Investigating the seasonal dynamics of surface water over the Qinghai–Tibet Plateau using Sentinel-1 imagery and a novel gated multiscale ConvNet

Abstract

ABSTRACT The surface water in the Qinghai–Tibet Plateau (QTP) region has undergone dramatic changes in recent decades. To capture dynamic surface water information, many satellite imagery-based methods have been proposed. However, these methods are still limited in terms of automation and accuracy and thus prevent surface water dynamic studies in large-scale QTP regions. In this study, we developed a new fully automatic method for accurate surface water mapping by using Sentinel-1 synthetic aperture radar (SAR) imagery and convolutional networks (ConvNets). Specifically, we built a new multiscale ConvNet structure to improve the model capability in surface water body extraction. Moreover, a gating mechanism is introduced to promote the efficient use of multiscale information. According to the accuracy assessment, the proposed gated multiscale ConvNet (GMNet) achieved the highest overall accuracy of 98.07%. We applied our GMNet for monthly surface water mapping on the QTP; accordingly, we found that the QTP region experienced significant surface water fluctuations over one year. The surface water also showed distinct spatial heterogeneity on the QTP; that is, the surface water fraction of the Inner Tibetan Basin was significantly higher than that of the Mekong Basin in both the wet and dry seasons.