2.2.2. Sentinel-2 Data

The Sentinel-2 (S2) constellation includes two identical satellites (S2A and S2B), which carry a multispectral instrument (MSI) for the acquisition of optical imagery at high spatial resolution (i.e., four spectral bands at 10 m, six bands at 20 m, and three bands at 60 m resolution). The S2 sensor acquired optical imagery during the same periods as S1 (Table 1), and the cloud-free tiles were downloaded in Level-2A (L2A), which provides orthorectified Bottom-Of-Atmosphere (BOA) reflectance, with sub-pixel multispectral registration. For this research, bands with 60 m spatial resolution were not considered due to their sensitivity to aerosol and clouds, whereas 20 m spectral bands were resampled to 10 m using the nearest neighbor method to preserve the pixels' original values [39].

#### 2.2.3. SAR Texture Features and Multispectral Indices

Since radar backscatter is strongly influenced by the roughness, geometric shape, and dielectric properties of the observed target, radar-derived texture information represents valuable information for classification tasks. Introduced by Haralick et al. [40], grey-level co-occurrence matrix (GLCM), depending on a given direction and a certain distance in the image, estimates the local patterns in image pixel intensities and spatial arrangement. Among many developed texture measures for vegetation mapping, GLCM, combined with the original radar image, is one of the most trustworthy methods for improving mapping accuracy. In this research, a set of nine texture features, derived from the GLCM, were calculated in the SNAP 8.0 software and used for vegetation mapping: Angular Second Moment (ASM), Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Mean, and Variance.

Satellite-based indices are commonly calculated from the spectral reflectance of two or more bands [41]. Using these indices indicates the relative abundance of features of interest, such as canopy chlorophyll content estimations, vegetation cover, and leaf area (Normalized Difference Vegetation Index—NDVI; Enhanced Vegetation Index—EVI; Soil Adjusted Vegetation Index—SAVI; Pigment Specific Simple Ratio—PSSRa) or water surfaces (Normalized Difference Water Index—NDWI). Moreover, modified and refined versions of the aforementioned indices were used (Modified Chlorophyll Absorption in Reflectance—MCARI; Green Normalized Vegetation Index—GNDVI; Modified Soil Adjusted Vegetation Index—MSAVI), as well as indices that use narrower red edge bands from S2 (Normalized Difference Index 45—NDI45; Inverted Red-Edge Chlorophyll Index— IRECI). Table 2 shows the multispectral indices employed in this research.

**Table 2.** Sentinel-1 (S1) and Sentinel-2 (S2) imagery used in this research.


\* NIR: Near-infrared band; R: Red band; G: Green band; B: Blue band; RE1, RE2, and RE3 represent Red-edge 1, 2, and 3 band, respectively. L is the adjusted factor that depends on terrain conditions and vegetation cover, where 0 indicates dense vegetation cover, and 1 represents areas without vegetation. In this research, L factor was set to 0.5 [52].
