Maize Canopy and Leaf Chlorophyll Content Assessment from Leaf Spectral Reflectance: Estimation and Uncertainty Analysis across Growth Stages and Vertical Distribution

Accurate estimation of the canopy chlorophyll content (CCC) plays a key role in quantitative remote sensing. Maize (Zea mays L.) is high-stalk crop with large leaf area and deep canopy. It has non-uniform vertical distribution (LCC), which limits sensing CCC. Therefore, it crucial to understand heterogeneity LCC reflectance spectra improve accuracy CCC monitoring. In this study, CCC, LCC, spectral were measured during two consecutive field growing seasons under five nitrogen treatments. The profile showed an asymmetric ‘bell-shaped’ curve structure was affected by application. also varied greatly between spatio–temporal conditions, could indicate influence heterogeneity. early growth stage, differences positions mainly concentrated red-edge (RE) near-infrared (NIR) regions, whereas visible region mid-late filling stage. had strong linear correlation vegetation indices (VIs), such as modified ratio (mRER, R2 = 0.87), but VI–chlorophyll models significant inversion errors throughout season, especially at vegetative stage late (rRMSE values ranged from 36% 87.4%). total canopy, sensitive identified multiple stepwise regression (MSR) model. L6 (R2-adj 0.9) L11 + L14 reproductive 0.93) be used evaluate status (L12 represents ear leaf). With relationship can estimated directly rRMSE 8.97%). variations analyzed, higher approach that avoid effects proposed.