Accurately estimating the above-ground biomass (AGB) of high-density tropical rainforests is a challenging issue. In this study, airborne multi-baseline PolInSAR data were used to estimate the tropical rainforest AGB in Gabon, Africa. The most suitable baseline combination of the PolInSAR data was initially determined through baseline selection, and the PolInSAR parameters related to forest height were obtained based on the forest canopy height estimation theory and microwave penetration depth theory. The height parameter, baseline parameter, and observation geometry parameter were then used as independent variables to construct the AGB regression model. Support vector regression (SVR) was chosen as the AGB estimation model, and the global best particle swarm algorithm (GLB-PSO) was used to optimize the SVR model’s parameters. The results show that the RFECV variable selection method is superior to the Pearson method. The GLB-PSO algorithm can also further improve the saturation point of the SVR model—the estimation results show that the saturation point of AGB estimation of PolInSAR multidimensional features combined with the SVR machine learning algorithm is up to 500 Mg/ha, while this saturation point can be increased to 650 Mg/ha when using the GLB-PSO-SVR algorithm.

中文翻译：

---

结合PolInSAR多维特征和优化机器学习算法的热带森林高密度森林AGB估计

准确估计高密度热带雨林的地上生物量（AGB）是一个具有挑战性的问题。在本研究中，机载多基线 PolInSAR 数据用于估计非洲加蓬的热带雨林 AGB。通过基线选择初步确定了PolInSAR数据最合适的基线组合，并基于森林冠层高度估计理论和微波穿透深度理论获得了与森林高度相关的PolInSAR参数。然后将高度参数、基线参数和观测几何参数作为自变量构建AGB回归模型。选择支持向量回归（SVR）作为AGB估计模型，并使用全局最佳粒子群算法（GLB-PSO）来优化SVR模型的参数。结果表明RFECV变量选择方法优于Pearson方法。 GLB-PSO算法还可以进一步提高SVR模型的饱和点——估计结果表明，PolInSAR多维特征结合SVR机器学习算法的AGB估计饱和点高达500 Mg/ha，而这个饱和点使用 GLB-PSO-SVR 算法时，点可以增加到 650 毫克/公顷。