Abstract Accurate habitat mapping methods are urgently required for the monitoring, conservation, and management of blue carbon ecosystems and their associated services. This study focuses on exposed intertidal seagrass meadows, which play a major role in the functioning of nearshore ecosystems. Using Sentinel-2 (S2) data, we demonstrate that satellite remote sensing can be used to map seagrass percent cover (SPC) and leaf biomass (SB), and to characterize its seasonal dynamics. In situ radiometric and biological data were acquired from three intertidal meadows of Zostera noltei along the European Atlantic coast in the summers of 2018 and 2019. This information allowed algorithms to estimate SPC and SB from a vegetation index to be developed and assessed. Importantly, a single SPC algorithm could consistently be used to study Z. noltei-dominated meadows at several sites along the European Atlantic coast. To analyze the seagrass seasonal cycle and to select images corresponding to its maximal development, a two-year S2 dataset was acquired for a French study site in Bourgneuf Bay. The potential of S2 to characterize the Z. noltei seasonal cycle was demonstrated for exposed intertidal meadows. The SPC map that best represented seagrass growth annual maximum was validated using in situ measurements, resulting in a root mean square difference of 14%. The SPC and SB maps displayed a patchy distribution, influenced by emersion time, mudflat topology, and seagrass growth pattern. The ability of S2 to measure the surface area of different classes of seagrass cover was investigated, and surface metrics based on seagrass areas with SPC ≥ 50% and SPC ≥ 80% were computed to estimate the interannual variation in the areal extent of the meadow. Due to the high spatial resolution (pixel size of 10 m), frequent revisit time (≤ 5 days), and long-term objective of the S2 mission, S2-derived seagrass time-series are expected to contribute to current coastal ecosystem management, such as the European Water Framework Directive, but to also guide future adaptation plans to face global change in coastal areas. Finally, recommendations for future intertidal seagrass studies are proposed.

中文翻译：

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Sentinel-2 遥感对 Zostera noltei 占主导地位的潮间带海草草甸的遥感

摘要 蓝碳生态系统及其相关服务的监测、保护和管理迫切需要准确的栖息地测绘方法。这项研究的重点是裸露的潮间带海草草甸，它们在近岸生态系统的功能中发挥着重要作用。使用 Sentinel-2 (S2) 数据，我们证明卫星遥感可用于绘制海草百分比覆盖率 (SPC) 和叶生物量 (SB)，并表征其季节性动态。在 2018 年和 2019 年夏季，从欧洲大西洋沿岸的三个带叶草的潮间带草甸获得了原位辐射测量和生物数据。这些信息使算法能够根据待开发和评估的植被指数估计 SPC 和 SB。重要的是，可以始终使用单个 SPC 算法来研究 Z。noltei 在欧洲大西洋沿岸的几个地点占主导地位的草地。为了分析海草的季节性周期并选择与其最大发育相对应的图像，我们为 Bourgneuf 湾的法国研究站点获取了一个为期两年的 S2 数据集。在暴露的潮间带草甸中证明了 S2 表征 Z. noltei 季节性周期的潜力。使用原位测量验证了最能代表海草生长年度最大值的 SPC 地图，导致均方根差异为 14%。SPC 和 SB 地图显示出不规则分布，受出水时间、泥滩拓扑和海草生长模式的影响。研究了 S2 测量不同类别海草覆盖表面积的能力，计算了基于 SPC ≥ 50% 和 SPC ≥ 80% 的海草面积的表面指标，以估计草甸面积范围的年际变化。由于空间分辨率高（像素大小为 10 m）、频繁的重访时间（≤ 5 天）和 S2 任务的长期目标，S2 衍生的海草时间序列预计将有助于当前的沿海生态系统管理，例如欧洲水框架指令，但也指导未来的适应计划，以应对沿海地区的全球变化。最后，提出了对未来潮间带海草研究的建议。S2 衍生的海草时间序列预计将有助于当前的沿海生态系统管理，例如欧洲水框架指令，但也将指导未来的适应计划，以应对沿海地区的全球变化。最后，提出了对未来潮间带海草研究的建议。S2 衍生的海草时间序列预计将有助于当前的沿海生态系统管理，例如欧洲水框架指令，但也将指导未来的适应计划，以应对沿海地区的全球变化。最后，提出了对未来潮间带海草研究的建议。