Algorithm for PM2.5 Mapping over Penang Island, Malaysia, Using SPOT Satellite Data

Air pollution is an important issue being monitored and regulated in industrial and developing cities. In this study, we explored the relationship between particulate matters of size less than 2.5 micron (PM 2.5) derived from the SPOT using regression technique. The aim of this study was to evaluate the high spatial resolution satellite data for air quality mapping by using FLAASH software. The corresponding PM 2.5 data were measured simultaneously with the acquired satellite scene and their locations were determined using a handheld Global Positioning System (GPS). Due to the fact that the current commercial aerosol retrieval method (FLAASH) was designed to work reasonably well over land, but is not accurate for water applications, adjustments had to be made to the software to optimize it for reflectance retrieval over water. In-situ reflectance spectra will be plot against spectra derived from the atmospherically corrected images. In this study, we ensure that all the atmospherically corrected spectra match reasonably with the in-situ spectra. We have developed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from remotely sensed imagery under general atmospheric and surface conditions. The algorithm was developed base on the aerosol characteristics in the atmosphere. The efficiency of the developed algorithm, in comparison to other forms of algorithm, will be investigated in this study. Results indicate that there is a good correlation between the satellites derived PM 2.5 and the measured PM 2.5. This study shows the potential of using the thermal infrared data for air quality mapping. The finding obtained by this study indicates that the FLAASH can be used to retrieve air quality information for remotely sensed data.