Accurate estimation of mangrove leaf area index (LAI) is fundamental for effective mangrove ecosystem management and protection. Remote sensing technology has showed its powerful potential in accurately retrieving mangrove LAI. The generic estimation model combining vegetation indices (VIs) with physically-based law, simplified as LAI-VIs model, has successfully estimated crop LAI. However, the capacity of estimating mangrove LAI using this model, so far, is unclear. Moreover, some studies have proved that estimation accuracy of terrestrial forests and crops LAI can be ameliorated with VIs based on red-edge band (VIs_RE) because of less affecting by canopy structure. However, little literature explores the ability of VIs_RE, especially, from different multispectral sensors, for estimating mangrove LAI. Therefore, our main purpose is to evaluate the robustness and sensitivity of the LAI-VIs_RE model from Sentinel-2, WorldView-2 (WV-2) and Unmanned Aerial Vehicle (UAV) multispectral imagery for estimating mangrove LAI. The estimation models with input variables of NDVI, NDVI_RE1 (band combination from red-edge and visible band), NDVI_RE2 (band combination from red-edge and near-infrared reflectance) from three types of multispectral imagery are used to calculate mangrove LAI of 99 plots. The results showed that the WV-2 imagery acquires the best estimation accuracy (R² = 0.72, RMSE = 0.414), followed by Sentinel-2 imagery (R² = 0.68, RMSE = 0.440), and UAV multispectral imagery (R² = 0.48, RMSE = 0.570). The analyses display the good results of the LAI-NDVI model and LAI-NDVI_RE1 model from WV-2 and Sentinel-2 imagery with the range of R² from 0.57 to 0.72, and the discrepant consequences of LAI-NDVI_RE2 model from UAV imagery with R² of 0.15, WV-2 imagery with R² of 0.67 and Sentienl-2 imagery with R² of 0.65, 0.18 and 0.12. This study proves that the generic estimation model and NDVI_RE1 derived from WV-2 and Sentinel-2 multispectral imagery could be deemed as a basic method and input variables of mapping mangrove LAI.

准确估算红树林叶面积指数 (LAI) 是有效管理和保护红树林生态系统的基础。遥感技术在准确检索红树林LAI方面显示出强大的潜力。结合植被指数（VIs）和物理规律的通用估计模型，简化为LAI-VIs模型，成功地估计了作物LAI。然而，到目前为止，使用该模型估计红树林 LAI 的能力尚不清楚。此外，一些研究证明，基于红边带（VIs_RE）的VIs可以提高陆生森林和农作物LAI的估计精度，因为受冠层结构的影响较小。然而，很少有文献探讨 VIs_RE 的能力，尤其是来自不同多光谱传感器的估计红树林 LAI 的能力。所以，我们的主要目的是评估来自 Sentinel-2、WorldView-2 (WV-2) 和无人机 (UAV) 多光谱图像的 LAI-VIs_RE 模型的鲁棒性和灵敏度，用于估计红树林 LAI。输入变量为NDVI、NDVI_RE1（红边和可见波段的波段组合）、NDVI_RE2（红边和近红外反射率的波段组合）三种多光谱图像的估计模型用于计算99的红树林LAI地块。结果表明，WV-2图像获得了最好的估计精度（R 来自三种类型的多光谱图像的 NDVI_RE2（红边和近红外反射的波段组合）用于计算 99 个地块的红树林 LAI。结果表明，WV-2图像获得了最好的估计精度（R 来自三种类型的多光谱图像的 NDVI_RE2（红边和近红外反射的波段组合）用于计算 99 个地块的红树林 LAI。结果表明，WV-2图像获得了最好的估计精度（R²  = 0.72，RMSE = 0.414），然后是 Sentinel-2 图像（R ²  = 0.68，RMSE = 0.440）和无人机多光谱图像（R ²  = 0.48，RMSE = 0.570）。分析显示，来自 WV-2 和 Sentinel-2 影像的 LAI-NDVI 模型和 LAI-NDVI_RE1 模型的良好结果，R ²范围为0.57 至 0.72，而 LAI-NDVI_RE2 模型与无人机影像的差异结果与[R ²为0.15，WV-2图像，其中R ²为0.67和Sentienl-2图像，其中R ²为0.65，0.18和0.12。本研究证明，基于WV-2和Sentinel-2多光谱影像的通用估计模型和NDVI_RE1可以作为映射红树林LAI的基本方法和输入变量。