Every year a large amount of aerosols released from wildfires and dust storms into the atmosphere may have potential impacts on the climate, environment, and air quality. Detecting smoke and dust aerosols and monitoring their movements and evolutions in a timely manner is a very meaningful task. Satellite remote sensing has been demonstrated as an effective means for the observation of these two kinds of aerosols. In this dissertation, an algorithm based on the multi-spectral technique for detecting smoke and dust aerosols is developed, by combining measurements of MODoderate resolution Imaging Spectroradiometer (MODIS) reflective solar bands and thermal emissive bands
Data from smoke/dust events occurred during last several years are collected visually as training data for spectral and statistical analyses. According to the spectral curves of various scene types (aerosols, cloud, vegetation, and water et al.), a series of spectral bands is selected jointly or separately and corresponding thresholds are defined for scene classification step by step. The multi-spectral algorithm is applied mainly to detect smoke plumes in the United States and dust storms in Asia. The detection results are validated not only visually with MODIS true color images, but also quantitatively with products of, Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The validations show that this multi-spectral detection algorithm is suitable to monitor smoke and dust in the selected study areas. The accuracy is quite good in most cases. Additionally, this algorithm can be used to detect smoke and dust aerosols at the areas near clouds even mixed with clouds
Detection of dust aerosol with multi-sensor satellite remote sensing measurements, MODIS and CALIPSO, is also performed tentatively in this dissertation. After spatial registration, the dust layers are identified combining CALIPSO Vertical Feature Mask product and measurements of MODIS brightness temperature difference between 12 and 11-im bands. Based on detecting results, the three-dimension information of dust aerosols is summarized
Additionally, the impacts of the mis-registration on the L1B data and dust aerosol detection results are assessed. The relative errors caused by mis-registration on L1B data are generally less than a few tenths of a percent. The impacts on dust detection results are relative large, usually has the trend as negligible at the homogeneous and semi-homogeneous areas, but large at the non-homogeneous areas
