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\begin{document}

\title{Towards time-continuous long-term monitoring of global lakes and
reservoirs: a novel algorithm for improving temporal frequency of lake
area time series}



\author[1]{Fangfang Yao}%
\author[1]{Jida Wang}%
\author[2]{Chao Wang}%
\author[3]{Jean-François Crétaux}%
\affil[1]{Kansas State University}%
\affil[2]{University of Puerto Rico}%
\affil[3]{Centre National d'Études Spatiales (CNES)}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
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 \textasciitilde{}2 km2 to
\textasciitilde{}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
\textasciitilde{}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.%
\end{abstract}%



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