Next-generation remote sensing and prediction of sand and dust storms: State-of-the-art and future trends

ABSTRACT

Sand and dust storms (SDS) have long been considered as a type of disastrous weather. Minimizing the severe influence of SDS on the environment and human life has been the focus of many studies. In this paper, we report the principles and characteristics of SDS while mainly focusing on the monitoring technologies of the natural disaster. Space- and ground-based monitoring are two types of technologies widely used in SDS monitoring. The space-based monitoring technologies mainly refer to the satellite remote sensing technologies, which are able to obtain large-scale accurate ground monitoring within a few days. The ground-based monitoring technologies typically perform short-range and small-scale ground monitoring within a specific area. In recent years, artificial intelligence (AI) has been applied in SDS monitoring to solve the existing limitations in SDS monitoring by improving the accuracy and efficiency of the monitoring and prediction results. Although the applications of AI in SDS sensing and prediction are still at an early stage, AI-enabled hybrid systems are envisioned as a major developing trend for SDS monitoring in the future.

Acknowledgements

This study is supported in part by the Key Research and Development Plan of Zhejiang, China (2021C03180, 2021C03181), the Fundamental Research Funds for the Central Universities, China (2020-KYY-529112-0002). P.J. acknowledges the support from the Hundred Talented program at the Zhejiang University, China. J.W., X.C., and J.R. acknowledge the support from the Student Research and Training Program (SRTP) at the Zhejiang University, China.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Key Research and Development Plan of Zhejiang, China [2021C03180,2021C03181]; Fundamental Research Funds for the Central Universities [2020-KYY-529112-0002].