Abstract The improved spatial and temporal resolution of latest-generation Earth Observation missions, such as Landsat 8 and Sentinel-2, has increased the potential of remote sensing for mapping land surface phenology in inland water systems. The ability of a time series of medium-resolution satellite data to generate quantitative information on macrophyte phenology was examined, focusing on three temperate shallow lakes with connected wetlands in Italy, France, and Romania. Leaf area index (LAI) maps for floating and emergent macrophyte growth forms were derived from a semi-empirical regression model based on the best-performing spectral index, with an error level of 0.11 m2 m−2. Phenology metrics were computed from LAI time series using TIMESAT to analyze the seasonal dynamics of macrophyte spatial distribution patterns and species-dependent variability. Particular seasonal patterns seen in the autochthonous and allochthonous species across the three study areas related to local ecological and hydrological conditions. How characteristics of the satellite dataset (cloud cover threshold, temporal resolution, and missing acquisitions) influenced the phenology metrics obtained was also assessed. Our results indicate that, with a full-resolution time series (5-day revisit time), cloud cover introduced a bias in the phenology metrics of less than 2 days. Even when the temporal resolution was reduced to 15 days (like the Landsat revisit time) the timing of the start and the peak of macrophyte growth could still be mapped with an error of no more than 2–3 days.

中文翻译：

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使用中分辨率卫星数据的密集时间序列评估大型植物的季节性动态

摘要 最新一代地球观测任务（如 Landsat 8 和 Sentinel-2）的空间和时间分辨率提高，增加了遥感用于绘制内陆水域系统地表物候的潜力。研究了中等分辨率卫星数据时间序列生成大型植物物候定量信息的能力，重点是意大利、法国和罗马尼亚的三个温带浅湖与湿地相连。浮动和挺生大型植物生长形式的叶面积指数 (LAI) 图源自基于表现最佳的光谱指数的半经验回归模型，误差水平为 0.11 m2 m-2。使用 TIMESAT 从 LAI 时间序列计算物候指标，以分析大型植物空间分布模式和物种相关变异性的季节性动态。在与当地生态和水文条件相关的三个研究区域的本地和外来物种中看到的特定季节性模式。还评估了卫星数据集的特征（云量阈值、时间分辨率和丢失的采集）如何影响获得的物候指标。我们的结果表明，在全分辨率时间序列（5 天重访时间）中，云覆盖在物候指标中引入了不到 2 天的偏差。