0.93 & 0.9 & & 0.90 & 0.89 & & 0.90 & 0.94 & \\  & Water & 1 & 1 & & 1 & 1 & 1 & 1 & \\ \hline \multirow{4}{*}{Cropland} & Barren & **0.80** & **0.77** & & **0.75** & **0.79** & & **0.72** & **0.77** & \\  & Built-up & 0.93 & 0.90 & & 0.87 & 0.85 & & 0.89 & 0.80 & \\  & Cropland & 0.88 & 0.91 & 0.84 & 0.84 & 0.86 & 0.85 & 0.83 & 0.84 & 0.85 \\  & **Pasture** & **0.85** & **0.87** & & **0.80** & **0.82** & & **0.77** & **0.79** & \\  & Water & 1 & 1 & & 1 & 1 & 1 & 1 & \\ \hline \multirow{4}{*}{Desert} & Barren & 1 & 1 & & 1 & 0.98 & & 0.97 & 1 & \\  & **Built-up** & **0.88** & **0.78** & & **0.89** & **0.79** & & **0.79** & **0.87** & \\  & Cropland & 0.92 & 0.93 & & 0.99 & 0.94 & & 0.92 & 0.82 & \\  & Water & 1 & 1 & & 1 & 1 & 1 & 1 & \\ \hline \multirow{4}{*}{Mountain} & Barren & 0.9 & 0.96 & & 0.83 & 0.95 & & 0.85 & 0.81 & \\  & **Cropland** & **0.85** & **0.82** & & **0.85** & **0.88** & & **0.80** & **0.78** & \\  & Pasture & 0.96 & 0.9 & 0.91 & 0.86 & 0.94 & 0.90 & 0.84 & 0.94 & 0.84 \\  & Snow & 0.92 & 1 & & 0.78 & 0.80 & & 0.90 & 0.89 & \\  & Water & 1 & 1 & & 1 & 1 & 1 & 1 & \\ \hline \multirow{4}{*}{Plain} & Barren & 0.91 & 0.98 & & 0.89 & 0.93 & & 0.93 & 0.95 & \\  & Built-up & 0.92 & 0.92 & & 0.93 & 0.95 & & 0.92 & 0.92 & \\  & Cropland & 0.89 & 0.91 & & 0.89 & 0.90 & & 0.93 & 0.93 & \\  & **Pasture** & **0.80** & **0.75** & & **0.81** & **0.78** & & **0.82** & **0.78** & \\ \hline \multirow{4}{*}{Semi-Arid} & Barren & 0.86 & 0.81 & & 0.83 & 0.87 & & 0.80 & 0.83 & \\  & Built-up & 0.86 & 0.85 & & 0.89 & 0.85 & & 0.82 & 0.82 & \\  & Cropland & 0.82 & 0.83 & & 0.80 & 0.85 & & 0.80 & 0.83 & \\  & **Pasture** & **0.79** & **0.74** & & **0.75** & **0.77** & & **0.77** & **0.75** & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The result of accuracy assessment methods in six different landscapes (after adopting G-SMOTE).