# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Preprocessing and Processing Tools  
Dealing with various RS datasets requires sufficient toolboxes and software. Several commercial and free open-source tools exist for RS image preprocessing and processing. Although great advances have been reached regarding toolbox development, a public release of processing software can further facilitate and promote experimentations. Figure 20 represents the most frequently employed tools and software for data preprocessing and processing in wetland studies (according to the reviewed literature). As shown, ArcGIS, ENVI, and Python programming packages (such as Scikit-learn, Keras, Tensorflow, PyTorch, Matplotlib, and Numpy) are the most popular and widely used tools for RS data processing when it comes to the study of wetland areas. Figure 18: Percentage of studies that use each type of feature category. Note that as almost all studies have used either optical or SAR imagery features, the category of imagery features was excluded here. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Imagery** & **Indicator/Band** & **Ref.** \\ \hline \multirow{4}{*}{Imagery Features} & Optical & blue band, red band, green band, the red-edge & - \\  & band, near-infrared, shortwave-infrared & \\ \cline{2-4}  & SAR & Backscatter Coefficient (HH, HV, VH, VV) & - \\ \cline{2-4}  & Optical & Normalized Difference Vegetation Index & [155,156,169,175,176,180] \\ \hline \multirow{4}{*}{\begin{tabular}{c} Spectral Indices ( vegetation/water/soil indices) \\ \end{tabular} } & Optical & Normalized Difference Water Index (NDWI) & [155,156,169,175,177,180,181] \\ \cline{2-4}  & Optical & Enhanced Vegetation Index (EVI) & [169,175] \\ \cline{2-4}  & Optical & Simple Ratio (SR) & [169] \\ \cline{2-4}  & Optical & Modified Normalized Difference Water Index & [177] \\ \cline{2-4}  & Optical & (NDWI) & [177] \\ \cline{2-4}  & Optical & Soil-Adjusted Vegetation Index (SAVI) & [175,176,181] \\ \cline{2-4}  & Optical & Modified Soil-Adjusted Vegetation Index-2 & [175] \\ \cline{2-4}  & Optical & (NAV2I) & [175] \\ \cline{2-4}  & Optical & Modified Normalized Difference Water Index & [175] \\ \cline{2-4}  & Optical & (NDWI) & [175] \\ \cline{2-4}  & Optical & \
\begin{tabular}{c} Tasseled-Cap Transformation Brightness \\ (TCB) \\ \end{tabular} & [175,182] \\ \cline{2-4}  & Optical & TC-Gremness (TCG) & [175,182] \\ \cline{2-4}  & Optical & TC-Witness (TCW) & [175,182] \\ \cline{2-4}  & Optical & Green Chlorophyll Index (Clgreen) & [177] \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Textural Metrics (GLCM) \\ \end{tabular} } & Optical and SAR & Mean & [169,175,178] \\ \cline{2-4}  & Optical and SAR & Variance & [169,176] \\ \cline{2-4}  & Optical and SAR & Homogeneity & [169,176,178,181,183] \\ \cline{2-4}  & Optical and SAR & Contrast & [169,176,178,181,183] \\ \cline{2-4}  & Optical and SAR & Entropy & [169,176,178,181,183] \\ \cline{2-4}  & Optical and SAR & Angular Second Moment & [169,176,183] \\ \cline{2-4}  & Optical and SAR & Correlation & [177,178,181,183] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Common features extracted from Optical and SAR imagery in wetland studies.