Indirect measurement of forest leaf area index using path length model and Multispectral Canopy Imager

Non-randomness within canopies and woody component are two factors limiting the accuracy of indirect leaf area index (LAI) measurement. Here we combine the path length distribution model and Multispectral Canopy Imager (MCI) together for the first time to improve the accuracy. The results show that non-randomness within canopies underestimates 17.1%-28.2% LAI, while woody component overestimates 14.6%-27.8% LAI in four forest sites. Although these two factors were sometimes offset, the degree of non-randomness within canopies and the proportion of woody component vary in different forests. More attention should be paid to the impact of the non-randomness within canopies and the woody component, especially in coniferous forest dominated by tree trunks and branches.