## 2 Materials and Methods
### Supervised Classification with Random Forest Algorithm  
For the supervised classifications, we used the random forest (RF) algorithm [45], an ensemble method that uses random subsets of a dataset (selected with replacement, a process called bagging) to train and grow multiple individual decision trees (classification and regression trees). During the training phase, each decision tree is grown independently, using different random subsets of the predictors and points (pixels). At each splitting node a limited, user-defined number of input predictors (_mtry_) is used. Each tree is thus trained to predict (vote) the class of new data based on the same predictors it was trained with. The majority of the votes in the forests determines the final class prediction of the RF. Random forests also take advantage of the subsets of data that were not bagged (out-of-bag samples) to validate model performance internally, using the trees to make predictions on the out-of-bag samples and obtain the out-of-bag error of the forest. This is especially useful for calculating variable importance measures and for variable selection approaches [46]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predictors** & **Sensor (Reference)** & **Description** \\ \hline Band 2—Blue (B) & Sentinel-2 & Native resolution: 10 m; Central wavelength: 490 nm \\ Band 3—Green (G) & Sentinel-2 & Native resolution: 10 m; Central wavelength: 560 nm \\ Band 4—Red (R) & Sentinel-2 & Native resolution: 10 m; Central wavelength: 665 nm \\ Band 5—Vegetation Red Edge (Re1) & Sentinel-2 & Native resolution: 20 m; Central wavelength: 705 nm \\ Band 6—Vegetation Red Edge (Re2) & Sentinel-2 & Native resolution: 20 m; Central wavelength: 740 nm \\ Band 7—Vegetation Red Edge (Re3) & Sentinel-2 & Native resolution: 20 m; Central wavelength: 783 nm \\ Band 8—Near Infrared (NIR) & Sentinel-2 & Native resolution: 10 m; Central wavelength: 842 nm \\ Band 8—Near Infrared narrow (NIRR) & Sentinel-2 & Native resolution: 20 m; Central wavelength: 865 nm \\ Band 11—Short-wave infrared (SWIR1) & Sentinel-2 & Native resolution: 20 m; Central wavelength: 1610 nm \\ Band 12—Short-wave infrared (SWIR2) & Sentinel-2 & Native resolution: 20 m; Central wavelength: 2190 nm \\ NDWI—Normalised Vegetation Water Index & Sentinel-2 [73] & (G - NIR)/(G + NIR) \\ NDWI—model Normalized Difference Water Index & Sentinel-2 [76] & (G - SWIR)/(G + SWIR1) \\ NDMI—Normalised Difference Moisture Index & Sentinel-2 [77] & (NIR - SWIR1)/(NIR + SWIR1) \\ nNDMI—narrow Normalised Difference Moisture Index & Sentinel-2 [78] & (NIRn - SWIR1)/(NIRn + SWIR1) \\ NDVI—Normalised Difference Vegetation Index & Sentinel-2 [79] & (NIR - R)/(NIR + R) \\ MSAVI Version 2—Modified Soil-Adjusted Vegetation Index & Sentinel-2 [80,81] & (NIR + - (NIR + R)/2 - (NIR - R))/2 \\ RESI—Red-edge Normalised Index & Sentinel-2 [82] & (Re3 + Re2 – Re1) \\ BSI—Bare Sediment Index & Sentinel-2 [83] & (SWIR1 + R) - (NIR + B)/(SWIR1 + R) + (NIR + B)) \\ GSI—Topsoil Grain Size Index & Sentinel-2 [84] & (R + B)/(R + B + G) \\ S1-VV polarisation—vertical transmit, vertical receive & Sentinel-1 & Native resolution: 5 m \(\times\) 20 m, resampled to 10 m \(\times\) 10 m \\ S1-VH cross polarisation—vertical transmit, vertical receive & Sentinel-1 & Native resolution: 5 m \(\times\) 20 m, resampled to 10 m \(\times\) 10 m \\ RVI—Radar Vegetation Index & Sentinel-1 & \(4\sigma\
u_{\rm H}\)\({}^{0}\)/(\(\sigma\
u_{\rm V}\)\({}^{0}\) + \(\sigma\
u_{\rm H}\)\({}^{0}\)) \\ S1-VH/VV—Radar Cross-polarisation ratio & Sentinel-1 & \(\sigma\
u_{\rm H}\)\({}^{0}\)/\(\sigma\
u_{\rm V}\)\({}^{0}\) \\ Digital Elevation Model (DEM) & Sentinel-2 [69] & Including heights ranging 1.65–4.69 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of predictors considered for the supervised classifications, including a selection of relevant Sentinel-2 spectral bands, Sentinel-1 Synthetic Aperture Radar VV and VH polarisation bands, a selection of indexes derived from both sensors, and the digital elevation model produced in this study.