Model-Based Sensitivity Analysis on Aerosol Optical Thickness Prediction

Abstract

Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k. The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy.