The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models.

先进的机器学习算法的可用性使生物物理参数的估计过程更加高效。但是，与已经建立的半经验程序相比，这些方法的效率很少。红树林的地上生物量（AGB）是至关重要的生物物理参数，因为它与碳储量和通量呈正相关。Sentinel-1 C波段SAR数据的免费提供和机器学习算法为估计热带红树林的AGB带来了希望。我们报告了位于印度东部沿海地区的Bhitarkanika野生动物保护区的185个四方类动物，每生0.04公顷，其AGB较高（70吨/公顷至666吨/公顷），这可能归因于物种组成。使用干涉式水云模型（IWCM）和深度学习模型生成的AGB地图彼此不同，因为它们依赖于不同的变量。IWCM更依赖于地面，尤其是地面和植被的连贯性，而树冠高度则是深度学习模型中最关键的变量。但是，基于深度学习的AGB映射中的微不足道的变化可以归因于解释相干性和VH反向散射的重要性。由于C波段SAR的冠层穿透力低，由于干涉图像对之间的时间间隔更长而导致的高时间去相关性以及红树林的高空间异质性，IWCM被认为是不适合AGB估算的方法。有趣的是，深度学习算法可以转换Bhitarkanika野生动物保护区中预测变量与红树林AGB之间的确切关系。使用Sentinel数据在红树林中进行的AGB估算研究应更多​​地侧重于使用机器学习算法（例如深度学习），而不是半经验模型。