A Surface Water Extraction Method Integrating Spectral and Temporal Characteristics

Abstract

Remote sensing has been applied to observe large areas of surface water to obtain higher-resolution and long-term continuous observation records of surface water. However, limitations remain in the detection of large-scale and multi-temporal surface water mainly due to the high variability in water surface signatures in space and time. In this study, we developed a surface water remote sensing information extraction model that integrates spectral and temporal characteristics to extract surface water from multi-dimensional data of long-term Landsat scenes to explore the spatiotemporal changes in surface water over decades. The goal is to extract open water in vegetation, clouds, terrain shadows, and other land cover backgrounds from medium-resolution remote sensing images. The average overall accuracy and average kappa coefficient of the classification were verified to be 0.91 and 0.81, respectively. Experiments applied to China’s inland arid area have shown that the method is effective under complex surface environmental conditions.