# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 6 Conclusions  
To date, most applications for surface water monitoring have tended to rely on either optical or SAR images, and only few studies have explored the advantages of a multi-sensor approach. From independent reference data and by comparison with existing global maps, we demonstrate how the fusion of optical and SAR data brings new enhanced information about the surface water dynamics in mainland China. We have demonstrated the value of using higher resolution (10 m) and denser time-series to identify previously unmapped water bodies consistently across years and seasons. This improved monitoring capacity has important management and planning implications in China, but also beyond. When world leaders adopted the SDGs, they also pledged to develop and support a global indicator framework needed to measure and report progress on achieving the 17 SDGs and their 169 associated targets. Our mapping approach is consistent with the monitoring requirements and methodological guidelines for SDG indicator 6.6.1 \"Change in the extent of water-related ecosystems over time\" [95]. With the Sentinel satellite constellation, data has continued to improve greatly, and combined with the advances in technical infrastructures for big data analysis, it is now within the realm of countries to implement national EO based surface water monitoring systems to support more evidence-based planning and management of water resources and for SDG reporting. It is worth remembering that the SDG guidelines operate with a 2001-2005 baseline period, hence the historic Landsat records still hold invaluable information. Thus, there is a strong incentive and justification for the continued research into the usage of Landsat and its continuation to the Sentinels (and the increasing amount of other new sensors) for tracking long-term surface water dynamics.  
Conceptualization, D.D., X.T, and C.T.; methodology, D.D, X.T., C.M.M.K., K.G and, C.T.; software, D.D.; validation, X.L. and T.G.; formal analysis, D.D., and X.T; investigation, D.D., and X.T.; data curation, X.L. and T.G.; writing--original draft preparation, D.D. and C.T.; writing--review and editing, D.D., X.T., X.L., C.M.M.K., K.G. and C.T.; visualization, D.D. and X.T.; supervision, T.G., K.G., and C.T; funding acquisition, T.G., and C.T. All authors have read and agreed to the published version of the manuscript.  
This study is supported by EOForChina project (File number: 18-M01-DTU) and ChinaWaterSense project (File number:8087-00002B) and the National Key R&D Program of China (No. 2018 YFE0106500).  
The European Commission and ESA provided the Sentinel data. The USGS and NASA provided the Landsat imagery. Google provided access to Google Earth Engine. The CRESDA and CNSA provided the Gaofen images. The JRC-GSWE product can be viewed and downloaded here [[https://global-surface-water.appspot.com/](https://global-surface-water.appspot.com/)]([https://global-surface-water.appspot.com/](https://global-surface-water.appspot.com/)) (accessed 23 April 2021), and the GLAD-GSWD product here [[https://glad.umd.edu/dataset/global-surface-water-dynamics](https://glad.umd.edu/dataset/global-surface-water-dynamics)]([https://glad.umd.edu/dataset/global-surface-water-dynamics](https://glad.umd.edu/dataset/global-surface-water-dynamics)) (accessed 23 April 2021).  
The authors declare no conflict of interest.