Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia. Issue 4 (15th February 2020) - Shi, Lamei; Zhang, Jiahua; Zhang, Da; Igbawua, Tertsea; Liu, Yuqin
Highlights: Integrates Support Vector Machine with satellite remote sensing to detect dust. Combines new and existing algorithm to improve the accuracy of dust detection. The developed DSD_SVMS is more reliable and efficient than threshold-based method. B7-B3, B20-B31 and B31/B32 are the optimum feature vectors in dust identification. Abstract: Enhancing the dust storm detection is a key part for the environmental protection, human healthy and economic development. The goal of this paper is to propose a new Support Vector Machine (SVM)-based method to automatically detect dust storms using remote sensing data. Existing methods dealing with this problem are usually threshold-based that are of great complexity and uncertainty. In this paper we propose a simple and reliable method combining SVM with MODIS L1 data and explore the optimal band combinations used as the feature vectors of SVM. The developed method was evaluated by MODIS and OMI data qualitatively and quantitatively on three study sites located in the Arabian Desert, Gobi Desert and Taklimakan Desert, and it was also compared to three other traditional methods based on their accuracy, complexity, reliability and sensitivity to thresholds. The detection results demonstrated that the combination of (Band7 − Band3)/(Band7 + Band3) ((B7 − B3)/(B7 + B3)), Band20 − Band31 (B20 − B31), and Band31/Band32 (B31/B32) can detect the dust storms more precisely than other individual bands or their combination. The comparison among those cases indicated that the proposed automatic method exhibited an advantage of minimizing the uncertainty and complexity, which were the limits of defining thresholds based on the threshold-based methods. The conclusions can provide references for studies that focus on statistical-based dust storm detection.