Exploring the depth of the maize canopy LAI detected by spectroscopy based on simulations and in situ measurements.

Abstract

The vertical distribution of leaves plays a crucial role in the growth process of maize. Understanding the vertical spectral characteristics of maize leaves is crucial for monitoring their growth. However, accurate estimation of the vertical distribution of leaf area remains a significant challenge in practical investigations. To address this, we used a 3D RTM to simulate the layered canopy spectra of maize, revealing the impact of canopy structure on remote sensing penetration depth across different growth stages and planting densities. The results of this study revealed differences in detection depth across growth stages. During the early growth stage, the depth was concentrated in the bottom 1 to 3 leaves of the canopy, reaching 1 to 4 leaves at the ear stage and 1 to 7 leaves during the grain-filling stage. The planting density had a notable effect on the detection depth at the bottom of the canopy. Moreover, compared with the other spectral bands, the near-infrared spectral range exhibited greater sensitivity to density variations. In terms of LAI inversion, a FuseBell-Hybrid model was constructed. We analyzed VIs across different planting density and canopy structural scenarios and found that compared with lower layers, increased density reduced the relative change rate in the upper leaf layers. The sensitivity patterns differed between plant architectures: VIred exhibited density-dependent sensitivity, with distinct responses between plant types, and MTVI2 demonstrated optimal performance for mid-canopy monitoring. This study highlights the influence of the heterogeneous structural characteristics of maize canopies on remote sensing detection depth during different phenological stages, providing theoretical support for enhancing multilayer crop monitoring in precision agriculture.

Plant phenotyping relevance

分光計測と3D放射伝達モデルを用いてトウモロコシ冠層の検出深度およびLAI推定法を構築・評価しており、植物形質取得手法が研究の中心である。

Code and data availability

The paper states its analysis code was uploaded to a public GitHub repository, which qualifies as an authors' public code asset for the LAI phenotyping analysis. No separate phenotype dataset or model checkpoint deposit is explicitly stated.