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.
Highlights
Dust storms refer to strong winds that roll up a large amount of sand and dust from the ground and make the horizontal visibility less than 1 km
The first part of this paper describes a brief review of the topic and previous research performed in this area; the second part presents the process of atmospheric radiation transmission, the spectral characteristics of dust and a typical surface, the generation process of the surface reflectance database, and the method of dynamic threshold of dust identification; the third part tests and verifies the four dust storms processes
This paper analyzed the spectral properties and mixed pixels of six typical land objects and proposed a dust storm monitoring method using dynamic threshold supported by the LSR dataset
Summary
Dust storms refer to strong winds that roll up a large amount of sand and dust from the ground and make the horizontal visibility less than 1 km. They have the characteristics of sudden occurrence and short duration. With multi-source, dynamic, current, and accurate properties, satellite remote sensing technology has the characteristics of wide coverage, continuous space, and fast and dynamic observation, which can play an important role in dust storm monitoring. MICAPS is a human–computer interaction system that supports weather forecast production It processes kinds of meteorological information such as ground observation data, aerological sounding data, satellite cloud data, numerical forecast data, radar data, typhoon path data, urban forecast data, etc. As MODIS imagery has a wide range and large span, observation data captured at 11 am and 2 pm (Beijing time), which are close to the imaging time, were adopted for the monitoring experiments
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