Estimating Stratified Biomass in Cotton Fields Using UAV Multispectral Remote Sensing and Machine Learning

Abstract

The accurate estimation of aboveground biomass (AGB) is essential for monitoring crop growth and supporting precision agriculture. Traditional AGB estimation methods relying on single spectral indices (SIs) or statistical models often fail to address the complexity of vertical canopy stratification and growth dynamics due to spectral saturation effects and oversimplified structural representations. In this study, a unmanned aerial vehicle (UAV) equipped with a 10-channel multispectral sensor was used to collect spectral reflectance data at different growth stages of cotton. By integrating multiple vegetation indices (VIs) with three algorithms, including random forest (RF), linear regression (LR), and support vector machine (SVM), we developed a novel stratified biomass estimation model. The results revealed distinct spectral reflectance characteristics across the upper, middle, and lower canopy layers, with upper-layer biomass models exhibiting superior accuracy, particularly during the middle and late growth stages. The coefficient of determination of the UAV-based hierarchical model (R2 = 0.53–0.70, RMSE = 1.50–2.96) was better than that of the whole plant model (R2 = 0.24–0.34, RMSE = 3.91–13.85), with a significantly higher R2 and a significantly lower root mean squared error (RMSE). This study provides a cost-effective and reliable approach for UAV-based AGB estimation, addressing limitations in traditional methods and offering practical significance for improving crop management in precision agriculture.

Keywords

• UAV multispectral sensing
• aboveground biomass
• stratified estimation
• vegetation index
• machine learning
• precision agriculture