干旱区生态环境知识资源中心

This paper compares and evaluates four principal methods of dust source and plume identification using MODIS data. The four MODIS methods used here are: (i) Roskovensky and Liou’s dust identification algorithm, (ii) Ackerman’s model, (iii) Normalized Difference Dust Index (NDDI), and (iv) Deep Blue algorithm. These techniques were applied to three recent significant events in the Middle East region. In addition, true color images and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used to evaluate the result of each technique. To optimize the result of dust detection for each technique, the published dust-nondust thresholds had to be considerably adjusted on an event-by-event basis. Results show that techniques that use brightness temperature (BT) difference are the most reliable techniques for dust source detection in several situations such as multiplume and multimineralogical conditions (unlike the NDDI index and other optical based algorithms). However, these techniques cannot effectively differentiate dust plume from the bright desert surfaces (due to the same thermal behavior of dust and desert surfaces in both BT31 and BT32). This weakness impelled us to develop a new model based on Ackerman’s technique because of its more precise results in dust source identification. In this new presented model called Middle East Dust Index (MEDI), BT29 was involved to highlight the difference between dust and desert surfaces as the [(BT31-BT29)/(BT32-BT29)] equation. In this equation, the values of dusty pixels are less than 0.6 while nondusty pixels are greater than 0.6. Results indicate that the MEDI model is ideal in both identifying dust plume and sources and desert surfaces. Finally, due to some misclassification of the MEDI model in differentiating cirrus clouds from dust plumes, the NDDI index was added to the initial model to distinguish them more accurately.