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.
Index Terms— Leaf area index, hyper-spectral satellite imagery, MESMA.
Zhaoming Zhang,Guojin He , and Qin Dai are with the Centre for Earth Observation and Digital Earth,CAS, Beijing, 100094, China (e-mail: zmzhang@ ceode.ac.cn, gjhe@ ceode.ac.cn, qdai@ ceode.ac.cn).
Hong Jiang is with the Spatial Information Research Centre, Fujian Province, Fuzhou 350002, China (e-mail: jh910@ fzu.edu.cn).
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Cite: Zhaoming Zhang, Guojin He, Qin Dai, and Hong Jiang, " Leaf Area Index Estimation Using MESMA Based on EO-1 Hyperion Satellite Imagery," International Journal of Information and Electronics Engineering vol. 4, no. 1, pp. 11-15, 2014.
