Many vegetation biophysical parameters, e.g. Leaf Area Index, have nonlinear relationships with spectral
reflectance, while Support Vector Regression (SVR) has fantastic nonlinear fitting ability owing to kernel inner product
and the sparseness of Support Vectors (SVs). Currently, most of the LAI inversion methods over Huanjing optical
satellites (HJ) are based on empirical relationship between LAI and spectral index, which have some limitations. In this
paper, we developed an algorithm combining SVR and physical model-PROSAIL to retrieve LAI over HJ-CCD image.
The model adopted a new HJ vegetation index (HJVI), which can lessen saturation on high LAI domain. Experiments
over simulations generated by PROSAIL model proved the new algorithm’s good performance on noising resisting and
effectiveness of HJVI. Finally, we applied the algorithm on a HJ1B CCD2 image and validated it with the field measured
data in Xinxiang, Henan Province, China. The RMSE of 0.5230 indicated the applicability of the SVR-based physical
method over HJ data.