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

• Integrating spectral sensing with allometric growth models improves accuracy of maize above-ground biomass prediction. • Strong leaf-to-non-leaf biomass correlation enhances remote sensing estimates of maize above-ground biomass. • Maize canopy spectral characteristics are predominantly influenced by leaf components. 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 R 2 of 0.79 and RMSE of 300.09 g/m 2 . 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.