Submerged aquatic vegetation (SAV) plays an important role in freshwater lake ecosystems. Due to its sensitivity to environmental changes, several SAV species serve as bioindicators for the trophic state of freshwater lakes. Variations in water temperature, light availability and nutrient concentration affect SAV growth and species composition. To monitor the trophic state as required by the European Water Framework Directive (WFD), SAV needs to be monitored regularly. This study analyses the development of macrophyte patches at Lake Starnberg, Germany, by exploring four Sentinel-2A acquired within the main growing season in August and September 2015. Two different methods of littoral bottom coverage assessment are compared, i.e. a semi-empirical method using depth-invariant indices and a physically based, bio-optical method using WASI-2D (Water Colour Simulator). For a precise Sentinel-2 imaging by date and hour, satellite measurements were supported by lake bottom spectra delivered by in situ data-based reflectance models. Both methods identified vegetated and non-vegetated patches in shallow water areas. Furthermore, tall- and meadow-growing SAV growth classes could be differentiated. Both methods revealed similar results when focusing on the identification of sediment and SAV patches (R₂ from 0.56 to 0.81), but not for a differentiation on SAV class growth level (R₂ <0.42).

中文翻译：

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Sentinel-2 用于绘制施塔恩贝格湖（德国）淹没水生植被时空发展图

沉水植被 (SAV) 在淡水湖生态系统中发挥着重要作用。由于其对环境变化的敏感性，一些 SAV 物种可作为淡水湖泊营养状态的生物指标。水温、光照和养分浓度的变化会影响 SAV 的生长和物种组成。为了按照欧洲水框架指令 (WFD) 的要求监测营养状态，需要定期监测 SAV。本研究通过探索 2015 年 8 月和 9 月主要生长季节内获得的四种 Sentinel-2A，分析了德国施塔恩贝格湖大型植物斑块的发育。比较了两种不同的沿海底部覆盖评估方法，即使用半经验方法深度不变指数和基于物理的，使用 WASI-2D（水彩模拟器）的生物光学方法。为了按日期和小时进行精确的 Sentinel-2 成像，卫星测量得到了基于原位数据的反射模型提供的湖底光谱的支持。这两种方法都可以识别浅水区的植被和非植被斑块。此外，可以区分高大和草甸生长的 SAV 生长等级。当专注于沉积物和 SAV 斑块的识别（R 2 从 0.56 到 0.81）时，两种方法都显示出相似的结果，但不是 SAV 类生长水平的区分（R 2 <0.42）。此外，可以区分高大和草甸生长的 SAV 生长等级。当专注于沉积物和 SAV 斑块的识别（R 2 从 0.56 到 0.81）时，两种方法都显示出相似的结果，但不是 SAV 类生长水平的区分（R 2 <0.42）。此外，可以区分高大和草甸生长的 SAV 生长等级。当专注于沉积物和 SAV 斑块的识别（R 2 从 0.56 到 0.81）时，两种方法都显示出相似的结果，但不是 SAV 类生长水平的区分（R 2 <0.42）。