## 7 Validation of Quantitative HydroSAR Information Layers
### Validating HYDRO30  
We used a quantitative approach and a qualitative approach to evaluate the performance of HYDRO30 products, including (1) a quantitative comparison of HYDRO30 with water extent information extracted from multispectral imagery and (2) a qualitative cross-comparison with water extents from a Google-developed flood forecasting model [54]. For our quantitative comparison to multispectral data, we selected near-simultaneous observations of Sentinel-1 SAR and (cloud-free) Sentinel-2 or Landsat-8 images over flood-affected regions. We selected these two multispectral sensors as references as they are of similar resolutions to Sentinel-1 and established methods for detecting surface water from their data exist. Due to persistent cloud coverage over the HKH during monsoon season, we could not perform quantitative comparisons to multispectral data over our main area of interest. Instead, we validated HYDRO30 against multispectral data for an area along the Missouri River at the corner of Nebraska, Kansas, and Missouri in the USA (Figure 11). We focused on data from 2019 for this analysis, as the midwestern US experienced a major flooding event in 2019 which was fueled by heavy rainfall and melting snow [55]. This flooding event was the longest-lasting flood since 1927 in the region, giving the opportunity to study HYDRO30's performance as the flood progressed. A total of 17 date pairs of SAR and optical (Landsat-8 and Sentinel-2) images were selected for cross-validation, with the dates falling between April and October 2019. These data pairs were selected based on a few factors. Firstly, we required that the SAR and optical images were acquired within a 5 day window of each other. This ensured that both datasets measured comparable surface water extents. Secondly, we required that the optical images contained less than 20% cloud cover in order to reduce discrepancies based on cloud cover and cloud shadows. We derived the water extent from Sentinel-1 using the HYDRO30 algorithm and from multispectral data using the established modified normalized difference water index (mNDWI) [56] approach. We compared the water extent information from both sensor types and calculated the skill scores to evaluate HYDRO30's products. Our skill scores included accuracy (\(A=((TP+TN))/T\)) and precision (\(P=TP/(TP+FP)\)), with \(TP\) meaning true positive, \(TN\) meaning true negative, \(FP\) meaning false positive, \(FN\) meaning false negative, and \(T\) meaning the total population. An example of this approach is shown in Figure 12. Here, the HYDRO30 water extent information is shown in the left panel, while the water extent mapped from optical data using the nNDWI is shown in the center. The right panel is a comparison of the produced water extent information, with blue areas indicating agreement, red regions indicating missed detections by HYDRO30, and light blue areas indicate water detections in HYDRO30 which were not found in the optical data. The example in Figure 12 demonstrates that HYDRO30 typically matched optical reference data with high accuracy (\(A=98\%\) and \(P=86\%\)). Additional examples of Sentinel-1 (S1) and optical image comparisons are presented in Table 1, showing overall consistent water mapping skills. Throughout the 17 data pairs in our analysis, we saw an average accuracy of \(A=99\%\) and average precision of \(P=79\%\). As cloud cover limits the usability of multispectral images during most of the HKH monsoon season, we were unable to perform a quantitative comparison to multispectral data for this region. Instead, we augmented our quantitative analysis along the Missouri River with a qualitative comparison to Google-provided flood forecasts which are available for some parts of the HKH. Figure 13a shows a comparison of the HYDRO30 data (blue) and Google flood forecasts (hash pattern) for the Kameng River in India. Both the SAR and forecast data stemmed from 26 June 2021.