The free charge and open-source of remote sensing imagery, including Landsat-8 and Sentinel-2, offer new opportunities for forest-based AGB mapping and monitoring, especially mangrove forests in the tropics. Modelling relationships between mangrove AGB estimation-based survey and remote sensing data (spectral bands and vegetation indices) have not been evaluated in Quang Ninh Province, Vietnam. In this study, we evaluated the capability of Landsat-8 and Sentinel-2 data for the retrieval and predictive mapping of mangrove AGB in Mong Cai Coast, Quang Ninh Province as a case study. We used 2019 Landsat-8 and Sentinel-2 to develop AGB estimation models through stepwise linear regression approaches in R statistics. We developed models each from spectral bands and vegetation indices derived from Landsat-8 and Sentinel-2 imagery. The results showed that the models based on spectral bands and vegetation indices derived from Sentinel-2 were more accurate in predicting the overall AGB of mangrove forests than those of Landsat-8 data. High correlation values between AGB and Sentinel-2-derived vegetation indices (Model 6.6, r${}^2=0.973$; Model 6.7, r${}^2=0.982$; Model 6.8, r${}^2=0$.988) and Landsat-8-derived vegetation indices (Model 6.1, r${}^2=0.927$; Model 6.2, r${}^2=0.927$; Model 6.3, r${}^2=0$.939); and between AGB and Sentinel-2-derived spectral bands (Model 5.5, r${}^2=972$; Model 5.4, r${}^2=0$.975); between AGB and Landsat-8 derived spectral bands (Model 5.2, r${}^2=0.913$; Model 5.3, r${}^2=0.935$; Model 5.1, r${}^2=0$.855) were obtained. The developed AGB estimation models have high prediction accuracy, agreements of observed and predicted AGB values of 89.88% for Landsat-8 derived vegetation index (Model 6.2), 96.51% for Sentinel-2 derived vegetation index (Model 6.6). Overall, both Landsat-8 and Sentinel-2 provide satisfactory results in the retrieval and predictive mapping of mangrove AGB. Our study suggests that mangrove conservation under C-PFES schemes should be applied over Quang Ninh Coast based on AGB estimation models developed.

遥感影像的免费和开源，包括 Landsat-8 和 Sentinel-2，为基于森林的 AGB 制图和监测提供了新的机会，尤其是热带地区的红树林。越南广宁省尚未评估基于红树林 AGB 估计的调查与遥感数据（光谱带和植被指数）之间的建模关系。在本研究中，我们评估了 Landsat-8 和 Sentinel-2 数据在广宁省芒街海岸红树林 AGB 检索和预测制图的能力作为案例研究。我们使用 2019 Landsat-8 和 Sentinel-2 通过 R 统计中的逐步线性回归方法开发 AGB 估计模型。我们分别根据来自 Landsat-8 和 Sentinel-2 图像的光谱带和植被指数开发了模型。结果表明，与Landsat-8数据相比，基于Sentinel-2光谱带和植被指数的模型在预测红树林整体AGB方面更准确。AGB 和 Sentinel-2 衍生的植被指数之间的高相关值（模型 6.6，r${}^2=0.973$; 型号 6.7, r${}^2=0.982$; 型号 6.8, r${}^2=0$.988) 和 Landsat-8 衍生的植被指数（模型 6.1，r${}^2=0.927$; 型号 6.2, r${}^2=0.927$; 型号 6.3, r${}^2=0$.939); 以及在 AGB 和 Sentinel-2 衍生的光谱带之间（模型 5.5，r${}^2=972$; 型号 5.4, r${}^2=0$.975); AGB 和 Landsat-8 衍生的光谱带之间（模型 5.2，r${}^2=0.913$; 型号 5.3, r${}^2=0.935$; 型号 5.1, r${}^2=0$.855) 获得。开发的 AGB 估计模型具有较高的预测精度，Landsat-8 派生植被指数（模型 6.2）的观测和预测 AGB 值的一致性为 89.88%，Sentinel-2 派生植被指数（模型 6.6）的一致性为 96.51%。总体而言，Landsat-8 和 Sentinel-2 在红树林 AGB 的检索和预测制图方面都提供了令人满意的结果。我们的研究表明，根据开发的 AGB 估计模型，应在广宁海岸应用 C-PFES 计划下的红树林保护。