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

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