The LAI Inversion based on Directional Second Derivative using Hyperspectral Data

Leaf area index (LAI) is an important structure parameter of vegetation system. The quantitative remote sensing can offer two dimensional distribution of LAI. The variation of background, atmospheric condition and canopy anisotropic reflectance were the three factors that can restrain the retrieved accuracy of LAI. Along with the emergence of hyperspectral remote sensor, such as Hyperion, it´s possible to calculate LAI using the second derivative method in spectral dimension. The second derivative can reduce the influence of background and improve the accuracy of LAI inversion. In order to integrate the second derivative into physical model and eliminate the influence of canopy reflectance anisotropy, we propose a new directional spectral second derivative method. Firstly a new hybrid canopy model was used, and then the directional spectral second derivative was deduced from the hybrid model, so the effects of anisotropy of canopy reflectance and background were removed in theory. Numerical and field tests show the noise can greatly impact the directional second derivative method. We put forward an innovative noise filtering approach in spectral and space domains, the directional second derivative worked well on the LAI retrieval by Hyperion image.