A method of analyzing LAI underestimation for dense vegetation based on the vertical distribution of the leaf area density

作者:Zhao, J (Zhao, Jing)[ 1,2 ] ; Li, J (Li, Jing)[ 1,2 ] ; Liu, QH (Liu, Qinhuo)[ 1,2,3,4 ] ; Yang, L (Yang, Le)[ 1,2 ] ; Bai, JH (Bai, Junhua)[ 1,2 ]

REMOTE SENSING LETTERS

卷: 9  期: 2  页: 121-130

DOI: 10.1080/2150704X.2017.1399471

出版年: 2018

文献类型:Article

摘要

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.

通讯作者地址: Li, J (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Jointly Sponsored Inst Remote Sensing & Digital E, Beijing 100101, Peoples R China.

通讯作者地址: Li, J (通讯作者)

Beijing Normal Univ, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Jointly Sponsored Inst Remote Sensing & Digital E, Beijing 100101, Peoples R China

[ 2 ] Beijing Normal Univ, Beijing 100101, Peoples R China

[ 3 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China

[ 4 ] Joint Ctr Global Change Studies, Beijing, Peoples R China