# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology
### Performance Validation and Results
In comparing Tables 3G and 3H to Table 3F, observables GLO 2 and GLO 3 added ambiguity to the results and were not likely to be sensitive to inundation, since adding them worsened the classification performance (-10% for the OW class in both cases, and -10-30% for FV). In Section 3.1, we stated our assumption that the GLO 1 observable would be able to capture most of the information related to inundation conditions, and that it would be the factor most strongly affecting the shape and strength of the measurement. The classification results in Tables 3F, 3G and 3H for observables GLO 1, GLO 2, and GLO 3 respectively, show that the dependence of the signal on inundation was well captured by the first principal component alone. Consequently, GLO2 and GLO3 were not selected in the final classification algorithm. In summary, we trained single DT's on different combinations of observables and ancillary datasets and assessed the performance of those against the PALSAR-2-based inundation reference maps for  
Figure 8: Flowchart showing the single decision tree (single DT) methodology based on classification trees built from standard CART [54]. In the top, the training samples for the set of GNSS-R observables defined in Table 3A to Table 3H, the ancillary datasets (aboveground biomass, elevation), and the PALSAR-2-based reference inundation maps were used to create a single DT for each row in Table 3. In the bottom, the validation samples for the same set of GNSS-R observables and ancillary datasets (aboveground biomass, elevation) were input to the trained Single DT to create inundation classification maps. The output was then compared to the PALSAR-2-based reference validation inundation maps to assess the accuracy of the classification results. cycles 74 and 91. We started with the most commonly used observables (corrected peak SNR and TES) and ancillary datasets (DEM and biomass), and we then added additional observables, retaining only those that increased classification accuracy. The results in Table 3A-H indicate that adding TES, LES, \(\Delta\), and GLO 1 to peak SNR improved the classification accuracy for OW and FV. Overall, NF areas were classified with high accuracy (\(\geq\)93%). Misclassifications of OW and FV as NF most likely occurred because of the surface conditions of those particular observations. A lower percentage of OW or FV within the scattering area caused the scattering to become more incoherent, and the power level, along with other observable parameters, become closer to NF. A comparison of Table 3A (corrected peak SNR + SRTM90 DEM + GFW biomass density) and Table 3F (corrected peak SNR + TES + LES + \(\Delta\) + GLO 1 + SRTM90 DEM + GFW biomass density) indicates that, for the latter, there was an overall improvement in classification accuracy of 7.4% for OW and 6.5% for FV, illustrating the value of using multiple observables in the classification algorithm. After exploring the contribution of the different observables towards the performance of the single DTs, we implemented the MDTR algorithm, described in Section 3.2, with the selected inputs derived from this explorative study (Table 3F): corrected peak SNR, TES, LES, \(\Delta\), GLO 1, SRTM90 DEM, and GFW biomass density, following the flowchart in Figure 4. Figure 9 and Table 4 summarize the results of our GNSS-R inundation classification obtained from the MDTR algorithm. Figure 9: Inundation classification of Pacaya-Samiria using the MDTR algorithm. In the top row, PALSAR-2-based inundation maps used as reference for (**a**) cycle 74 during the end of the high-flood season and (**b**) for cycle 91 during the end of the low-flood season. In the bottom row, GNSS-R inundation classification results for the same seasonal cycles: (**c**) cycle 74 and (**d**) cycle 91. For display purposes only, we are showing the CYGNSS results at approximately 3 km. Figure 10 shows that most of the misclassifications occurred around the areas of flooded vegetation.