Analysis of the Application of Machine Learning Algorithms Based on Sentinel-1/2 and Landsat 8 OLI Data in Estimating Above-Ground Biomass of Subtropical Forests

作者：Yuping Wang, Steven Hancock, Wenquan Dong, Yongjie Ji, Han Zhao, Mengjin Wang · 发表于：Forests · 年份：2025 · DOI：10.3390/f16040559 · 被引用次数：2 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

Accurate monitoring of aboveground biomass (AGB) in subtropical forests plays an important role in maintaining biodiversity and the balance of forest ecosystems. It is of high importance to explore how machine learning models can improve the ability and accuracy of AGB estimation of different types of subtropical forests under the conditions of active and passive open-source remote sensing (RS) data. In this study, the subtropical forests in the Pu’er region of Yunnan Province were used as the research object, and backscattering coefficients, mean reflectance, and textural features from Sentinel-1, Sentinel-2, and Landsat 8 OLI open-source RS data were used as the data source. We classified the subtropical forests into three basic forest types: broadleaf forest, coniferous forest, and mixed forest. Based on filtering and analyzing RS features, we performed forest AGB inversion using Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost). The results show that: (1) VH-related texture features in Sentinel-1, and red-edge band features, IR band features, and texture features in Sentinel-2 and Landsat 8 OLI are sensitive to changes in forest AGB. (2) Among the three nonparametric methods, the XGBoost algorithm had the highest estimation accuracy with an MAE of 10.05 t/ha and RMSE of 12.43 t/ha in coniferous forests; the second estimation accuracy in mixed forests with an MAE of 20.18 t/ha and RMSE of 25.33 t/ha; and the estimation accuracy in...