A Global Scale 30m Water Surface Detection Optimized and Validated for Landsat 8

Abstract:

Life on Earth as we know it is impossible without water. Its importance to biological diversity, human well-being and the very functioning of the Earth-system cannot be overstressed, but we have remarkably little detailed knowledge concerning the spatial and temporal distribution of this vital resource. Earth observing satellites operating with high temporal revisits yet moderate spatial resolution have provided global datasets documenting spatial and temporal changes to water bodies on the Earth’s surface. Landsat 8 has a data acquisition strategy such that global coverage of all land surfaces now occurs more frequently than from any preceding Landsat mission and provides 30 m resolution data. Whilst not the last word in temporal sampling this presents a basis for mapping and monitoring changes to global surface water resources at unprecedented levels of spatial detail. In this paper we provide a first 30 m resolution global synthesis of surface water occurrence, we document permanent water surfaces, seasonal water surfaces and always-dry surfaces. These products have been derived by optimizing a methodology previously developed for use with moderate resolution MODIS imagery for use with Landsat 8. The approach is based on a transformation of RGB color space into HSV combined with a sequence of cloud, topographic and temperature masks. Analysis at the global scale used the Google Earth Engine platform applied to all Landsat 8 acquisitions between June 2013 and June 2014. Systematic validation is done and demonstrated our ability to map surface water. Our method can be applied to other Landsat missions offering the potential to document changes in surface water over three decades; our study shows examples illustrating the capacity to map new water surfaces and ephemeral water surfaces in addition to the three previous classes. Thanks to an optimized data acquisition strategy, a full-free and open data policy and the processing capacity of the GEE global land surface process monitoring at Landsat-class resolution is a reality. However high temporal revisit is needed to improve inter and intra annual characterization and we expect further improvements to the new products presented here as we complement the Landsat program with data from the forthcoming Sentinel 2 mission, and potentially others too.