# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 2 Materials and Methods
### Vegetation Index Calculation  
To map the vegetation condition of wetlands using Sentinel-2 data, a total of forty vegetation indices (VIs) were selected and calculated from S2 median mosaics covering the study area. Initially, the most commonly used spectral vegetation indices that describe various aspects of plant growth and health, such as greenness, leaf chlorophyll content, pigments, and water content in the canopy, were identified for the study. These VIs were then arranged into four groups and listed in Table 2.  
Figure 2: Locations of field measurements conducted within Biebrza National Park highlighted by red dots and presented on Sentinel-2 median mosaic comprising cloudless satellite images from June–July 2022. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Start Date** & **End Date** &
\begin{tabular}{c} **No. of Sentinel-2 Acquisition** \\ **Dates Used for Mosaic** \\ \end{tabular} \\ \hline April–May & 25 April 2022 & 12 May 2022 & 7 \\ June–July & 20 June 2022 & 7 July 2022 & 9 \\ July–August & 20 July 2022 & 4 August 2022 & 7 \\ August–September & 20 August 2022 & 8 September 2022 & 10 \\ September–October & 20 September 2022 & 28 October 2022 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sentinel-2 imaging periods used for generating cloudless median mosaics. Figure 3: Cloud area distribution during growing season in 2022 at Sentinel-2 scenes covering Biebraza, Wetlands study area (source code: [36]). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Abbreviation** & **Name** & **Equation** & **Citation** \\ \hline \multirow{6}{*}{Spectral Indices of Greenness} & \multirow{2}{*}{AFRI1600} & Aerosol Free Vegetation & \multirow{2}{*}{AFRI\(\Pi\)600 \(=\)\(R_{842}-0.66\times\frac{R_{842}}{R_{842}+0.66\times R_{840}}\)} & [37] \\ \cline{2-3}  & & Index 1600 & & \\ \cline{2-3}  & \multirow{2}{*}{ARVI} & Atmospherically Resistant & \multirow{2}{*}{ARVI \(=\)\(\frac{R_{842}-R_{865}-0.069\times(R_{842}-R_{840})}{R_{842}+R_{865}-0.069 \times(R_{840}-R_{840})}\)} & [38] \\ \cline{2-3}  & & Vegetation Index & & \\ \cline{2-3}  & \multirow{2}{*}{CTVI} & Corrected Transformed & \multirow{2}{*}{CTVI \(=\)\(\frac{\text{NDVI}+0.5}{\text{NDVI}+0.5}\times\sqrt{\left|\text{NDVI}+0.5 \right|}\)} & [39] \\ \cline{2-3}  & & \multirow{2}{*}{EVI} & Enhanced Vegetation Index & \multirow{2}{*}{EVI \(=\)\(2.5\times\frac{R_{842}-R_{845}}{R_{842}+0.1\times R_{842}-7.5\times K_{840}+1.0}\)} & [40] \\ \cline{2-3}  & \multirow{2}{*}{GDVI} & Green Difference & \multirow{2}{*}{GDVI \(=\)\(R_{842}-R_{8560}\)} & [41] \\ \cline{2-3}  & & Vegetation Index & & \\ \cline{2-3}  & \multirow{2}{*}{GI} & Greenness Index & \multirow{2}{*}{GI \(=\)\(\frac{R_{840}}{R_{846}}\)} & [42] \\ \cline{2-3}  & & & \\ \cline{2-3}  & \multirow{2}{*}{GNDVI} & Green Normalized & \multirow{2}{*}{GNDVI \(=\)\(\frac{R_{842}-R_{840}}{R_{842}+R_{840}}\)} & [43] \\ \cline{2-3}  & & Vegetation Index & & \\ \cline{2-3} \cline{2-3}  & \multirow{2}{*}{mNDVI} & Modified NDVI & \multirow{2}{*}{mNDVI \(=\)\(\frac{R_{842}-R_{845}}{R_{842}+R_{846}}\)} & [44] \\ \cline{2-3}  & & & \\ \cline{2-3} \cline{2-3}  & \multirow{2}{*}{NDVI} & Normalized Difference & \multirow{2}{*}{NDVI \(=\)\(\frac{R_{842}-R_{845}}{R_{842}+R_{846}}\)} & [45] \\ \cline{2-3}  & & Vegetation Index & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of spectral-vegetation-index-dedicated Sentinel-2 bands used for the study.