Abstract: A spectral method is provided for partitioning type and loading with aerosol optical depth. Based on multi-spectral optical aerosol depth, particle-size distribution and refractive index are derived by normalizing first- and second-order derivatives for processing quantitative calibration of main components. According to the optical feature parameters of various aerosol types, a radiation theory is applied to simulate multi-spectral optical depth for each density, including those of mixed types. The intrinsic parameters of aerosol types are figured out by constructing normalized derivative aerosol indices (NDAI). The clear characteristic differences between aerosol types are used to figure out main components of aerosols and their mixing ratios. The simulation result of the normalized index of various aerosol type is in good agreement with the ground observation data of Aerosol Robotic Network. It shows that NDAI is quite practicable in quantitative calibration of main components of atmospheric aerosol.

Abstract: A spectral method is provided for partitioning type and loading with aerosol optical depth. Based on multi-spectral optical aerosol depth, particle-size distribution and refractive index are derived by normalizing first- and second-order derivatives for processing quantitative calibration of main components. According to the optical feature parameters of various aerosol types, a radiation theory is applied to simulate multi-spectral optical depth for each density, including those of mixed types. The intrinsic parameters of aerosol types are figured out by constructing normalized derivative aerosol indices (NDAI). The clear characteristic differences between aerosol types are used to figure out main components of aerosols and their mixing ratios. The simulation result of the normalized index of various aerosol type is in good agreement with the ground observation data of Aerosol Robotic Network. It shows that NDAI is quite practicable in quantitative calibration of main components of atmospheric aerosol.

Abstract: A fusion method is provided to obtain a spatiotemporal image. The present invention is based on a conventional model—a spatial and temporal adaptive reflectance fusion model (STARFM). In the present invention, top-of-atmosphere (TOA) reflectance is kept in image fusion. Furthermore, Himawari-8, a geostationary satellite having a very high temporal resolution (10 minutes), is used. The present invention uses similar spectral bands as whose used for high-spatial-resolution image in satellites like Landsat-8 and SPOT-6. The present invention combines a high spatial-resolution image with a high temporal-resolution image obtained from Himawari-8. Thus, a TOA-reflectance-based spatial-temporal image fusion method (TOA-STFM) is proposed for generating an image having high spatiotemporal resolution. The present invention can be applied for air quality monitoring.

Abstract: A fusion method is provided to obtain a spatiotemporal image. The present invention is based on a conventional model—a spatial and temporal adaptive reflectance fusion model (STARFM). In the present invention, top-of-atmosphere (TOA) reflectance is kept in image fusion. Furthermore, Himawari-8, a geostationary satellite having a very high temporal resolution (10 minutes), is used. The present invention uses similar spectral bands as whose used for high-spatial-resolution image in satellites like Landsat-8 and SPOT-6. The present invention combines a high spatial-resolution image with a high temporal-resolution image obtained from Himawari-8. Thus, a TOA-reflectance-based spatial-temporal image fusion method (TOA-STFM) is proposed for generating an image having high spatiotemporal resolution. The present invention can be applied for air quality monitoring.