# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 5 Discussion
### Temporal and Spatial Evaluation  
Temporally, the Sentinel data provides a denser (\(\times\)3) time-series over Landsat that enables a better characterization of the seasonal dynamics of the water extent across the entire calendar year. Incorporating this into our algorithm results in two major advances. First, the higher revisit time of Sentinel-2 increases the chances of obtaining cloud free imagery. Secondly, Sentinel-2 may still be constrained when clouds are persistent and/or under light constraining conditions. In those cases, our algorithm utilizes cloud and light insensitive SAR data from Sentinel-1 enabling surface water detection, irrespective of cloud and light conditions. In comparison to the SWE algorithms, there are significant differences. These are most notable during winter months (cf. November to February) where clouds are more prevalent and light is a limiting factor. During this period, large parts of mainland China are not processed by the JRC-GSWE algorithm, as it includes a threshold for low sun-angles. As a result, there is no JRC-GSWE data available for the Daxhiazi validation site in December. A similar exclusion criterion is not used by the GLAD-GSWD algorithm, which nevertheless exhibits substantially lower SWE estimates (compared to GRAS-SWD) during winter, primarily due to the lack of suitable Landsat observations. Our algorithm produces a better SWE estimate due to the increased number of available optical observations and the fusion with SAR imagery, and hence, overcomes light limitations inherent in passive-based sensor systems, providing a more reliable monthly SWE estimate throughout the year. Aggregations through time facilitate the analysis of water dynamics. We defined two classes: permanent water, and seasonal water, based upon the frequency of water observations normalized by the total number of valid observations. Our comparisons with JRC-GSWE and GLAD-GSWD show a large amount of agreement for permanent water at approximately 100,000 km\({}^{2}\). Contrary to permanent water, the variation in seasonal water extent is large, varying from 200,000 km\({}^{2}\) (JRC-GSWE) to 360,000 km\({}^{2}\) (GLAD-GSWD). [PERSON] et al. [12], suggest that the JRC-GSWE underreports seasonal waters because of mischaracterization of the water regime, whilst both sources acknowledge and report lower accuracies for classifying seasonal water [11; 12]. The GRAS-SWD estimate of seasonal water falls in between JRC-GSWE and GLAD-GSWD, and a direct comparison suggests our algorithm is better at capturing seasonal dynamics, although more quantitative research is required to confirm this. The seasonal consistency of our algorithm is further underpinned by temporal profiles showing patterns of expected seasonal change in Poyang and Miyun reservoir. This is also corroborated by other studies using our algorithm to investigate spatio-temporal variation of water resources in the North China Plain [90]. Despite the convincing visual assessment, our algorithm also showed reduced performance in the seasonal strata compared to the land or water strata. We, therefore, recommend future research on quantifying the performance of surface water detection algorithms within the seasonal strata. Spatially, the 10 m resolution facilitates a better overall delineation of the land-water boundary by improving the detection of smaller inland water bodies and land features within water bodies. In comparison to JRC-GSWE and GLAD-GSWD, it is shown how most of the small farm ponds ecosystem in the Hunan province cannot be mapped at the scale of Landsat pixels. It may be somehow surprising that our algorithm, which captures more water bodies than JRC-GSWE or GLAD-GSWD, arrives at almost the same areal estimates at the national scale. However, this is explained by the fact that although small water bodies are far more numerous than larger water bodies, their cumulative extent is only a fraction of the surface water area of large water bodies [22].