Abstract
<p>Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. However, the spatial resolution of AOD, based on Moderate-resolution Imaging Spectroradiometer (MODIS), is low, and hard to meet the needs of regional air quality fine monitoring. In 2018, China launched Gaofen-6 satellite, which set up a network with Gaofen-1 enabling two-day revisited observations in China's land area, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. Along with advancement of the Earth Observation System and the launch of high-resolution satellites, it is of profound significance to give full play to the active role of high-scoring satellites, in monitoring atmospheric environmental elements such as atmospheric aerosols and particulate matter concentrations, and achieve high-resolution retrieval of AOD through Gaofen satellites.</p><p>In this paper the data of Gaofen-6 and Gaofen-1 was used to retrieve the AOD. based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm. This algorithm can retrieve the surface reflectance and AOD synchronously through constructing a closed equation based on double star observations. It can be applied to various types of surface reflectance which extends the coverage of the retrieval of AOD inversion effectively. Experimental data includes the satellite data of New South Wales and eastern Queensland on November 21, 2019, which have been suffered from unprecedented large-scale forest fires for over 2 months. The retrieval of AOD during the time with the satellite data is benefit for the prevention and monitoring of forest fire. The experimental results are compared with the AERONET ground observation data for preliminary validation. The correlation coefficient is about 0.7. The experimental results show that the method have higher accuracy, and further validation work is continuing.</p>
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