Surface water map of China for 2015 (SWMC-2015) derived from Landsat 8 satellite imagery

ABSTRACT

Large-scale surface water mapping not only helps us protect, utilize and manage water resources but also contributes to the understanding of climate change and the hydrologic cycle. A recent study showed that a multilayer perceptron (MLP) neural network is an effective method to identify various surface water types from Landsat 8 Operational Land Imager (OLI) satellite imagery. We use this method to produce a surface water map of China for 2015 (SWMC-2015) at a 30 m pixel size. The accuracy of SWMC-2015 was assessed with a set of random water and not water validation points. The strengths and limitations of SWMC-2015 include: the SWMC-2015 clearly shows the major lake clusters and river networks with high mapping accuracy and the overall accuracy and kappa coefficients of SWMC-2015 are 90% and 0.78, respectively. The accuracy of SWMC-2015 can be improved from perspective of training samples representation, verification sample seasonal fluctuation and mixed pixel. The SWMC-2015 is available for free download on the remote sensing of global change website (https://vapd.gitlab.io/post/swmc2015/).

Acknowledgments

The authors thank editors and three anonymous reviewers for their valuable and careful suggestions to improve our manuscript.

Additional information

Funding

This research was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA190090300), the National Natural Science Foundation of China (51779269, 61731022, and 61701495) and the National Key Research and Development Program of China—rapid production method for large-scale global change products (2016YFA0600302).