# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology
### Datasets  
The reference for training and validation used in the MDTR was obtained from PALSAR-2 ScanSAR mosaics that were composited into 14 day cycles, listed in Table 2, and used to derive the inundation maps [10]. Six of the cycles listed above (71, 77, 79, 83, 85, 88, and 93) were used to train the MDTR algorithm, and two of those cycles (74--end of the high-flood season, and 91--beginning of the high-flood season) were used to evaluate the results. The PALSAR-2-based reference inundation maps were resampled from their original 50 m \(\times\) 50 m resolution to 0.005\({}^{\circ}\)\(\times\) 0.005\({}^{\circ}\) (~500 m \(\times\) 500 m) to match the CYGNSS grid. Figure 4 shows a false color composite of the PALSAR-2 mosaic corresponding to cycle 74 (8 May to 21 May, end of the high flood season), and the derived PALSAR-2 inundation classification that we used as reference to train the MDTR algorithm. The accuracy of the PALSAR-2 reference maps was determined to be 91.8% [10]. The PALSAR-2 derived inundation classifications had seven classes defined (no data, open water, flooded low vegetation, flooded high vegetation, non-flooded vegetation, bare/artificial surface, and masked areas). In this study, we condensed those seven classes into four: (1) Flooded Vegetation (includes high and low flooded vegetation), (2) Open Water, (3) Non-Flooded vegetation, and (4) Masked areas and regions with no data coverage. CYGNSS observables were averaged within each corresponding 14 day cycle (Table 2). Figure 5 illustrates two of the observables, DDM corrected peak SNR and GLO 1, for cycles 77 and 93, along with the contemporaneous PALSAR-2-based reference inundation classification maps used for training the MDTR algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cycle** & **Start Date** & **End Date** \\ \hline
071 & Mar. 27, 2017 & Apr. 09, 2017 \\
074 & May 08, 2017 & May 21, 2017 \\
077 & Jun. 19, 2017 & Jul. 02, 2017 \\
079 & Jul. 17, 2017 & Jul. 30, 2017 \\
082 & Aug. 28, 2017 & Sep. 10, 2017 \\
085 & Oct. 09, 2017 & Oct. 22, 2017 \\
088 & Nov. 20, 2017 & Dec. 03, 2017 \\
091 & Jan. 01, 2018 & Jan. 14, 2018 \\
093 & Feb. 12, 2018 & Feb. 25, 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acquisition dates for the PALSAR-2-based reference inundation classification maps. Figure 4: **(a)** False color composite of the PALSAR-2 mosaic for Cycle 74 (May 08 to May 21, end of the high flood season) where R: HH polarization, G: HV polarization, and B: HH/HV polarization ratio, provided by Reference [10], note R = Red, G = Green, B = Blue, H = Horizontal, V = Vertical. **(b)** PALSAR-2 derived inundation classification for cycle 74. The inundation classification was originally derived with seven classes [10]. For this study, those classes were condensed to four (1) Flooded Vegetation, (2) Open Water, (3) Non-Flooded vegetation, and (4) areas that were Masked or for which there was no data. Figure 5: Seasonal comparison. Left column shows two examples of the modified PALSAR-2-based reference inundation classification maps for **(a)** the end of the high-flood season during cycle 77, and **(d)** the end of the low-flood season during cycle 93. Center column shows the same seasonal comparison for the CYGNSS observable corrected peak SNR during **(b)** cycle 77 and **(e)** cycle 93. Right column shows the seasonal comparison for the CYGNSS observable GLO 1 during **(c)** cycle 77 and **(f)** cycle 93. Plots **(b,c,e,f)** are in logarithmic scale (dB). All of the analysis was done at the first Fresnel zone gridded at \(500\)\(\mathrm{m}\times 500\)\(\mathrm{m}\). For display purposes only, this Figure shows the CYGNSS observables at approximately 3 km. The two cycles selected correspond to a 14-day window (see Table 2) in late June and early July showing high-flood, and a 14 day window in mid-late February showing low-flood. Differences in both peak SNR and GLO 1 can be seen between the two seasons, with higher peak SNR and GLO values during the high-flood than the low-flood period.