## 1 Introduction
(2020) developed a SVM-based algorithm to detect dust storms from MODIS satellite image and they used the UV aerosol-index from the OMI (Ozone Monitoring Instrument) on board the Aura satellite, also part of the A-Train, to assess the detection results [29]. As mentioned above, the physically-based methods for passive sensors often suffer from a variety of problems, which could in turn influence the ML-based methods if they are used as the training and/or testing dataset. This study is motivated by the importance and wide application of satellite-based dust detection, and inspired by the limited success of the above emerging studies. Our main objective is to develop global dust detection algorithms based on ML methods to detect dust pixels in cloud-free conditions from the daytime satellite images by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite mission. We use the collocated VIIRS and CALIOP dust detection product as the training and testing dataset. Similar to MODIS, VIIRS is a passive sensor with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. Note that Suomi-NPP is not part of the A-Train. Thus, the VIIRS-CALIOP collocation is more challenging than the MODIS-CALIOP collocation. Nevertheless, VIIRS is chosen in this study for two important reasons. First of all, MODIS sensors have operated for almost 20 years and are coming to the end of their life cycle. In contrast, VIIRS flies not only on the Suomi-NPP mission but also the follow-up Joint Polar Satellite System (JPSS) missions that are designed to serve the U.S. for many years to come. Secondly, although CALIOP and MODIS can be easily collocated, the MODIS pixels along the CALIOP ground-track all have a similar near-nadir viewing angle as a result of their almost identical satellite orbits. Because dust reflection in the visible and the transmittance in the infrared are both dependent on the viewing angle, the limitation in the viewing angle sampling can lead to biases in the training data and thereby the ML-based algorithm [15; 31; 32; 33], especially when the trained algorithm is applied to the pixels off the collocated CALIOP track (hereafter referred to as the \"off-track pixels\"). In contrast, because CALIOP and Suomi-NPP are in different satellite orbits, the collocated pixels can sample a much larger range of viewing angle from \(-60\) to \(60\)[34]. As shown later, our ML-based detection algorithm shows similar skills both along and off the CALIOP track, which probably benefited from the unbiased viewing angle sampling in the collocated VIIRS-CALIOP data. Another objective of this study is to test and compare different ML methods for dust detection. More specifically, we will show results from five different ML based methods including logistic regression (LR), K-nearest neighbors (KNN), random forest (RF), feed forward neural network (FFNN), and convolutional neural network (CNN). The experience gained from this comparison will provide valuable guidance for the future development of ML based satellite remote sensing algorithms. In comparison with the previous studies, this research is novel and important in several regards. (1) The ML-based methods can help avoid many aforementioned problems facing the physically based algorithms. For example, ML-based methods have been shown in many studies to be much more flexible than the threshold-based methods, which helps dust detection in miscellaneous environments encountered when the algorithm is applied on a global scale. Moreover, in our study, instead of using a subset of VIIRS bands, which is a common practice in physically-based methods, we use all the 16 moderate resolution bands. (2) In comparison with the aforementioned ML-based dust detection studies, we use the collocated CALIOP products as the training and validation data sets. Moreover, we aim to develop a general detection algorithm that can be applied on a global scale, which is more challenging and at the same time more useful than regional algorithms.