Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations

TL;DR

This paper develops a framework using machine learning and mutual information to identify variables influencing biases between MODIS satellite and AERONET ground measurements of aerosol optical depth.

Contribution

It introduces a multivariate regression approach with neural networks and a brute-force variable selection method to pinpoint factors affecting measurement bias.

Findings

Identified key variables influencing aerosol optical depth bias.

Demonstrated the effectiveness of neural networks in multivariate regression.

Implemented a parallel computing approach for large-scale variable analysis.

Abstract

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program.