ABSTRACT 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.

中文翻译：

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基于MODIS观测的改进尘埃识别指数（IDII）

摘要 卫星遥感可以作为一种理想的探测高时空尺度沙尘暴的技术。在本文中，我们提出了一种基于中分辨率成像光谱仪 (MODIS) 图像的改进尘埃识别指数 (IDII)。IDII算法用于监测2016年至2018年西亚地区129次沙尘暴事件。 天气站地面观测、RGB图像、正交偏振云气溶胶激光雷达（CALIOP）和臭氧监测仪（OMI） ) 用于验证 IDII 算法。此外，将所提出算法的性能与全球灰尘检测指数（GDDI）进行了比较。结果表明，准确率（Ac）、正确阳性检测概率（POCD）、IDII 和 GDDI 的假阳性检测 (POFD) 和概率分别为 82%、85%、33% 和 71%、74%、27%。同样在水上作为一个具有挑战性的对象，可以清楚地看到 IDII 的性能比 GDDI 方法要好得多。我们的结果表明，所提出的方法可以处理灰尘检测算法的常见局限性，即不同表面（陆地和水）上的灰尘检测、季节性变化以及灰尘与其他物体（如云）之间的相似性。