# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 2 Materials
### Satellite Data
#### 2.3.3 Sentinel-2 Data  
Sentinel-2 satellites collect multi-spectral optical imagery of high spatial resolution every 5 days [105]. Both satellites carry a Multi-Spectral Imaging Instrument (MSI) with a swath width of 290 km, which is designed to capture electromagnetic waves of the visible and near infrared region to the shortwave infrared region of the spectrum. More specifically, Sentinel-2 MSI captures targets' reflectance responses in 13 bands of different wavelengths, namely B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, and B12, with various spatial resolutions of 10, 20, and 60 m.  
For the purposes of this study, 19 Sentinel-2 Level-1C images, having the minimum cloud coverage over the study area, were retrieved from the Copernicus Open Access Hub during the period January-October 2018, resulting in a dense time-series. The selected acquisition dates are extended across the entire growth cycle of the cultivated crops and is expected to hold important information at key phenological stages, such as flowering, ripening, and harvesting phases. Additionally, the 60 m bands (i.e., B01, B09, and B10) were excluded. Regarding the images pre-processing, Sentinel-2 products were atmospherically corrected to bottom of atmosphere reluctance by using Sen2 Cor algorithm in the SNAP software, and all the bands were resampled to 10 m spatial resolution.  
Apart from using the original Sentinel-2 bands, we evaluated the potential of four vegetation indices, namely the Normalized Difference Vegetation index (NDVI), the Normalized Difference Water Index (NDWI), the Plant Senescence Reflectance Index (PSRI), and the Soil Adjusted Vegetation Index (SAVI), in categorizing the different types of crops of the agricultural study area (Equations (14)-(17)). Vegetation indices are calculated via simple mathematical formulas, by using information of targets' spectral responses at different wavelength intervals [106] and provide valuable, additional information about vegetation's  
Figure 3: Flowchart of Sentinel-1 data pre-processing.  
status and growth, resulting in better separability between different crop classes [107], as demonstrated by numerous crop classification studies [108]. In particular, NDVI [109] is considered the most popular vegetation index in crop monitoring and classification studies [110], being indicative of vegetation's status and photosynthetic activity [111]. NDWI proposed by [PERSON] (1996) [112], is an effective indicator of vegetation's water content [113]. PSRI, defined by [PERSON] (1999) [114] exhibits great sensitivity in the aging stage of plant development. SAVI introduced by [PERSON] (1988) [115] reduces soil effect in NDVI values [106] and is descriptive of crop's structural characteristics, such as leaf area index [116].  
\[NDVI=\frac{\rho_{NIR}-\rho_{R}}{\rho_{NIR}+\rho_{R}}=\frac{B4-B8}{B4+B8} \tag{14}\]  
\[NDWI=\frac{\rho_{G}-\rho_{NIR}}{\rho_{G}+\rho_{NIR}}=\frac{B3-B8}{B3+B8} \tag{15}\]  
\[PSRI=\frac{\rho_{R}-\rho_{G}}{\rho_{NIR}}=\frac{B4-B2}{B6} \tag{16}\]  
\[SAVI=\frac{\rho_{NIR}-\rho_{R}}{\rho_{NIR}+\rho_{R}+L}*(L+1) \tag{17}\]