Supervised Classification of Aerosol Types from POLDER-3 and OMI satellite data

Abstract

Aerosol is defined as the suspension of solid or liquid in the atmosphere. While some aerosols do not pose any serious threat to humankind, others have devastating effects. Thus, it is important to understand the type and distribution of these aerosols in the atmosphere. While traditional satellite data pose delays due to the huge amount of data that it has to process through the traditional pipeline, machine learning is quickly proving to be a likely winning candidate in providing accurate and efficient models. The advances in machine learning and cloud computing combined with the terabytes of data from the earth observation satellites open up avenues for creating newer and variant data products of better accuracy in the domain of aerosol classification. There is a recognized need for distinguishing and characterizing different kinds of aerosols in the 5.6 billion dollar air quality market. This research focuses on the investigation and designing of machine learning models for the aerosol retrieval process. To meet this end we implement supervised learning on satellite data to achieve aerosol classification to distinguish the different types of aerosols. The two satellites whose data will be analyzed are POLDER-3 and OMI. In this study, the three supervised learning algorithms SVM, RF and KNN were implemented to classify aerosol types for the year 2006 on POLDER-3 and OMI satellite data. We used results from previous studies on POLDER-3 as eight input aerosol class labels along with selected microphysical parameters for supervised learning and could achieve a very high reproducibility of the aerosol classes with a reduction in training time. Similarly, we used three aerosol label classes as input along with selected microphysical parameters to generate a high reproducibility. The results showed that SVM performed best on POLDER-3 data while RF was the best performing algorithm on OMI data. Using SVM on POLDER-3 dataset with hyperparameter tuning we reached an overall accuracy of 99%, precision of 99%, recall of 98%, and f1-score of 99% on POLDER-3 dataset for the eight classes of aerosols. Using RF on OMI dataset with hyperparameter tuning we reached an overall accuracy of 99%, precision of 99%, recall of 99%, and f1-score of 99% on OMI dataset for the three classes of aerosols. It was concluded that while machine learning still has a long way to go, it shows promising results in the field of satellite data processing for aerosol classification.