## 5 Conclusions and Perspectives  
This study presents a methodology to monitor and quantify surface water under all weather conditions within Cambodia and the Mekong Delta in Vietnam, using high quality Sentinel-1 SAR observations, freely available online. The methodology is based on a neural network classification trained with optical Landsat-8 images at 30 m spatial resolution. The information content of each satellite input is analyzed and the inputs are selected to optimize the performance of the classification. This method allows the detection of surface water with good accuracy when compared to visible and NIR data under clear sky conditions, as well as when compared to a floodability map derived from topography data. Surface water maps derived from the proposed NN show a spatial correlation of \(\sim\)90% when compared to Landsat-8 water maps, with a true positive water detection of \(\sim\)90%. Compared to MODIS/Terra water maps over the Delta in 2015, our products share the same wetland seasonal cycle and dynamics, with a temporal correlation of \(\sim\)99%.  
In the future, we will first apply the method to other areas under similar environments in southeast Asia and in other parts of the globe, and second we will test it in more vegetated environments. The final goal is to develop a general method capable of performing at the global scale to exploit the  
Figure 12: (**a,c**) SAR and (**b,d**) MODIS surface water maps at 500 m resolution over the Mekong Delta in May (**a,b**) and October (**c,d**) 2015.  
full spatial coverage of the Sentinel-1 mission. For this purpose, several approaches will be tested to improve the retrieval scheme. First, the introduction of a priori information from a topography-based floodability index will increase information on flooding and reduce ambiguities in the SAR signal with other surface parameters. Second, with the launch of the optical Sentinel-2 satellite, Sentinel-2 observations could be used to replace Landsat-8 data, and to train the SAR surface water classification under clear sky conditions. The classification could then be extended to the cloudy areas using the SAR data. Third, the temporal information in the SAR backscatter could also be exploited (i.e., minimum or standard deviation of the time series) as this information has been shown to improve the detection of floods [13]. Finally, the high-resolution inundation extent retrieval maps could be post-processed in order to reduce the inherent noise in such high-spatial retrievals. We plan to test random walk techniques for that purpose.  
We would like to thank [PERSON] for proving the methodology to derive the MODIS 500 m surface water maps. We are thankful to [PERSON] for interesting discussions and for sharing some of his tools in the framework of his ESA-funded postdoc (CCI Living Planet Fellowship AO/1-7829/14/I-MB). We also thank [PERSON], [PERSON], [PERSON], and [PERSON] for interesting discussions and suggestions for this work. This study was financially supported by a PhD fellowship from the Vietnam International Education Development (911 project). We would like to thank three anonymous reviewers for their helpful comments and suggestions that helped to improve the quality of this manuscript.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper. All authors contributed to the discussion of results and the preparation of the manuscript. The authors declare no conflict of interest.