Machine Learning-Enhanced Aerosol Type Classification: Global Multi-Parameter Aerosol Modeling and an Automated Classifier Driven by Active–Passive Remote Sensing Parameters

Abstract. Accurate classification of aerosol types is crucial for regional aerosol pollution source tracking and optical remote sensing studies. However, conventional classification methods are mostly relying on the single-device observation that provides limited optical parameters, and the use of fixed empirical thresholds that limits the classification accuracy. This study develops a machine learning-enhanced aerosol types classification method that integrates active and passive optical remote sensing data. First, the global multi-parameter aerosol model is built from high-quality aerosol product datasets of 19 representative AERONET sites worldwide, 7 distinct aerosol type classes were established using the K-means clustering algorithm. Then,
based on the established aerosol database with multiple characteristic parameters, we propose a random forest-based aerosol types classifier, which uses only active-passive remote sensing directly retrieved parameters as inputs. This classifier is evaluated to achieve an overall accuracy of approximately 80% and successfully classifies the dominant aerosol types at 27 typical AERONET sites. This study offers a practical and reliable method for combining active and passive remote sensing data to automated aerosol types classification.