Leaf area index inversion corrected with clumping index of maize canopy

Leaves are the main organs of photosynthesis and transpiration in green plants． Leaf area index (LAI) is an important indicator reflecting both light utilization and structure of plant canopies． With advancements in remote sensing technology, remote sensing inversion of LAI based on physical models has become an essential method to monitor crop growth and to estimate crop yield． Typically, it is assumed that crop canopy structure is uniformly distributed and one-dimensional radiation transfer-model (1D-RTM), such as PROSAIL, is used for inversion． However, two issues are often overlooked. Is the crop canopy structure of truly uniform distribution, and if not, how does this assumption affect inversion results? In this study, we take maize, an important grain crop, as an example to study． Initially, 3D scenes of maize canopies are reconstructed． We quantitatively compare reflectance simulated by PROSAIL model (1D-RTM) and LESS model (3D-RTM) to reveal deviation in simulated reflectance caused by non-uniform distribution of leaves during different growth stages of maize canopy． Considering variations of clumping index throughout the growth process of maize canopy, we use LESS model to calculate corresponding gap fractions and clumping indices for typical maize canopy structures, and establish a relationship between clumping index and effective leaf area index (LAI_e)． Furthermore, we use clumping index to correct vegetation LAI_e obtained from PROSAIL model, yielding actual LAI． We show that accuracy of corrected LAI has significantly improved, with R² increasing from 0.27 to 0.55. This method is expected to enhance precision of crop LAI inversion using high-resolution remote-sensing data．