## 4 HydroSAR Product Portfolio and Product Algorithms
### Quantitative HydroSAR Flood Information Products  
#### 4.3.1 HYDRO30: Surface Water Extent  
Our 30 m surface water extent product, HYDRO30, is a modified version of an algorithm published in [47] and performs adaptive thresholding of dual-pol RTC30 images to derive the surface water extent. A HAND layer derived from the Copernicus GLO-30 DEM is included to improve robustness. The HYDRO30 algorithm includes the following processing steps, which are also visualized in Figure 6.  
**Step 1: Preparing Input Data. ** We used the algorithms described in Sections 4.2.1 and 4.2.2 to generate the HAND and RTC30 product layers needed for surface water threshold calculation. Due to the large data volumes associated with this regional scale monitoring effort, we performed these operations in the cloud-based processing environment (Section 6) which hosts HydroSAR. HAND data and RTC30 products were generated at a 30 m resolution and in CoG formats. **Steps 2 and 3: Adaptive Threshold Calculation. ** We used a modified version of the adaptive thresholding approach in [12] to identify surface water candidate pixels from the RTC30 input data. Information about the modifications we applied relative to [12] along with their motivation are provided in the individual processing step descriptions below. Like in [12], we utilized a bi-level quadtree tile structure to ensure robust threshold calculation. We first divided the scenes into a parent level structure with a tile size of \(100\times 100\) pixels. Each parent tile was subsequently subdivided into \(2\times 2\) child objects of \(50\times 50\) pixels each. Once this structure was created, we used the following three criteria to select the subset of parent tiles most suitable for automatic threshold calculation:  
Figure 6: Summary of the HydroSAR HYDRO30 workflow, consisting of RTC generation (see Section 4.2.2), threshold calculation, selection of candidate water pixels, a post-processing refinement step, permanent and flood water discrimination, and product dissemination. * For each parent tile, we calculated the coefficient of variation (\(CV\)) of the mean radar brightness values of its four child objects. This is a departure from [12], who used the standard deviation as a metric. We found \(CV\) to be more robust across regions with different average radar brightness. Parent tiles with high \(CV\) values are potential candidates for threshold calculation, as a high \(CV\) is expected for tiles which contain both the water and land semantic classes. Tiles which offer the highest (\(>\)95% percentile) \(CV\) were selected as candidates. * We required the parent objects to have a mean radar brightness lower than the mean of all parent tiles. This ensured that tiles lying on the boundary between water and land areas were selected. * To improve the robustness of the threshold calculation, we excluded parent tiles which were not in flood-prone regions. To accomplish this, we labeled the pixels with HAND elevations \(\leq\)15 m as unlikely to be flooded. This threshold was based on studies which found that average flood depths of up to 10 m are possible in the region [48]. We added an additional buffer of 5 m to ensure that most tiles which could include flood water were retained for further analysis. This is a second departure from [12], where the DEM instead of the HAND was used to identify tiles which could be discarded. As the HAND is a hydrologically conditioned dataset, we found it to be a more suitable and robust variable to identify tiles which were not in flood-prone regions. Tiles were only considered if less than 20% of their pixels were identified as not being flood-prone. We selected the \(N=5\) best tiles for threshold calculation, using the \(CV\) of the mean radar brightness values as a ranking metric. We calculated the threshold values for each of these \(N=5\) tiles using expectation maximization [11] and defined the best scene-wide classification threshold \(\tau_{g}\) as the arithmetic mean of the five individual thresholds.