Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.