Research Article | Open Access

Volume 2025 |Article ID 100100 | https://doi.org/10.1016/j.plaphe.2025.100100

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

Jinpeng Cheng,^1,6 Jiao Wang,^2,6 Dan Zhao,^3,6 Fenghui Duan,¹ Qiang Wu,¹ Yongliang Lai,¹ Jianbo Qi,⁴ Shuping Xiong,¹ Hongbo Qiao,² Xinming Ma ,^1,2 Hao Yang ,³ Guijun Yang ^3,5

¹College of Agronomy, Henan Agricultural University, Zhengzhou, 450046, China
²College of Information and Management Science, Henan Agricultural University, Zhengzhou, 450046, China
³Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
⁴Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
⁵College of Geological Engineering and Geomatics, Chang'an University, Xi'an, 710054, China
⁶These authors contributed equally to this work

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.