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Atmospheric aerosols play an important role in climate change research. It was found that different algorithms and instruments produce somewhat different results for aerosol optical depth (AOD) even if the same location at the same time is observed. Therefore, it is critical to integrate data from multiple platforms and techniques to derive a consistent AOD product. This paper introduced an approach...
In this paper, an advanced synergetic algorithm for aerosol retrieval from small satellite data is presented and applied on MODerate resolution Imaging Spectroradiometer (MODIS) and the Hyper-Spectral Imager (HSI) data from China HJ-1A satellite of the Environment and Disasters Monitoring Microsatellite Constellation. Using this algorithm, 500m MODIS data are downscaled to 100m based on maximal mutual...
As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the range of the estimates of aerosol's climate effects. In this paper, three different data assimilation...
On Oct. 28th, 2009, a heavy fog hit East China. From the MODIS RGB composite image, it can be seen clearly that, much of land of the area (110°-123°E, 30°-42°N) was covered by the heavy fog or cloud. Using MODIS multi-satellite algorithm for aerosol optical depth (AOD) retrieval, we get the AOD Map at 470, 550 and 660 nm from MODIS data at 1km × 1km resolution. By Validating with the AERONET data,...
Aerosol Optical Depth (AOD) is a vitally important physical parameter of aerosol which can be retrieved from satellite remote sensing data. The traditional algorithm such as MODIS aerosol algorithm is only appropriate for the dark pixels which have low reflectance. If the surface reflectance is high, the algorithm is ineffective. In this paper we proposed an advanced quantitative retrieval algorithm...
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