资源环境科技发展态势分析平台

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, ), time series of area values for 6,817 global reservoirs (with an integrated capacity of 6,099km(3)) 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 1.73x10(5)km(2) to 3.94x10(5)km(2). 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).

Plain Language Summary Understanding of water surface area dynamics is important for modern water resources management. Due to the difficulties collecting data from ground, remote sensing images from satellites have been widely used to map the water coverage and then to analyze the dynamics. However, one typical problem when using satellite images is that they are frequently contaminated by clouds, cloud shadows, and terrain shadows, which result in the underestimation of the water area. Thus, we developed a novel algorithm to remove the impacts of these contaminations for monitoring water area accurately. After comprehensive evaluations, the algorithm was proved to be able to effectively enhance the Landsat-based water area results. A data set containing monthly surface area time series for 6,817 global reservoirs from 1984 to 2015 was subsequently generated using the algorithm. This work (both the data set and the algorithm) can support many applications on both global and local scales to benefit the water management, hydrology, and remote sensing communities.