Pixel Based Model For High Latitude Dust Detection

Current methods of dust detection rely on spectral sensitivity at visible (RGB) and infrared wavelengths. However, their application on different regions needs to be tuned to mitigate errors associated with background properties. High latitude dust (HLD) regions are characterized by surface with variable albedos and land cover, thus further complicating the dust detection. Leveraging supervised machine learning (ML) methods, we propose a new method accounting for regional differences of dust occurrence.