## 3 Results
Based on PA, UA, and F-scores, the analysis of accuracies at the class level shows other differences between the classification methods (Figure 4). Water bodies are omitted from the analysis considering the high accuracies obtained with all the classification approaches. According to these accuracy indices, the most problematic classes are shrublands, grasslands, bare soil, and built-up. Adding the S1 to the S2 dataset improves the classification of all the LULC classes, both in the PB (PB_S2S1) and in the OB (OB_S2S1) approaches, except the UA of woodlands. The further addition of textures derived from S1 in the OB method (OB_S2S1T) improves the UA of woodlands and grasslands, and the PA of bare \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CODE**} & **Number of Selected** & **Selected Spectral** & **Textural** & \multicolumn{2}{c}{**Output**} \\  & **Features** & **Bands and Indices** & **Indices** & **Resolution** & **OA** \\ \hline \multirow{4}{*}{PB\_S2} & \multirow{4}{*}{9} & B2, B4, B8, B11, B12, & & & \\  & & NDVI, GNDVI, & - & 10 & 0.744 \\  & & & NDMI, BSI & & & \\  & & B2, B4, B8, B11, B12, & & & \\ PB\_S2S1 & 11 & & NDVI, GNDVI, & - & 10 & 0.812 \\  & & & NDMI, BSI, _VV, VH_ & & & \\ PB\_PL & 6 & & b, g, r, n, NDVI, & - & 4.77 & 0.667 \\  & & & GNDVI & - & 4.77 & 0.816 \\  & & & b, g, r, n, B11, B12, & & & \\ PB\_S2S1 PL & 12 & & NDVI, GNDVI, & - & 4.77 & 0.816 \\  & & & NDMI, BSI, _VV, VH_ & & & \\  & & B2, B4, B8, B11, B12, & & & \\ OB\_S2 & 14 & & NDVI, GNDVI, & - & 4.77 & 0.816 \\  & & & NDMI, BSI & & & \\ OB\_S2S1 & 16 & & NDVI, GNDVI, & - & 4.77 & 0.816 \\  & & & B2, B4, B8, B11, B12, & & & \\ OB\_S2S1T & 19 & & NDVI, GNDVI, & - & 4.77 & 0.822 \\  & & & NDMI, BSI, _VV, VH_ & & & \\ OB\_PL & 11 & & & & & \\  & & & B2, B4, B8, B11, B12, & & & \\ OB\_S2S1 PL & 16 & & NDVI, GNDVI, & - & 4.77 & 0.883 \\  & & & & & \\  & & & b, r, n, B11, B12, & & & \\ OB\_PLS2S1 & 16 & & NDVI, GNDVI, & - & 4.77 & 0.906 \\  & & & NDMI, BSI, VV, VH & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LULC classification approaches, number of features, bands, and indices selected for the classification, output resolution, and overall accuracy (OA). Classification approaches’ codes are reported in Table 2. S2 features are in upper case, S1 features are in italic upper case, and PlanetScope features are in lower case. Spectral indices (NDVI, GNDVI, NDMI, BSI) are reported in Table 3. Textural index codes and descriptions are reported in Table 4. The statistical significance of the OA differences between the classifications is shown in Table S1. soils and built-up. The OB approach to the dataset composed of S2 and S1 data (OB_S2S1) enhances the classification of croplands and built-up classes. Using only Planet data in the PB (PB_PL) and OB (OB_PL) approaches, even adding objects' textural information in the latter, produces worse classification results, mainly for shrublands and grasslands. In addition to producing a higher-resolution LULC map, data integration of all three satellites' data improves the classification of all LULC classes in the PB approach (PB_S2S1 PL). The same integration used with the OB approaches (OB_S2S1 PL and OB_PLS2S1) improves the accuracy of almost all the LULC classes, especially those most problematic for the other approaches (i.e., UA of shrublands, PA of grasslands, UA of bare soil). The slightly better performance of the OB_PLS2S1 method can be recognized mainly in UA of woodlands, PA of shrublands, and PA of grasslands. Figure 3: Comparison of LULC classification approaches in a representative region of the study area. Location of the area (blue arrow and rectangle) within the study area (red rectangle) (**a**), Google aerial view (**b**), 1_PB_S2 (**c**), 2_PB_S2S1 (**d**), 3_PB_PL (**e**), 4_PB_S2S1 PL (**f**), 5_OB_S2 (**g**), 6_OB_S2S1 (**h**), 7_OB_S2S1T (**i**), 8_OB_PL (j), 9_OB_S2S1 PL (**k**), 10_OB_PLS2S1 (**l**). Codes are reported in Table 2.