综合分析25篇论文，推荐多源遥感与机器学习结合估算森林生物量的最佳组合。
Machine Learning and Multi-source Remote Sensing in Forest Aboveground Biomass Estimation: A Review
- 系统梳理25篇论文，对比不同机器学习方法与多源遥感数据的组合效果。
- 随机森林使用最频繁（88%），梯度提升在75%比较中表现更优。
- 哨兵1号最常用，融合哨兵1、2号与激光雷达效果最佳，适合生态研究者参考。
准确量化森林地上生物量（AGB）对制定保护地球的决策与政策至关重要。机器学习（ML）与遥感（RS）技术被广泛用于更高效地完成该任务，但现有研究缺乏对最新ML方法与多源遥感数据组合的系统性综述，尤其未充分考虑森林生态特征。本研究从80余篇相关文献中筛选出25篇符合严格标准的论文，系统分析了所采用的机器学习方法及遥感数据组合。结果显示，随机森林在88%的研究中被使用，而极端梯度提升在75%的对比研究中表现更优。哨兵-1（Sentinel-1）是最常使用的遥感数据源，多传感器融合（如哨兵-1、哨兵-2与LiDAR）表现出显著优势。研究为整合机器学习与遥感技术进行森林地上生物量估算提供了可参考的传感源、变量与方法建议。
原文摘要 · Abstract (English)
Quantifying forest aboveground biomass (AGB) is crucial for informing decisions and policies that will protect the planet. Machine learning (ML) and remote sensing (RS) techniques have been used to do this task more effectively, yet there lacks a systematic review on the most recent working combinations of ML methods and multiple RS sources, especially with the consideration of the forests' ecological characteristics. This study systematically analyzed 25 papers that met strict inclusion criteria from over 80 related studies, identifying all ML methods and combinations of RS data used. Random Forest had the most frequent appearance (88\% of studies), while Extreme Gradient Boosting showed superior performance in 75\% of the studies in which it was compared with other methods. Sentinel-1 emerged as the most utilized remote sensing source, with multi-sensor approaches (e.g., Sentinel-1, Sentinel-2, and LiDAR) proving especially effective. Our findings provide grounds for recommending which sensing sources, variables, and methods to consider using when integrating ML and RS for forest AGB estimation.
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