Main content area

Constructing long-term high-frequency time series of global lake and reservoir areas using Landsat imagery

Yao, Fangfang, Wang, Jida, Wang, Chao, Crétaux, Jean-François
Remote sensing of environment 2019 pp. 111210
Internet, Landsat, altimeters, basins, climate change, geometry, ice, mineral content, monitoring, mountains, remote sensing, salt lakes, satellite altimetry, snow, surface water, time series analysis, topology
Improved monitoring of inundation extent 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 propose 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 automates three primary steps on the cloud-based platform Google Earth Engine. It first leverages multiple spectral indices to optimize water mapping from archival Landsat images acquired since 1992. Errors induced by minor contaminations are next corrected by the topology of isobaths extracted from nearly cloud-free images. The isobaths are then used to recover water areas under major contaminations through an efficient vector-based interpolation. We validate 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 mean relative error of 2.2%. 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) and extended the hypsometric (area-level) ranges revealed by cloud-free images alone. The combined time series also improve the monthly coverage by an average of 43%, resulting in a bi-monthly water area record during the entire satellite altimetry era thus far (1992–2018). The robustness of this method is further verified under five challenging mapping scenarios, including fluvial lakes in humid basins, reservoirs with complex geometries, saline lakes with high mineral concentrations, lakes/reservoirs in mountainous regions, and pan-Arctic lakes with frequent snow/ice covers. 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.