# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 4 Results
### Classification Accuracy  
Table 1 lists the overall classification accuracy for each of the five sites. Overall, accuracies are satisfactory for all sites (>90%), with users and producers' accuracies generally near or above 90%. The one exception is Daxihaizi during winter. While OA remains high (>95%), water PA is markedly lower (79%) compared to the other locations and months. This may be explained by the fact that the Daxihaizi reservoir is shallow and exhibits seasonal changes controlled by upstream discharge. As a result, the classification may be impacted by land-water mixing at the pixel scale and stronger bottom reflectance from shallow waters. Around two-thirds (68%) of the misclassified water pixels were from the seasonal strata, and one-third (33%) was mixed pixels containing both land and water according to the validation. Furthermore, a local threshold optimization could offer improvements since around half (54%) of the incorrectly labeled water pixels have a GRAS-SWD water probability greater than 20%. The seasonal variation in the accuracies is also apparent in the Yellow River Upstream location, albeit to a lesser extent. Here the performance is reduced by 3 percentage points in winter (cf. month 11) for the overall accuracy (Table 1). This lower accuracy is likely caused by the presence of ice. According to the validation, 75% of the water samples contained some fraction of ice, and yet the accuracy is not substantially diminished. This could be explained by the fact that we did not use a dedicated ice mask, but rather chose to consider ice as water in the binary classification. More generally, we note that the Yellow River validation site was considerably complex, with very limited vegetation cover and wetland characteristics, including many small and shallow water pools, whose extents vary according to upstream discharge and seasonality. This may explain the lower overall accuracies here. The only other site with notable presence of ice is Sanggan (44% of the water samples impacted by ice) and yet this site ranks third in terms of overall accuracy and also performs well at the class level. Aggregating all the samples across the sites results in an overall accuracy of 94%, with lower overall and per class accuracies observed in the seasonal water strata (Table 2). This is to be expected since the seasonal class is the most dynamic. It is also arguably the most important strata to ensure accuracy in since it encompasses the land-water boundary. The lowest scoring metric appears to be water PA, which may suggest that the total water extent is underestimated, however the UA is arguably more important, especially when aggregating through time. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Sample Locations** & **Validation Date** & **Gaofen Sensor** & **Ice** & **OA** & Land UA & Land PA & Water UA & Water PA \\ \hline Guisman & 22-90-2019 & GF-1 & no & 99\% & 99\% & 99\% & 98\% & 97\% \\ Yellow River Upstream & 13-11-2019 & GF-6 & yes & 90\% & 88\% & 89\% & 91\% & 90\% \\ Yellow River Upstream & 10-48-2019 & GF-1 & no & 93\% & 90\% & 93\% & 95\% & 92\% \\ \hline Xijang & 27-01-2019 & GF-1 & no & 93\% & 93\% & 95\% & 94\% & 91\% \\ \hline Daxihaizi & 20-12-2019 & GF-1 & no & 95\% & 96\% & 98\% & 88\% & 97\% \\ \hline Daxihaizi & 14-06-2019 & GF-2 & no & 98\% & 98\% & 99\% & 93\% & 82\% \\ \hline Sanggan & 18-03-2019 & GF-6 & yes & 97\% & 90\% & 97\% & 95\% & 92\% \\ \hline \end{tabular}
\end{table} 
Table 1: Classification accuracies (%) displayed as a heat map for each of the five validation sites. OA = Overall accuracy, UA = User’s accuracy, PA = Producer’s accuracy.