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Satellite remote sensing may serve as an ideal technique to detect dust storms for high temporal and spatial scales. In this paper, we propose an improved dust identification index (IDII) based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The IDII algorithm was used to monitor 129 dust storm events over the West Asia region from 2016 to 2018. Ground-based observations of synoptic stations, RGB images, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Ozone Monitoring Instrument (OMI) were implemented for validating IDII algorithm. In addition, the performance of the proposed algorithm was compared with the Global Dust Detection Index (GDDI). The results show that the accuracy (Ac), Probability Of Correct positive Detection (POCD), and Probability Of False-positive Detection (POFD) for the IDII and GDDI are 82%, 85%, 33%, and 71%, 74%, 27%, respectively. Also over the water as a challenging object, it is clearly seen that the IDII performs much better than GDDI method. Our results suggest that the proposed approach can deal with the common limitations of dust detection algorithms, i.e. dust detection over different surfaces (land and water), seasonal changes, and similarity between dust and other objects like clouds. [ABSTRACT FROM AUTHOR]