Investigation and validation of a dust data fusion method based on monitoring data from geostationary and polar-orbiting satellites

The objective of this study was to integrate the advantages of multi-source remote sensing to monitor dust storms and better discriminate between regions where dust storms occur. Firstly, The traditional evidence theory algorithm was improved by not only considering the certainty of the evidence, but also considering the average level of support for the subsets of evidence in the discrimination framework in the process of evidence combination by reducing the conflict between synthesized data. Then the algorithm is applied to the FY-2E infrared difference dust index (IDDI) and the FY-3A dust strength index (DSI) to categorize the study region as either a dust storm area, non-dust storm area, or possible dust storm area. Finally, the result was validated and analyzed using the monitored data from ground stations. Both the accuracy and reliability of the dust monitoring results were considerably improved using our method.