Statistical Assessment Of Terra Modis Aerosol Optical Depth (C051) Over Coastal Regions
| dc.contributor.author | Nikou Langeroodi, Sahabeh Safarpour | |
| dc.date.accessioned | 2017-01-19T08:08:03Z | |
| dc.date.available | 2017-01-19T08:08:03Z | |
| dc.date.issued | 2016-04 | |
| dc.description.abstract | Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been widely used to address environment and climate change issues with daily global coverage. Aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. MODIS-Terra and Global Aerosol Robotic Network (AERONET) products can be obtained from the Multi-sensor Aerosol Products Sampling System (MAPSS) for coastal regions during 2000-2010. Using data collected from 83 coastal and 158 non-coastal stations worldwide from AERONET from 2000-2010, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard the Terra satellite. The main aim of this statistical assessment of AOD over coastal regions is to produce modified MODIS AOD with minimum error when compared with the reference value given by AERONET. At first we evaluate the accuracy of MODIS AOD data under different algorithm, season and geographical region over the coastal regions and non-coastal regions using information from the AERONET network. After removing retrievals with quality flags below1 for Ocean algorithm and below 3 for Land algorithm, the accuracy of AOD retrieved from MODIS Dark Target Ocean algorithms is greater than the MODIS Dark Target Land algorithms and the Deep Blue algorithm. The reasons of the retrieval error in AOD are found to be the various underlying surface reflectance. In the next step, we developed a general linear model (GLM) that can explain the influence of geographical region and season on the association between MODIS and AERONET. We found that the GLM model performed better work on the noncoastal regions. After that we developed multiple linear regression models (MLR) using MODIS AOD product (MODIS AOD, Cloud Fraction and Mean reflectance) that can effectively produce modified MODIS AOD of high relationship with AERONET for different season and geographical region over coastal regions. It has showed that seasonal multiple linear regression is better than general and the spring multiple linear regression is the best model for seasonal model. When the MLR models are synchronizing in each region, a model with highest correlations can be considered as the best for AOD retrieval in the region. Artificial intelligent techniques are successfully used in modeling of highly complex and non-linear phenomena. In this study, we developed artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) for prediction of AOD by using MODIS aerosol products over the coastal regions. Finally, a comparison between developed ANN, ANFIS and MLR was made and the outcomes disclosed that cascade neural network model can predict aerosol optical depth retrieval better than does ANFIS and MLR model. We selected two different geographical regions in different location for evaluation of ANN, ANFIS and MLR models in different season. The ANN provides better correlation compared to ANIFS and MLR both generally and seasonally. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/3509 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Aerosol optical depth | en_US |
| dc.title | Statistical Assessment Of Terra Modis Aerosol Optical Depth (C051) Over Coastal Regions | en_US |
| dc.type | Thesis | en_US |
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