Evaluation of Maximum Likelihood Estimation and regression methods for fusion of multiple satellite Aerosol Optical Depth data over Vietnam

Abstract

This paper applied different data fusion methods including Maximum Likelihood Estimation (MLE) and Linear Regression methods on satellite images over Vietnam areas from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. In comparison with ground station Aerosol Robotic Network (AERONET), the regression method is better than Maximum Likelihood Estimator (MLE). Our results show that the fusion methods can improve both data coverage and quality of satellite aerosol optical depth (AOD). Strong correlations were observed between fused AOD and AERONET AOD (R2 = 0.8118, 0.7511 for Terra regression and MLE method, respectively). This paper presented the evaluation of data fusion algorithm and highlighted its importance on the satellite AOD data coverage and quality methods from multiple sensors.