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Towards time-continuous long-term monitoring of global lakes and reservoirs: a novel algorithm for improving temporal frequency of lake area time series
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  • Fangfang Yao,
  • Jida Wang,
  • Chao Wang,
  • Jean-François Crétaux
Fangfang Yao
Kansas State University

Corresponding Author:[email protected]

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Jida Wang
Kansas State University
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Chao Wang
University of Puerto Rico
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Jean-François Crétaux
Centre National d'Études Spatiales (CNES)
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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 produced area time series, combining those from cloud-free images and recovered from contaminated images, exhibit strong correlations with altimetry levels (Spearman’s rho mostly ~0.8 or larger) and extended the hypsometric (area-level) ranges revealed by cloud-free images alone. The combined time series also improved the monthly coverage by an average of 43%, resulting in a bi-monthly water area record during the satellite altimetry era thus far (1992–2018). Given such performance and a generic nature of this method, we foresee its potential applications to assisting water area recovery for other optical and SAR sensors (e.g., Sentinel-2 and SWOT), and to estimating lake/reservoir storage variations in conjunction with altimetry sensors.