In this paper, the AOD over Beijing on Sep. 4th and Oct. 6th, 2014 are retrieved by applying DDV method using Landsat8/OLI data. Both cases show that retrieved AOD over Beijing has a higher value over urban areas and a relative lower value over mountainous area, and the distribution of AOD is greatly influenced by the land cover type. The ground-based PM2.5 data is also used to validate the derived AOD, and there is a good agreement between the AOD and PM2.5 concentrations with a high correlation coefficient of 0.8742. The calculated AOD reflects the detailed information of the atmosphere over Beijing due to its higher spatial resolution, and it is concluded that the DDV algorithm can be applied to Landsat8/OLI to retrieve AOD over Beijing.
Recently, aerosol optical depth (AOD) study has become more important in the field of atmosphere sciences. AOD datasets retrieved from satellites are widely used in multiple fields because of their wide coverage and low cost. However, the integrity of AOD spatial coverage can be easily influenced by clouds, rain, haze and other weather phenomena. Fortunately, the full coverage AOD images are producible by employing the data fusion algorithm and ancillary methods. Based on AOD data derived from MODIS and OMI with meteorological parameters on November 18, 2013 over the East China, this study combined the universal kriging with stepwise regression and second-order polynomial fitted to extend the coverage of MODIS AOD at 550 nm. Results showed that stepwise regression method is efficient to infer the MODIS AOD by using the OMI AOD and meteorological parameters. The wind speed, relative humidity, pressure and solar radiation have significant impacts on the spatial and temporal distributions of AOD. The mean prediction error of universal kriging prediction model is 0.0047 in this paper, indicating that the universal kriging is an effective and accurate interpolation method for AOD data fusion. The methods employed in this paper can provide the data source of AOD for studies in climate and other related fields, effectively compensating the non-full coverage shortcoming of satellite AOD datasets.
Aerosol optical depth (AOD) is a key indicator of atmospheric environment. Aerosol remote sensing is the most efficient way to obtain the temporal and spatial distributions of AOD. In this paper, the data from Environment Satellite (HJ-1) CCD camera were employed to retrieve AOD by using deep blue algorithm over the Yangtze River Delta. The third band (in blue) was firstly extracted from the MODIS land surface reflectance product (MOD09) and then converted to the first band of CCD/HJ-1. According to the characteristics of the study area and CCD data, a multi-dimension look up table was then built by the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). AOD over the Yangtze River Delta were finally retrieved from the radiance of the first band of CCD/HJ-1. After the retrieved AOD were validated by the MODIS AOD product (MOD04), the correlation coefficient (R) is 0.64 by regression of all cloud screened pixels (1147). The retrieved AOD has a higher spatial resolution than the MODIS AOD and thus can provide more detailed information. Compared with the AERONET ground observation data, the retrieved AOD is closer to the ground-based data than the MODIS AOD.