Improving the retrieval of the biophysical parameters of vegetation canopies using the contribution index

Abstract

Biophysical parameters, such as leaf area index (LAI) and leaf chlorophyll content, play crucial roles in precision agricultural management, forest ecology monitoring, and global climate change studies. Accurate and robust retrieval of these parameters from remote sensing data still remains a challenge. One of the commonly used methods is through the inversion of a physical canopy model. However, it is often an ill-posed problem, mainly due to the model complexity and observation uncertainties. In this study, a contribution index (CI) was derived to quantify the effect of a given observation on the retrieval of model parameters of interest that accounted for both the uncertainty of this observation and its sensitivity to the model parameters. The CI was used in the merit function to weight each observation to improve the physical model inversion. To evaluate the CI based merit function, the look-up table (LUT) model inversion was conducted using the coupled PROSPECT and SAIL model to retrieve LAI and leaf chlorophyll content. The results using both simulated and real hyperspectral data showed that employing CI significantly improved the retrieval accuracy by reducing the prediction errors by at least 10 % compared with the traditional LUT method.