数字农科院2.0

How to Better Use Canopy Height in Soybean Biomass Estimation

文献类型： 外文期刊

作者： Zhu, Yanqin;Fan, Fan;Zhang, Zhen;Yu, Xun;Jiang, Tiantian;Li, Liming;Liu, Yadong;Bai, Yali;Tang, Ziqian;Liu, Shuaibing;Yin, Dameng;Jin, Xiuliang

作者机构：

关键词： above-ground biomass;canopy height;soybean;growth stage;machine learning

期刊名称： AGRICULTURE-BASEL

ISSN：

年卷期： 2025 年 15 卷 10 期

页码：

收录情况： SCIE（2025版）

摘要： Soybean, a globally important food and oil crop, requires accurate estimation of above-ground biomass (AGB) to optimize management and prevent yield loss. Despite the availability of various remote sensing methods, systematic research on effectively integrating canopy height (CH) and spectral information for improved AGB estimation remains insufficient. This study addresses this gap using drone data. Three CH utilization approaches were tested: (1) simple combination of CH and spectral vegetation indices (VIs), (2) fusion of CH and VI, and (3) integration of CH, VI, and growing-degree days (GDDs). The results indicate that adding CH always enhances AGB estimation which is based only on VIs, with the fusion approach outperforming simple combination. Incorporating GDD further improved AGB estimation for highly accurate CH data, with the best model achieving a root mean square error (RMSE) of 87.52 +/- 5.88 g/m2 and a mean relative error (MRE) of 28.59 +/- 1.99%. However, for the multispectral data with low CH accuracy, the VIs + GDD fusion (RMSE = 92.94 +/- 6.84 g/m2, MRE = 30.08 +/- 2.29%) surpassed CH + VIs + GDD (RMSE = 97.99 +/- 6.71 g/m2, MRE = 31.41 +/- 2.56%). The findings highlight the role of CH accuracy in AGB estimation and validate the value of growth-stage information in robust modeling. Future research should prioritize the refining of CH prediction and the optimization of composite variable construction to promote the application of this approach in agricultural monitoring.

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