The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient (R) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of − 0.735, and both have a moderate association with AOD with a correlation of 0.451 and − 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.
Color distortion is the main weakness of the current fusion techniques, which occurs because of radiometric differences between panchromatic (PAN) and multispectral (MS) images. In this study, a novel fusion technique was developed to merge the PAN and MS images of the GeoEye-1 satellite and produce superior fused images without color distortion. This technique proposes reducing the difference in radiometry between the PAN band and the MS bands by using only the parts of the MS bands inside the area of the PAN band. Therefore, modification coefficients were used for the MS bands in the definition of the intensity (I) equation based on their overlapping areas with the PAN band. As the reflectance of vegetation is high in the NIR band and low in the RGB bands, this technique suggests using an additional coefficient for the NIR band in the definition of the I equation to add the correct effect of vegetation. This coefficient is variable for all types of land cover based on the percentage of the agricultural areas within the image, which leads to significant and stable performance for all types of land cover. This study aims to evaluate the performance of this technique by a comparison with five standard fusion techniques: fast-intensity-hue-saturation, principal component analysis, Gram Schmidt fusion, hyper-spherical color space, and Ehlers fusion. Three datasets of GeoEye-1 satellite PAN and MS images in Tanta City, Egypt, with different land cover classes (agricultural, urban, and mixed areas), were used in this study. The output fused images were compared with the original PAN and MS images by statistical analysis and visual inspection. The proposed fusion technique demonstrated great efficiency in producing fused images of superior spatial and spectral quality for all types of land cover.
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