In this study, we determine the most suitable multispectral sensor that can accurately detect and map eroded areas from other land cover types in Sekhukhune rural district, Limpopo Province, South Africa. Specifically, the study tested the ability of multi-date (wet and dry season) Landsat 8 OLI and Sentinel-2 MSI images in detecting and mapping eroded areas. The implementation was done, using a robust non-parametric classification ensemble: Discriminant Analysis (DA). Three sets of analysis were applied (Analysis 1: Spectral bands as independent dataset; Analysis 2: Spectral vegetation indices as independent and Analysis 3: Combined spectral bands and spectral vegetation indices). Overall classification accuracies ranging between 80% to 81.90% for MSI and 75.71%–80.95% for OLI were derived for the wet and dry season, respectively. The integration of spectral bands and spectral vegetation indices showed that Sentinel-2 (OA = 83, 81%), slightly performed better than Landsat 8, with 82, 86%. The use of bands and vegetation indices as independent dataset resulted in slightly weaker results for both sensors. Sentinel-2 MSI bands located in the NIR (0.785–0.900 μm), red edge (0.698–0.785 μm) and SWIR (1.565–2.280 μm) regions were selected as the most optimal for discriminating degraded soils from other land cover types. However, for Landsat 8OLI, only the SWIR (1.560–2.300 μm), NIR (0.845–0.885 μm) region were selected as the best regions. Of the eighteen spectral vegetation indices computed, NDVI and SAVI and SAVI and Global Environmental Monitoring Index (GEMI) were ranked selected as the most suitable for detecting and mapping soil erosion. Additionally, SRTM DEM derived information illustrates that for both sensors eroded areas occur on sites that are 600 m and 900 m of altitude with similar trends observed in both dry and wet season maps. Findings of this work emphasize the importance of free and readily available new generation sensors in continuous landscape-scale soil erosion monitoring. Besides, such information can help to identify hotspots and potentially vulnerable areas, as well as aid in developing possible control and mitigation measures.

在这项研究中，我们确定了最合适的多光谱传感器，该传感器可以准确地检测和绘制南非林波波省Sekhukhune农村地区其他土地覆盖类型的侵蚀区域。具体来说，该研究测试了多日期（湿季和干季）Landsat 8 OLI和Sentinel-2 MSI图像在检测和绘制侵蚀区域中的能力。使用强大的非参数分类集成：判别分析（DA）完成了实现。应用了三组分析（分析1：光谱带为独立数据集；分析2：光谱植被指数为独立数据，分析3：光谱带和光谱植被指数组合）。MSI和OLI的总分类准确度分别在80％至81.90％之间和OLI的75.71％–80.95％之间。光谱带和光谱植被指数的积分表明，Sentinel-2（OA = 83，81％）的性能略好于Landsat 8，分别为82％和86％。使用波段和植被指数作为独立数据集会导致两个传感器的结果略弱。选择NIR（0.785–0.900μm），红边（0.698–0.785μm）和SWIR（1.565–2.280μm）区域中的Sentinel-2 MSI谱带是区分其他土地覆盖类型的退化土壤的最佳选择。但是，对于Landsat 8OLI，只有SWIR（1.560–2.300μm），NIR（0.845–0.885μm）区域被选为最佳区域。在计算的18种光谱植被指数中，NDVI和SAVI，SAVI和全球环境监测指数（GEMI）被选为最适合检测和绘制土壤侵蚀的地图。此外，SRTM DEM得出的信息表明，对于这两个传感器，侵蚀区域均发生在海拔600 m和900 m的地点，在旱季和湿季图中都观察到相似的趋势。这项工作的发现强调了免费的，随时可用的新一代传感器在连续的景观规模土壤侵蚀监测中的重要性。此外，这些信息可以帮助识别热点和潜在的脆弱区域，并有助于制定可能的控制和缓解措施。