The importance and vulnerability of coastal marshes necessitate effective ways to closely monitor them. Optical remote sensing is a powerful tool for this task, yet its application to diverse coastal marsh ecosystems consisting of different marsh types is limited. This study samples spectral and biophysical data from freshwater, intermediate, brackish, and saline marshes in Louisiana, and develops statistical and machine learning models to assess the marshes’ biomass with combined ground, airborne, and spaceborne remote sensing data. It is found that linear models derived from NDVI and EVI are most favorable for assessing Leaf Area Index (LAI) using multispectral data (R² = 0.7 and 0.67, respectively), and the random forest models are most useful in retrieving LAI and Aboveground Green Biomass (AGB) using hyperspectral data (R² = 0.91 and 0.84, respectively). It is also found that marsh type and plant species significantly impact the linear model development (P < .05 in both cases). Sensors with coarser spatial resolution yield lower LAI values because the fine water networks are not detected and mixed into the vegetation pixels. The Landsat OLI-derived map shows the LAI of coastal mashes in Louisiana mostly ranges from 0 to 5.0, and is highest for freshwater marshes and for marshes in the Atchafalaya Bay delta. The CASI-derived maps show that LAI of saline marshes at Bay Batiste typically ranges from 0.9 to 1.5, and the AGB is mostly less than 900 g/m². This study provides solutions for assessing the biomass of Louisiana’s coastal marshes using various optical remote sensing techniques, and highlights the impacts of the marshes’ species composition on the model development and the sensors’ spatial resolution on biomass mapping, thereby providing useful tools for monitoring the biomass of coastal marshes in Louisiana and diverse coastal marsh ecosystems elsewhere.

沿海沼泽地的重要性和脆弱性需要采取有效的方式来对其进行密切监测。光学遥感是完成此任务的有力工具，但它在由不同沼泽类型组成的各种沿海沼泽生态系统中的应用受到限制。这项研究从路易斯安那州的淡水，中度，咸淡和咸水沼泽中提取光谱和生物物理数据，并开发统计和机器学习模型，以结合地面，空中和星载遥感数据评估沼泽的生物量。发现从NDVI和EVI得出的线性模型最适合使用多光谱数据评估叶面积指数（LAI）（R ² 分别为0.7和0.67），而随机森林模型对于使用高光谱数据（ 分别为R ² = 0.91和0.84 ）检索LAI和地上绿色生物量（AGB）最为有用。还发现，沼泽类型和植物种类对线性模型的发展有显着影响（在两种情况下，P <.05）。具有较宽空间分辨率的传感器产生的LAI值较低，因为未检测到细水网并将其混入植被像素中。由Landsat OLI衍生的地图显示，路易斯安那州沿海麦芽的LAI大多在0到5.0之间，对于淡水沼泽和Atchafalaya Bay三角洲的沼泽最高。来自CASI的地图显示，巴蒂斯湾的盐沼的LAI通常在0.9到1.5之间，而AGB大多小于900 g / m ^2。。这项研究提供了使用各种光学遥感技术评估路易斯安那州沿海湿地生物量的解决方案，并强调了湿地物种组成对模型开发的影响以及传感器的空间分辨率对生物量作图的影响，从而为监测海洋生物量提供了有用的工具。路易斯安那州沿海沼泽的生物量以及其他地方的多样化沿海沼泽生态系统。