Authors: Fangfang Yao*, Kansas State University, Jida Wang, Kansas State University, Chao Wang, University of North Carolina at Chapel Hill, Jean-François Crétaux, Centre National d'Études Spatiales (CNES)
Topics: Water Resources and Hydrology, Remote Sensing
Keywords: Global surface water, Lakes, Reservoirs, Contaminated images, Long-term changes, Intra-annual variation, Area time series
Session Type: Poster
Presentation File: Download
Improved monitoring of inundation area variations in lakes and reservoirs is crucial for assessing surface water resources in a growing population and a changing climate. Although long-record optical satellites, such as Landsat missions, provide sub-monthly observations at fairly fine spatial resolution, cloud contamination often poses a major challenge for producing temporally continuous time series. We here proposed a novel method to improve the temporal frequency of usable Landsat observations for mapping lakes and reservoirs, by effectively recovering inundation areas from contaminated images. This method automated three primary steps on the cloud-based platform Google Earth Engine. It first leveraged multiple spectral indices to optimize water mapping from archival Landsat images acquired since 1992. Errors induced by minor contaminations were next corrected by the topology of isobaths extracted from nearly cloud-free images. The isobaths were then used to recover water areas under major contaminations through an efficient vector-based interpolation. We validated this method on 428 lakes/reservoirs worldwide that range from ~2 km2 to ~82,000 km2 with time-variable levels measured by satellite altimeters. The recovered water areas show a relative root-mean-squared error of 2.2%, and the errors for over 95% of the lakes/reservoirs below 6.0%. The combined time series also improved the monthly coverage by an average of 43%, resulting in a bi-monthly water area record over the past 25 years.