The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping.

本文提出了一种基于Sentinel-2和Landsat影像绘制河流砾石条的方法。所提出的方法因此使用频谱信号混合分析（SSMA），因为其结果允许开发用于地表水，砾石和植被的土地覆盖率图。该方法在中欧斯洛文尼亚西北部Soča河流域上游的空间异质山区得到验证。取消混合可得到MAE约为0.1的高精度馏分图。砾石馏分的映射最准确，表明该方法可成功用于河流砾石柱映射。与基于高分辨率参考数据手动选择的组相比，自动选择的最终成员组的性能稍差（MAE最高为0.05）。Sentinel-2和Landsat影像均可用于精确地图绘制，两个遥感系统之间的差异在0.05 MAE之内。对于研究区域，基于SSMA的软分类方法比基于光谱角度映射的硬分类更准确地用于土地覆盖图。该方法有望在需要高精度子像素信息的其他情况下有效使用，因为它能够检测到小规模变化，而这些变化在硬分类映射中可能不会被注意到。