Leaf Area Index Estimation Using MESMA Based on EO-1 Hyperion Satellite Imagery
Keywords:
Leaf area index, hyper-spectral satellite imagery, MESMA.Abstract
Leaf area index (LAI) is an important surface
biophysical parameter used by many process-oriented
ecosystem models. Traditionally, remote sensing based
techniques to estimate LAI have either been based on the
empirical–statistical approach that relates ground-measured
LAI to the spectral vegetation indices, or on a radiative transfer
modeling approach. However, both approaches have their
limitations. In recent years, much effort has been expended to
develop new remote sensing based LAI estimation methods.
Multiple endmember spectral mixture analysis (MESMA) is an
important one in the newly developed LAI retrieval methods.
The aim of this study is to test the effectiveness of MESMA in
LAI retrieval of broad-leaf forest in Asian subtropical monsoon
climate region. In this study, EO-1 hyperion hyper-spectral
imagery acquired on May 22rd, 2012 was employed to carry out
an experiment on the MESMA method to estimate LAI in the
forested area of Yongan county, Fujian province, located in
southeast of China. MESMA based LAI estimation model for
broad-leaf forest in the study area were finally formulated. The
result shows MESMA method can achieve a good LAI
estimation result.
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