## 6 Conclusions and Outlook  
In this study, several ML based algorithms, namely, LR, KNN, RF, FFNN, and CNN, are tested for detecting the occurrence of dust aerosols from daytime VIIRS satellite images. These algorithms are trained based on one year (2014) of collocated state-of-the-art CALIOP dust detection products. Based on different combinations of pixel and predictor variable selections, four sets of input data are constructed for the training (i.e., 0D-AllVar, 0D-SelectVar, 2D-AllVar, 2D-SelectVar). A validation based on the testing data shows that the FFNN trained with the 2D-allVar input data is the best performing model, with a 84.99% overall accuracy on the CALIOP track. The FFNN model was then tested for the entire VIIRS granule, which covers more regions than CALIOP-VIIRS collocated track spatially. Off-track FFNN dust distribution agrees reasonably well with collocated track FFNN dust distribution, which indicates that there was no systematic bias between on and off track model application. In addition, off-track dust distribution showed a similar distribution with on-track CALIOP predictions, which warrants that the FFNN can be used to reproduce CALIOP results outside the CALIOP track. Comparisons are also made between the FFNN method with a physically-based dust detection algorithm both on and off the CALIOP track. The FFNN method agrees better with the CALIOP than the PHYS model on the CALIOP track, which is expected because FFNN is trained based on CALIOP observations. When applied to all of the VIIRS granules, the FFNN detects significantly more dust than the PHYS model. Case studies suggest that the difference may be coming from a number of different factors, such as the different definitions of the dust event and the use of different numbers of VIIRS observations in the two algorithms. As far as we know, this is the first attempt to use an ML based algorithm trained with the collocated CALIOP products for global dust detection from satellite images. The results suggest that, even though the ML algorithms are only trained on the narrow track of CALIOP, they can be applied to the whole VIIRS granule and retrieve statistically similar dust frequency. This is very encouraging and meaningful for a couple of reasons. First of all, this study, along with many recent ones (e.g., [34]), demonstrates the great potential of ML methods for satellite based aerosol and cloud retrievals. Second, our study also demonstrates that, even through the active sensors like CALIOP have an extremely small spatial sampling rate, their observations and retrievals provide unique information on aerosol that are highly useful for training the ML methods. However, a close collocation between the active and passive sensors are needed, which is an important factor to be considered in the planning of next-generation NASA satellite missions. Despite the encouraging results, our study has several important limitations. First, the abnormally high frequency of dust in the Antarctic and some remote ocean regions indicate that our ML methods inherited some misidentification problems from the CALIOP retrieval. The problems of the operational CALIOP aerosol retrievals are beyond the scope this study. In future research, this issue could be alleviated by using additional constraints, such as the AOD value, for dust detection when constructing the training data. Second, because we excluded cloudy pixels from this study, our algorithms can only detect dust in cloud-free conditions and require a reliable cloud masking algorithm to screen out the cloudy pixels first. Theoretically, it is possible to train ML algorithms to identify both clouds and dust at the same time if there can be enough high-quality training data. However, this would be much more challenging and will be explored in future research. Third, our algorithms can be used only during the daytime. In future research, the ML algorithms can be trained only using the thermal infrared bands of VIIRS and as such they will be able to detect dust during both the daytime and nighttime.