# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology
### Performance Validation and Results  
In a trade assessment, we first determine the GNSS-R observables and ancillary datasets associated with improving our inundation classification. We discard those observables that diminish accuracy. Here, we assessed the classification accuracy of each individual observable for the three classes defined (Open Water (OW), Flooded Vegetation (FV), and Non-Flooded Vegetation (NF)). We built a single decision tree (DT) from each observable and one or two of the ancillary datasets (DEM and biomass). Each single DT was constructed following a standard CART based classification algorithm [54]. Figure 8 explains the methodology followed to train and validate a single DT. Figure 8 shows the methodology followed to train and validate the single DTs built from the GNSS-R observables and ancillary datasets listed in Table 3. We started by building the classification algorithm with the most commonly used observables (corrected peak SNR and TES) and the ancillary datasets (DEM and biomass). We then assessed additional observables and identified those that improved the classification accuracy. The single DTs were trained (using cycles 71, 77, 79, 83, 85, 88, and 93), and then their performance was assessed using independent data from cycles 74 and 91 (end of high-flood season and beginning of high-flood season, respectively). Various combinations of inputs (corrected peak SNR, TES, LES, \(\Delta\), GLO 1, GLO 2, GLO 3, SRTM90 DEM, GFW AGB) were tested, with the aim of assessing the impact of each one on classification performance. Confusion matrices summarizing the results of each of these iterations are shown in Table 3, focusing on three classes: OW, FV, and NF. We first investigated corrected peak SNR since it is the most widely used observable, as in References [30; 32], along with DEM and biomass information. Table 3A shows the performance of a single DT built using corrected peak SNR + SRTM90 DEM + GFW biomass density as inputs. Table 3A shows that corrected peak SNR provided high classification accuracy for the NF class (93.63%) but low accuracy for both OW and FV (both \(<\)60%). The next step was to investigate the TES observable along with the DEM and biomass ancillary datasets, as summarized in Table 3B. Results indicate that the TES observable improved the classification of OW by approximately 4% and FV by about 1.5 % when compared to corrected peak SNR. We then assessed classification accuracy with both corrected peak SNR and TES observables, together with the two ancillary datasets. The results are summarized in Table 3C, and indicate that a higher classification accuracy was achieved than with any of the previous combinations of inputs (Table 3A,B). OW was classified with 64% accuracy, FV with 59%, and NF with 93%, resulting in a 5.3% increase for OW and 5% for FV as compared to Table 3A,B. Next, we analyzed the contribution of the ancillary datasets towards classification accuracy. Table 3D,E shows the classification accuracy results using both corrected peak SNR and TES and a single ancillary dataset: SRTM90 DEM and then biomass density, respectively. A comparison of Table 3D with Table 3E shows that the SRTM90 DEM contributed overall less to classification accuracy than biomass. For example, classification accuracy of OW was 14% higher with biomass than with the SRTM90 DEM. The low contribution of the SRTM90 DEM was likely because the Pacaya-Samiria region is relatively flat, however, its inclusion is important in areas with large topographic variability.