State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 200062, Shanghai, China.
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 200062, Shanghai, China.
Sci Total Environ. 2021 Jul 10;777:146051. doi: 10.1016/j.scitotenv.2021.146051. Epub 2021 Feb 24.
Quantifying temporal and spatial changes in microphytobenthos (MPB) biomass is critical for understanding its ecological function in estuarine food web networks and carbon flows. However, tidal fluctuations and the complex composition of coastal sediment limit remote sensing applications for estimating MPB biomass seasonal variations in estuarine tidal flats. We integrated radar Sentinel-1 (S1) and optical Sentinel-2 (S2) remote sensing data to quantify the temporal and spatial variability in MPB biomass in the Changjiang estuary, China. Pixels of water bodies on the tidal flats were removed by dynamic threshold segmentation of the water index with the combined S1 and S2 data, and salt marsh pixels were masked with the first red-edge band in the S2 data. We used the continuum-removed spectral absorption depth feature to construct a regression model for estimating MPB biomass with a regression coefficient of 0.81. The results showed that spectral absorption continuum removal methods using broadband multispectral data for MPB estimation are a promising alternative to hyperspectral narrowband ratio operation. Compared with the widely used normalized difference vegetation index (NDVI), the scaled absorption depth feature was more stable for MPB estimation under a changeable sediment background. The produced seasonal map showed that the high biomass levels of the MPB in the study area are not limited to one season and one site, with an annual mean biomass of 14.39 mg chlorophyll a (Chl-a)·m and 71% confirmed accuracy. The highest biomass levels occurred in summer in the supratidal zone (19.51 mg Chl-a·m) and in spring in the intertidal zone (17.10 mg Chl-a·m) in the Changjiang estuary. The relative shore height, derived from the tidal range here, is an important variable that shapes the MPB spatial distribution. This study demonstrates the potential of integrating high-spatial-resolution (10 m) S1 and S2 data for future large-scale estimation of intertidal MPB.
定量研究微型底栖生物(MPB)生物量的时空变化,对于理解其在河口食物网和碳流动中的生态功能至关重要。然而,潮汐波动和沿海沉积物的复杂组成限制了遥感应用,难以估算河口潮滩 MPB 生物量的季节性变化。本研究通过雷达 Sentinel-1(S1)和光学 Sentinel-2(S2)遥感数据的融合,定量分析了中国长江口 MPB 生物量的时空变化。利用 S1 和 S2 数据联合进行水体水指数动态阈值分割,去除潮滩水体像素,利用 S2 数据的第一红边波段掩蔽盐沼像素。利用连续谱去除光谱吸收深度特征,构建了一个回归模型,用于估算 MPB 生物量,回归系数为 0.81。结果表明,使用宽带多光谱数据进行 MPB 估算的光谱吸收连续谱去除方法是替代高光谱窄带比值运算的一种有前途的方法。与广泛使用的归一化差异植被指数(NDVI)相比,在变化的泥沙背景下,缩放吸收深度特征更适合 MPB 估算。生成的季节图表明,研究区 MPB 的高生物量水平不仅局限于一个季节和一个地点,年平均生物量为 14.39mg 叶绿素 a(Chl-a)·m,有 71%的生物量被正确识别。生物量最高的季节是夏季的潮上带(19.51mg Chl-a·m)和春季的潮间带(17.10mg Chl-a·m)。相对岸高,即本地区潮汐范围的衍生变量,是塑造 MPB 空间分布的重要因素。本研究证明了整合高空间分辨率(10m)S1 和 S2 数据用于未来大规模估算潮间带 MPB 的潜力。