为探究协同主被动遥感估算森林地上生物量(AGB)的潜力,以广州市中心区域的森林AGB为研究对象,基于哨兵1号(Sentinel-1)SAR数据和哨兵2号(Sentinel-2)多光谱影像,提取31个多源遥感特征(包括6种SAR特征和25种光学特征),结合实测AGB,采用6种机器学习(ML)回归模型(随机森林、支持向量机、极端梯度增强、K近邻回归、类别提升、线性回归)建立森林AGB反演模型。结果表明:1)可见耐大气指数绿色(VIGreen)植被特征在森林AGB反演中表现突出,其在随机森林(RF)特征重要性排序中位列第5。不同极化组合方式也对森林AGB反演贡献显著;2)在不同数据集组合中,RF模型在6种回归模型中的精度最高;3)仅使用光学数据的回归精度高于仅使用SAR数据的精度;4)融合SAR与光学数据所获得的森林AGB反演精度远高于仅使用SAR或光学数据;与仅使用SAR相比,决定系数(R²)提升0.48,均方根误差(RMSE)减少3.73;与仅使用光学数据相比,R²提升0.14,RMSE减少2.08。ML算法结合光学与SAR数据可有效提升森林AGB反演的精度。

To investigate the potential of synergistic active-passive remote sensing for estimating forest aboveground biomass (AGB),with the central urban area of Guangzhou as the study area,31 multi-source remote sensing features (including 6 SAR features and 25 optical features) were extracted through Sentinel-1 SAR data and Sentinel-2 multispectral imagery.Combined with field-measured AGB,six machine-learning (ML) regression models (Random Forest,Support Vector Machine,Extreme Gradient Boosting,k-Nearest Neighbors regression,AdaBoost,and Linear Regression) were used to develop forest AGB inversion models.The results showed that:1) The Visible Atmospherically Resistant Index Green (VIGreen) performed prominently in forest AGB inversion,ranking fifth in feature importance in the Random Forest (RF) model;different polarization combinations also contributed significantly to AGB inversion;2) Across different dataset combinations,the RF model achieved the highest accuracy among the six regression models;3) Models using only optical data outperformed those using only SAR data;4) Fusion of SAR and optical data produced substantially higher AGB inversion accuracy than using SAR or optical data alone:compared with SAR-only features,the coefficient of determination (R²) increased by 0.48 and the root mean square error (RMSE) decreased by 3.73;compared with optical-only features,R² increased by 0.14 and RMSE decreased by 2.08.Therefore,ML approaches that integrate optical and SAR data can effectively improve the accuracy of forest AGB inversion.