Extraction of drainage networks is an important element of river flow routing in hydrology and large-scale estimates of river behaviors in Earth sciences. Emerging studies with a focus on greenhouse gases reveal that small rivers can contribute to more than half of the global carbon emissions from inland waters (including lakes and wetlands). However, large-scale extraction of drainage networks is constrained by the coarse resolution of observational data and models, which hinders assessments of terrestrial hydrological and biogeochemical cycles. Recognizing that Sentinel-2 satellite can detect surface water up to a 10-m resolution over large scales, we propose a new method named Remote Sensing Stream Burning (RSSB) to integrate high-resolution observational flow location with coarse topography to improve the extraction of drainage network. In RSSB, satellite-derived input is integrated in a spatially continuous manner, producing a quasi-bathymetry map where relative relief is enforced, enabling a fine-grained, accurate, and multitemporal extraction of drainage network. RSSB was applied to the Lancang-Mekong River basin to derive a 10-m resolution drainage network, with a significant reduction in location errors as validated by the river centerline measurements. The high-resolution extraction resulted in a realistic representation of meanders and detailed network connections. Further, RSSB enabled a multitemporal extraction of river networks during wet/dry seasons and before/after the formation of new channels. The proposed method is fully automated, meaning that the network extraction preserves basin-wide connectivity without requiring any postprocessing, hence facilitating the construction of drainage networks data with openly accessible imagery. The RSSB method provides a basis for the accurate representation of drainage networks that maintains channel connectivity, allows a more realistic inclusion of small rivers and streams, and enables a greater understanding of complex but active exchange between inland water and other related Earth system components.

排水网络的提取是水文中河流径流路由的重要元素，也是地球科学中河流行为的大规模估计。以温室气体为重点的新兴研究表明，小河可造成内陆水域（包括湖泊和湿地）的全球碳排放量的一半以上。但是，排水网络的大规模提取受到观测数据和模型分辨率的限制，这阻碍了对陆地水文和生物地球化学循环的评估。认识到Sentinel-2卫星可以在大范围内探测到高达10-m分辨率的地表水，我们提出了一种名为遥感流燃烧（RSSB）的新方法，该方法将高分辨率的观测流位置与粗糙的地形相结合，以改善对水的提取。排水网络。在RSSB中，卫星输入的输入以空间连续的方式进行集成，生成准测深图，在该图上强制执行相对释放，从而可以对排水网络进行细粒度，准确和多时相提取。RSSB应用于澜沧江-湄公河流域，形成了一个10米分辨率的排水管网，通过河流中心线测量证实，位置误差显着减少。高分辨率提取可真实显示弯道和详细的网络连接。此外，RSSB使在潮湿/干旱季节以及在形成新河道之前/之后能够对河网进行多时相提取。所提出的方法是完全自动化的，这意味着网络提取无需任何后处理即可保持流域范围的连通性，因此，利用公开可访问的图像，有助于排水网络数据的构建。RSSB方法为准确表示排水网络提供了基础，可保持渠道连通性，允许更现实地包含小河流和小溪，并使人们更加了解内陆水域与其他相关地球系统组件之间的复杂但活跃的交换。