# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 5 Discussion
### Accuracy  
Validation of the algorithm showed good performance (94% OA) across a diversity of ecoregions, and over landscapes influenced by several of the known challenges for satellite-based surface water mapping, including topography, dense vegetation, clouds, low sun angles, low backscatter land covers, as well as snow and ice. The effect of topography is manyfold including image distortion, deformation, and impact on signal values. In order to reduce these effects a DEM is required. Most studies until recently have relied upon using the Shuttle Radar Topography Mission (SRTM) DEM [84]. However, there are some known quality issues with the SRTM DEM, including the reference year 2000 [85]. Our algorithm therefore used a new enhanced DEM (cf. GLO-30 DEM), which we suspect, although this was not directly quantified, helped us achieve high accuracies in mountainous regions (cf. the validation sites in Xijiang and Guxian). Permanently low backscatter areas (flat impervious areas and dry sandy surfaces) can pose a challenge for SAR-based water detection [86], such as in the Daxihazi validation  
Figure 10: Land cover distribution [83] (**a**) and counts of small water bodies (<0.81 ha) within 10 \(\times\) 10 km grid cells over Hunan province by GRAS-SWD (**b**); JRC-GSWE (**c**) and GLAD-GSWD (**d**). site, which is situated in the desert biome. As a mitigation, and to maintain high mapping accuracies in such environments, our algorithm takes a conservative approach by excluding SAR if there is a low backscatter and an absence of optical-derived water in the previous three months. Despite this conservative approach SAR offers the benefits of filling the temporal observations gaps caused by clouds and light restrictions, which is most notably demonstrated by the increased water detection during winter months compared to JRC-GSWE and GLAD-GSWD, and the ability to accurately map permanent small water bodies in the cloud prone Hunan province. The cost of using a SAR exclusion mask is the reduced ability of our algorithm to detect short-lived floods in their entirety. Still, and as demonstrated in the case of Poyang Lake, the water dynamics, incl. timing and extent of floods, are well captured. This is a significant improvement, as many studies limit their investigations to the dry season due to cloud cover [87]. Snow and ice represent a special challenge as they are both part of the hydrological cycle and therefore need specific considerations. Snow for the most part forms on the land surface, whereas ice represents the frozen state of inland water bodies. For that reason, our approach masks snow as part of the pre-processing, and indirectly considers ice as water in the sense that we did not attempt to include a specific ice predictor as part of the classification model. Two of the validation sites (Yellow River Upstream and Sanggan) had a substantial presence of ice during winter and we found that it did not significantly reduce accuracies when considered as water. Classifying ice as water can of course be disputed or unwanted for certain applications, and a future development could be to expand our algorithm to a multi-class classification with separate predictions of water and ice, or explicitly remove ice. There are multiple other challenges for surface water detection and not all were evaluated in the current study. Our results may include uncertainties due to dense inundated vegetation, which is a known problem for both optical and SAR based water detection. Nonetheless, the occurrence of inundated vegetation is not expected to be a dominant issue at the scale of mainland China and why it is unlikely to have a significant impact on our results. Strong winds are also known to influence the SAR signal by roughening the water surface and making the contrast between land and water less discernable. While not directly addressed, we believe the influence of wind to be relatively minor as we use multi-temporal compositing rather than single SAR acquisitions, which are more susceptible to wind effects.