Dust storm detection of a convolutional neural network and a physical algorithm based on FY-4A satellite data

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Quantitative identification of a dust storm weather is key to forecasting and early warning of dust storm disasters. However, traditional visibility ground-based measurements cannot be extended to regional observations. Remote sensing of dust storms is associated with large uncertainties in dust thresholds. For accurate quantification of the dust storm region, this study proposes a dust storm mask algorithm to identify dust storm in the Tarim Basin. The dust storm mask includes two algorithms to identify the dust storm outbreak and the spatial extent by using the Advanced Geostationary Radiation Imager (AGRI) on board the FY-4A satellite. A deep learning convolutional neural network (CNN) is employed for the dust storm mask, and the AGRI bands 1–3, 5–6, and 11–13 are used as model parameters. A physical algorithm (PA) is adopted to construct a dust storm mask using three physical dust indices: the Normalized Difference Dust Index (NDDI2.25μm-0.47μm/2.25μm+0.47μm), the Dust Ratio Index (DRI7.10μm/3.75μm), and the Brightness Temperature Difference (BTD3.75μm-13.50μm). The results show that the CNN algorithm has a higher classification accuracy on dust storm detection compared to the PA. This advantage suggests that the CNN can effectively monitor large-scale dust storms. The dust storm identification results were compared and analyzed with the AGRI true color, Aerosol Optical Depth products, and Ultra Violet Aerosol Index products.