Automatic Correction of Contaminated Images for Assessment of Reservoir Surface Area Dynamics

Abstract

The potential of using Landsat for assessing long-term water surface dynamics of individual reservoirs at a global scale has been significantly hindered by contaminations from clouds, cloud shadows, and terrain shadows. A novel algorithm was developed toward the automatic correction of these contaminated image classifications. By applying this algorithm to the data set by Pekel et al. (2016, https://doi.org/10.1038/nature20584), time series of area values for 6,817 global reservoirs (with an integrated capacity of 6,099 km3) were generated from 1984 to 2015. The number of effective images that can be used in each time series has been improved by 81% on average. The long-term average area for these global reservoirs was corrected from 1.73 × 105 km2 to 3.94 × 105 km2. The results were proven to be robust through validation using observations, synthetic data, and visual inspection. This continuous reservoir surface area data set can provide benefit to various applications (both at continental and local scales).