A method of analyzing LAI underestimation for dense vegetation based on the vertical distribution of the leaf area density. Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Zhao, Jing; Li, Jing; Liu, Qinhuo; Yang, Le; Bai, Junhua
• Is Part Of: Remote sensing letters. Volume 9:Issue 2(2018)
• Page Start: 121
• Page End: 130
• Publication Date: 2018-02-01
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2017.1399471
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Canopy reflectance saturation is a major cause of leaf area index (LAI) underestimation, especially for vegetation types with large LAI values. For a dense canopy, the vertical distribution of the leaf area density is a factor that cannot be ignored. The multilayer Scattering by Arbitrary Inclined Leaves model considering Hotspot effects (SAILH) was combined with vertical profiles of the leaf area density to simulate the layer reflectance and corresponding layer contributions of a dense corn canopy. Canopy reflectance saturation in this paper was defined as a layer reflectance contribution less than 5%, and the sum of the LAI values of these layers directly resulted in LAI underestimation. The results showed that the degrees of canopy reflectance saturation and LAI underestimation increased with increasing LAI and strongly depended on the vertical distribution of the leaf area density. The LAI was underestimated by 8.7% and 10.2% on average for true LAIs of 4.23 and 4.69, respectively. The simulated LAI underestimations were validated based on LAI inversion from field spectral measurements for corn. LAI underestimation was compared between the model simulations and inversions. The differences in LAI underestimation were 0.19 and 0.13 for true LAIs of 4.23 and 4.69, respectively.