A robust two-stage framework for maize above-ground biomass prediction integrating spectral remote sensing and allometric growth model

文献类型： 外文期刊

第一作者： Yang, Mohan

作者： Yang, Mohan;Wu, Qiang;Zhang, Jun;Cheng, Jinpeng;Xiong, Shuping;Ma, Xinming;Qi, Jianbo;Yang, Guijun;Yang, Hao;Cheng, Jinpeng;Yang, Guijun;Wang, Zanpu;Wang, Zhenyu

作者机构：

关键词： Maize; Above-ground biomass (AGB); Radiative transfer model; Allometric growth model; Spectral remote sensing

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 235 卷

页码：

收录情况： SCI

摘要： Above-ground biomass (AGB) is a key indicator for evaluating maize growth dynamics and yield. Although the remote sensing methods have demonstrated utility in biomass estimation, they often overlook the fundamental heterogeneity in spectral contributions between photosynthetic (primarily leaves) and non-photosynthetic organs (stems, ears, and tassels). In this study, we present a methodology to predict AGB by integrating spectral remote sensing and allometric growth theory. We first demonstrate that leaf organs predominate in determining canopy spectral characteristics, with non-leaf components exhibiting minimal influence on spectral signatures. Building on this theoretical foundation, we developed a two-stage estimation framework that first quantifies leaf biomass using canopy spectral indices and subsequently predicts non-leaf organ biomass through stage-specific allometric growth relationships. Results demonstrate the substantial improvements in estimation accuracy, with the framework achieving an R2 of 0.79 and RMSE of 300.09 g/m2. Compared to direct spectral estimation of total AGB, we significantly improve prediction accuracy, demonstrating a 216 % increase in explanatory power and a 46.69 % reduction in error. The framework's robustness across environmental and temporal scales validates its theoretical foundation and practical utility. These findings advance our understanding of biomass allocation dynamics while providing a rigorous approach for non-destructive biomass estimation in maize cultivation systems.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序