Leaf Area Index Retrieval Using Red Edge Parameters Based on Hyperion Hyper-spectral Imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Remote sensing technology provides a practical way to estimate LAI at a large spatial scale, and hence, considerable effort has been expended in developing LAI estimation models from remotely sensed imagery. LAI estimation models were usually formulated using multi-spectral satellite imagery, and hyper-spectral satellite data was scarcely used because it is very difficult to acquire the needed hyper-spectral satellite imagery. Compared to multi-spectral imagery, hyper-spectral imagery has its advantage in LAI retrieving because hyper-spectral data can be used to extract red edge optical parameters, which provides a new way to estimate LAI. In this paper, EO-1 hyperion hyper-spectral imagery was used to estimate LAI in the forested area of Yongan county, Fujian province, located in southeast of China. Two primary red edge optical parameters, red edge position (REP) and red well position (RWP), were extracted from hyperion data; and LAI estimation models for broad-leaf forest in Fujian province were formulated.