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Air pollution is becoming more serious, especially in developing countries like Vietnam. Air pollution is affecting human health, especially atmospheric particulate matter (PM) with a diameter 2.5 μm (PM2.5) or 10 μm (PM10). Atmospheric particulate matter affect to scatter or absorb electromagnetic energy which reflect to satellite sensors. Hence, PM10 concentration can be determined based on ground measured data or satellite images. PM10 concentration is determined by multiple linear regression method using field measured value and atmospheric reflectance calculated by satellite images at the same time. The atmospheric reflectance is defined as the subtraction of top of atmospheric reflectance (TOA) and surface reflectance value. Therefore, atmospheric correction algorithm for surface reflectance plays an important role in determining PM10 concentration by using remote sensing data. In this article, the authors presented the assessment effect of atmospheric correction methods as DOS (Dark Object Subtraction), FLAASH (Fast Line-of-sight Atmospheric Analysis of Hyper cubes) and LaSRC (Landsat 8 Surface Reflectance Code) for accurate regression of PM10 concentration from satellite images. The experimental data were Landsat 8OLI of Hanoi area, Vietnam at three times on 22 January 2015; 30 May 2015 and 12 October 2016.

rossijskij žurnal nauk o zemle/russian journal of earth sciences

Assessment the effect of atmospheric correction algorithms for monitoring PM10 concentration by using Landsat 8OLI data: A case study in Hanoi, Vietnam