The mapping of water bodies is an important application area of satellite-based remote sensing. In this contribution, a simple framework based on supervised learning and automatic training data annotation is shown, which allows to map inland water bodies from Sentinel satellite data on large scale, i.e. on state level. Using the German state of Bavaria as an example and different combinations of Sentinel-1 SAR and Sentinel-2 multi-spectral imagery as inputs, potentials and limits for the automatic detection of water surfaces for rivers, lakes, and reservoirs are investigated. Both quantitative and qualitative results confirm that fully automatic large-scale inland water body mapping is generally possible from Sentinel data; whereas, the best result is achieved when all available surface-related bands of both Sentinel-1 and Sentinel-2 are fused on a pixel level. The main limitation arises from missed smaller water bodies, which are not observed in bands with a resolution of about 20 m. Given the simplicity of the proposed approach and the open availability of the Sentinel data, the study confirms the potential for a fully automatic large-scale mapping of inland water with cloud-based remote sensing techniques.

水体制图是基于卫星的遥感的重要应用领域。在此贡献中，显示了基于监督学习和自动训练数据注释的简单框架，该框架允许大规模（即在州级别）从Sentinel卫星数据中绘制内陆水体。以德国巴伐利亚州为例，以Sentinel-1 SAR和Sentinel-2多光谱图像的不同组合作为输入，研究了自动检测河流，湖泊和水库水面的潜力和极限。定量和定性结果均证实，根据Sentinel数据，通常可以进行全自动的大规模内陆水域制图。而，当Sentinel-1和Sentinel-2的所有可用的与表面相关的带都在像素级别融合时，将获得最佳结果。主要的局限性是由于缺少较小的水体而引起的，这些水体在分辨率约为20 m的波段中未观察到。鉴于所提出方法的简单性和Sentinel数据的开放可用性，该研究证实了使用基于云的遥感技术对内陆水域进行全自动大规模制图的潜力。