## 2 Materials and Methods
### Evaluation of Classification Performance  
Similar to previous attempts to map inundation across the extent of the Amazon Basin [9; 11; 12], a conventional accuracy assessment was not possible because collecting representative ground-truth data over such a large and challenging landscape is unfeasible. Equally, unlike other studies mapping inundation within herbaceous wetlands, we could not use higher-resolution images to act as pseudo ground-truth points [55] as the dense forest canopy masks the ground beneath in optical images and higher-resolution radar image data (i.e., C-band and X-band imagery). Rather, we conducted an evaluation of the RadWet-L classified outputs by examining a set of random points against the original input ALOS-2 PALSAR-2 imagery. Interpretation of whether the classification was correct or not was guided by our knowledge of inundation patterns across the basin, alongside our field observations from previous work [28].  
This evaluation was applied to twelve 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) ALOS-2 PALSAR-2 tiles, distributed across the study area, each acquired at different times during the hydrological year. Each tile was populated with 1000 random points. Where points were distributed in areas of no data those points were removed, creating a total visual evaluation dataset of 11,162 points:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Predictor Variable** & **Inundated Forest** & **Open Water** \\ \hline HH Mean & **1.11** & **1.29** \\ HH Std Dev & 0.31 & 0.24 \\ HV Mean & **0.76** & **1.28** \\ HV Std Dev & 0.28 & 0.49 \\ NPDI Mean & **1.20** & **1.08** \\ NPDI Std Dev & 0.94 & **0.98** \\ Incidence Angle Mean & **0.20** & 0.24 \\ Incidence Angle Std Dev & 0.44 & **0.63** \\ Slope Mean & 0.37 & **0.46** \\ Slope Std Dev & 0.39 & **0.48** \\ HAND Mean & 0.20 & 0.14 \\ HAND Std Dev & 0.41 & **0.24** \\ \hline \hline \end{tabular}
\end{table}
Table 2: JM-Distances for each classification predictor variable. Variables used as independent variables in the classifier are shown in bold.  
1462 points for Inundated Forest, 569 points for Open Water, and 9131 points for other landcover.  
Subsequent data on the number of false/true positives/negatives was used to calculate accuracy assessment metrics per class. Confidence intervals of accuracy assessment metrics were generated following the bootstrapping method of [PERSON] et al. [55] and [PERSON] et al. [56], whereby 1000 iterations, selecting 50% of the total validation dataset at each iteration, were used to estimate the variance of the classification evaluation statistics. An area-based confidence interval for open water and inundated forest was then defined for each class using errors of commission and omission with the following equations from [PERSON] et al. [55]:  
\[\text{area}_{\text{i}}95\text{CI}_{\text{Lower}}=\text{area}_{\text{i}}-( \text{area}_{\text{i}}\times\text{Comission P}_{95})\]  
\[\text{area}_{\text{i}}95\text{CI}_{\text{Upper}}=\text{area}_{\text{i}}+( \text{area}_{\text{i}}\times\text{Omission P}_{95})\]  
This information was subsequently used to report estimated inundation extents with their associated confidence range (95 th percentile).  
To supplement this evaluation procedure, we also conducted comparisons of RadWet-L time series estimates of inundation extent against other products that are indicative of inundation at the basin and sub-basin scale (detailed in Sections 2.10.1-2.10.4).