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Measurements made by satellite remote sensing, Moderate Resolution ImagingSpectroradiometer (MODIS), and globally distributed Aerosol Robotic Network(AERONET) are compared. Comparison of the two datasets measurements for aerosoloptical depth values show that there are biases between the two data products.In this paper, we present a general framework towards identifying relevant setof variables responsible for the observed bias. We present a general frameworkto identify the possible factors influencing the bias, which might beassociated with the measurement conditions such as the solar and sensor zenithangles, the solar and sensor azimuth, scattering angles, and surfacereflectivity at the various measured wavelengths, etc. Specifically, weperformed analysis for remote sensing Aqua-Land data set, and used machinelearning technique, neural network in this case, to perform multivariateregression between the ground-truth and the training data sets. Finally, weused mutual information between the observed and the predicted values as themeasure of similarity to identify the most relevant set of variables. Thesearch is brute force method as we have to consider all possible combinations.The computations involves a huge number crunching exercise, and we implementedit by writing a job-parallel program.

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