The leaf area index is one of key parameters for ecosystem monitoring, global carbon circulation and climate change. The remote sensing data from different satellites have
become the primary data source for estimating leaf area index from regional to global scale. In this study, we assess the potential use of Landsat TM, HJ-1 CCD, and SPOT
HRVIR sensors for leaf area index estimation in a mountainous area. Results suggest that three sensors behave similarly for LAI inversion over complicated terrain. The
maximum correlation coefficients are in the order of broadleaf forest (0.82) > shrub/grass (0.78) > all plots (0.61) > needleleaf forest (0.53), which are derived from the field LAI-SPOT SWVI, TM SR, SPOT SWVI, and SPOT RSR relationships for all plots, needleleaf forest, broadleaf forest, and shrub/grass, respectively.