Extracting river networks that are both accurate and topologically connected is important for applications that involve correct routing of material, for example water and sediment, through such networks. We combined water and sediment extraction using radar and multispectral imagery from Sentinel-1 and Sentinel-2 to create both water and sediment masks over a range of study areas. These were then used to condition topographic Digital Elevation Models (DEMs) by lowering the elevation of pixels with both water and sediment present, in a process known as stream burning. We examined how stream burning could improve accuracy of extracted networks and identified the most effective method of burning for optimal results. We find deeper burning depths improved accuracy, with diminishing returns: we suggest burning 40 to 50 meters. We find sediment burning improves accuracy in humid and temperate landscapes, but arid landscapes should be burned using only water pixels. We find accuracy of extracted networks is significantly better on the COP30 global topographic dataset compared to the NASADEM dataset, mainly due to the time of collection. The AW3D30 DEM and FABDEM datasets have accuracies just below that of the COP30 DEM.

中文翻译：

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提取准确的河网：重新审视溪流燃烧


提取准确且拓扑连接的河流网络对于涉及通过此类网络正确路由材料（例如水和沉积物）的应用非常重要。我们使用来自 Sentinel-1 和 Sentinel-2 的雷达和多光谱图像结合水和沉积物提取，在一系列研究区域创建水和沉积物掩模。然后，通过降低存在水和沉积物的像素的高程，将这些数据用于调节地形数字高程模型 (DEM)，这一过程称为“溪流燃烧”。我们研究了流燃烧如何提高提取网络的准确性，并确定了最有效的燃烧方法以获得最佳结果。我们发现更深的燃烧深度可以提高准确性，但收益递减：我们建议燃烧 40 到 50 米。我们发现沉积物燃烧可以提高潮湿和温带景观的准确性，但干旱景观应仅使用水像素来燃烧。我们发现，与 NASADEM 数据集相比，COP30 全球地形数据集上提取的网络的准确性明显更高，这主要是由于收集时间的原因。 AW3D30 DEM 和 FABDEM 数据集的精度略低于 COP30 DEM。