文献类型： 外文期刊
作者： Zhu, Yanqin 1 ; Fan, Fan 2 ; Zhang, Zhen 1 ; Yu, Xun 2 ; Jiang, Tiantian 1 ; Li, Liming 1 ; Liu, Yadong 2 ; Bai, Yali 2 ; Tang, Ziqian 2 ; Liu, Shuaibing 2 ; Yin, Dameng 2 ; Jin, Xiuliang 2 ;
作者机构： 1.Anhui Univ Sci & Technol, Sch Geomat, Huainan 232001, Peoples R China
2.Chinese Acad Agr Sci, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, Beijing 100081, Peoples R China
3.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Sanya 572024, Peoples R China
4.Wageningen Univ & Res, Informat Technol Grp, NL-6708 PB Wageningen, Netherlands
关键词： above-ground biomass; canopy height; soybean; growth stage; machine learning
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 10 期
页码：
收录情况： SCI
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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