## 4 Discussion  
RadWet-L is an automated algorithm that can be applied to a time series of imagery and therefore can be used to reconstruct inundation extent history between or within a hydrological year, including assessments of inundation extent in both wet and dry conditions. For the Amazon Basin, we found that we were able to detect temporal variations in inundation extent over the 2019-2022 study period that are closely related to other measurements, such as NASA-GFZ GRACE-FO's water equivalent thickness. This indicates that RadWet-L could be used to detect specific responses to climatological/hydrological events that affect the hydrological regime of the Amazon and its sub-basins, such as strong El Nino and La Nina events. The classification evaluation reported an overall agreement of 97% (95 th Conf Int: 96.5-97.4) when compared against the input ALOS-2 PALSAR-2 ScanSAR imagery. The majority of errors were found to be a result of false negatives, specifically, areas of inundated forest being wrongly classified as other landcover (i.e., not inundated). These were generally observed at the boundary between areas of inundation and non-inundation. Here, mixed pixels result in a weakening of the double-bounce backscatter signal. In many instances, human interpretation could identify these areas as inundated forest, but this interpretation relied on contextual information, such as proximity to a river channel/ributary or the general shape and landscape of where the inundated forest feature is located. This was particularly the case for long thin features representing small river channels. This contextual knowledge cannot be readily included in the XGBoost classifier used in RadWet-L. Future work should consider the use of Convolutional Neural Network (CNN) deep learning classifiers that are better suited to capturing contextual information, potentially leading to improved classification performance [64]. However, it is also important to acknowledge and measure the apparent cost-benefit of employing more computationally demanding data analysis approaches and aligning this with our responsibility to provide energy-efficient solutions [65]. Nevertheless, given their complexity, wetland classification maps are unlikely to be perfect and it is therefore important to properly measure and quantify confidence. Following the approach used in the evaluation of the Global Mangrove Watch products [55],  
Figure 16: Comparison between ESA GlobCover [63] (**top**), the Global Lakes and Wetlands Database [61; 62] (**centre**), and RadWet-L Inundation Frequency (2019–2023) (**bottom**). we calculated accuracy confidence intervals. In doing so, users can interpret RadWet-L-generated maps and data products in the context of the supplied accuracy scores and related confidence intervals. We recommend that this becomes common practice for remote sensing analysts. RadWet-L was applied to ALOS-2 PALSAR-2 ScanSAR imagery that was acquired with a nominal revisit period of 42 days. This temporal granularity is likely to be less suitable for capturing fluctuations in inundation in more rapidly responding catchments, particularly those in drier environments where Hortionian overland flow predominates (i.e., rainfall is less likely to infiltrate the soil and more likely to rapidly move into stream and river channels) leading to flashy responses to rainfall events. Additionally, the 42-day granularity means that for most years, the peak high/low water extent for a specific region may not exactly coincide with an ALOS-2 PALSAR-2 overpass. This is likely to account for some differences in the date of high/low water peaks with the monthly observations by GRACE-FO. Deploying RadWet-L on data from the upcoming (planned launch in 2024) NASA-ISRO NISAR (L-band and S-band) mission, with a 12-day revisit time, would potentially allow for these dynamics to be captured more accurately. Some satellite observation systems offer higher temporal frequency mapping of inundation extent.