Dust Storm Remote Sensing Monitoring Supported by MODIS Land Surface Reflectance Database

Abstract

MODIS (Moderate Resolution Imaging Spectroradiometer) land product subsets can provide high-quality prior knowledge for the quantitative inversion of land and atmospheric parameters. Using the LSR (Land Surface Reflectance) dataset, dust storm remote sensing monitoring in this study was carried out via quality control and data synthesis. A dynamic threshold supported dust storm monitoring method was proposed based on a monthly synthesized LSR database, which is produced using MOD09A1 data. The apparent reflectance of clear-pixels with different atmospheric conditions was simulated by the radiative transfer model. A pixel can be identified as a dust pixel if the apparent reflectance is larger than that of the simulated data. The proposed method was applied to the monitoring of four dust storms, the results of which were evaluated and analyzed via visual interpretation, MICAPS (Meteorological Information Comprehensive Analysis and Process System), and the OMI AI (Ozone Monitoring Instrument Aerosol Index) with the following conclusions: the dust storm monitoring results showed that most of the dust areas could be accurately detected when compared with the true color composite images, and the dust monitoring results agreed well with the MICAPS observation station data and the OMI AI dust products.