ABSTRACT

Accurate observation of plant biomass is crucial for estimation of global carbon stocks and ecosystem productivity, and optical spectroscopy technology represents a potential solution. However, the plant water content limits biomass estimation accuracy, due to its contribution to the plant spectral characteristics. In this study, we used a plant water-based spectrum correction (PSC) method to decrease the influence of the plant water content, and biomass models were developed by combining the PSC and multiple linear regression (MLR) methods. The spectral data of 387 tree leaves were acquired using an ASD FieldSpec 3 spectrometer, and their water content values were determined by the oven-drying method. Based on the plant water values of the leaves, the optimal model for the PSC-MLR method was determined and was treated as the ultimate PSC-MLR model (coefficient of determination R² of validation = 0.6959, root-mean-square error of validation = 0.0220 kg m⁻², mean relative error of validation = 20.60%, ratio of performance to deviation of validation = 1.3492), which produced a better performance than the standard MLR model. This work shows the great potential of combining PSC with MLR for improved plant biomass estimation accuracy.

摘要

准确观察植物生物量对于估计全球碳储量和生态系统生产力至关重要，而光谱技术代表了一种潜在的解决方案。然而，植物含水量限制了生物量估计的准确性，因为它对植物光谱特征的贡献。在本研究中，我们使用植物水基光谱校正 (PSC) 方法来降低植物含水量的影响，并结合 PSC 和多元线性回归 (MLR) 方法开发生物量模型。使用ASD FieldSpec 3光谱仪获取387片树叶的光谱数据，采用烘干法测定其含水量值。根据叶片的植物水分值，验证的R ² = 0.6959，验证的均方根误差 = 0.0220 kg m ^-2，验证的平均相对误差 = 20.60%，性能与验证偏差的比率 = 1.3492），产生了比标准更好的性能MLR 模型。这项工作显示了将 PSC 与 MLR 相结合以提高植物生物量估计精度的巨大潜力。