# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology
### Performance Validation and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Inputs**} & \multirow{2}{*}{**PALSAR-2 Class**} & \multirow{2}{*}{**GNSS-R Class**} & \multirow{2}{*}{**OW**} & \multirow{2}{*}{**FV**} & \multirow{2}{*}{**NF**} \\ \cline{3-4} \cline{6-7}  & & & & & \\ \hline \multirow{2}{*}{A} & **corrected peak SNR + SRTM90** & \multirow{2}{*}{**OW**} & 58.87\% & 18.14\% & 22.99\% \\  & DEM + GFW biomass density** & FV & 1.83\% & 53.87\% & 44.30\% \\  & & NF & 0.42\% & 5.95\% & 93.63\% \\ \hline \multirow{2}{*}{B} & **TES + SRTM90 DEM + GFW** & \multirow{2}{*}{**OW**} & 63.08\% & 15.22\% & 21.69\% \\  & & **biomass density** & FV & 1.81\% & 55.37\% & 42.82\% \\  & & NF & 0.34\% & 6.01\% & 93.64\% \\ \hline \multirow{2}{*}{C} & **corrected peak SNR + TES +** & \multirow{2}{*}{**OW**} & 64.21\% & 14.94\% & 20.85\% \\  & SRTM90 DEM + GFW biomass density & FV & 1.81\% & 58.98\% & 39.21\% \\  & & NF & 0.42\% & 5.82\% & 93.76\% \\ \hline \multirow{2}{*}{D} & **corrected peak SNR + TES +** & \multirow{2}{*}{**OW**} & 46.76\% & 16.78\% & 36.44\% \\  & SRTM90 DEM & FV & 1.92\% & 62.58\% & 35.48\% \\  & & NF & 0.51\% & 5.28\% & 94.20\% \\ \hline \multirow{2}{*}{E} & **corrected peak SNR + TES +** & \multirow{2}{*}{**OW**} & 60.26\% & 14.96\% & 24.78\% \\  & GFW biomass density & FV & 2.52\% & 58.39\% & 39.09\% \\  & NF & 0.41\% & 5.53\% & 94.06\% \\ \hline \multirow{2}{*}{F} & **corrected peak SNR + TES +** & \multirow{2}{*}{**OW**} & **65.40\%** & 13.98\% & 20.62\% \\  & + \(\Delta\) + GLO 1 + SRTM90 DEM +** & FV & 1.76\% & **60.26\%** & 37.98\% \\  & GFW biomass density & NF & 0.30\% & 4.95\% & **94.75\%** \\ \hline \multirow{2}{*}{G} & **corrected peak SNR + TES +** & \multirow{2}{*}{**OW**} & 59.11\% & 11.12\% & 29.77\% \\  & + \(\Delta\) + GLO 1 + GLO 2 + SRTM90 & FV & 1.45\% & 30.26\% & 68.29\% \\  & DEM + GFW biomass density & NF & 0.27\% & 2.26\% & 97.47\% \\ \hline \multirow{2}{*}{H} & **corrected peak SNR + TES +** & \multirow{2}{*}{**OW**} & 54.21\% & 22.07\% & 23.72\% \\  & + \(\Delta\) + GLO 1 + GLO 3 + SRTM90 & FV & 1.40\% & 50.03\% & 48.57\% \\ \cline{1-1}  & DEM + GFW biomass density & NF & 0.31\% & 4.23\% & 95.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Single DT performance assessed through PALSAR-2-based inundation reference maps for cycles 74 and 91 (end of high-flood season and beginning of high-flood season, respectively). This confusion matrix shows the classification accuracy results for different input combinations and the best results are highlighted in bold. Open Water (OW), Flooded Vegetation (FV), and Non-Flooded Vegetation (NF). Note: the results with the highest performance are marked in bold, input #F.  
Table 3F illustrates the impact of including LES, \(\Delta\), and GLO 1 to the list of inputs, together with corrected peak SNR, TES, DEM, and biomass information (Table 3C). We did not include rows in the Table for each of them because their individual impact to the classification is minimal. Instead we assessed the overall improvement associated with LES, \(\Delta\), and GLO 1 collectively (Table 3F). Even though the individual assessment is not included in Table 3, we summarize it here: LES observable did not contribute to any further separability in any class, but since it did not negatively impact classification performance it was still considered in the final classification algorithm. The observable \(\Delta\) helped improve classification of OW by about 1.5% and classification of FV by about 1.3%. Finally, there was an improvement of 0.7% for OW after adding GLO 1. Comparison of Tables 3C and 3F highlights the overall improvement in accuracy upon inclusion of these three observables, which was about +2% for OW, +1% for FV, and +1% for NF. Table 3G,H shows the effect of adding GLO 2 and GLO 3, respectively.