| Author: | Ding, Ning |
| Title: | Dust aerosol optical thickness retrieval based on neural network using geostationary satellite images (Himawari 8) |
| Advisors: | Wong, Man-sing Charles (LSGI) |
| Degree: | M.Sc. |
| Year: | 2017 |
| Subject: | Dust storms -- Remote sensing Aerosols -- Remote sensing Atmosphere -- Remote sensing Artificial satellites Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Construction and Environment Department of Land Surveying and Geo-Informatics |
| Pages: | x, 60 pages : color illustrations |
| Language: | English |
| Abstract: | Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the major uncertainties in global climatic modeling as it is known to have a significant impact on the radiation budget and atmospheric stability. This dissertation develops an integrated model for dust storm detection and retrieval based on the geostationary satellite, Himaeari-8. The proposed model consists of two components: (i) using the threshold of Brightness Temperature Difference (BTD), Three-band Volcanic Ash Product (TVAP), the Infrared Difference Dust Index (IDDI), Reverse Absorption Technique (RAT) index to obtain dust storm pixels; (ii) a feed-forward Multilayer Perceptron (MLP) neural network, trained using Back Propagation (BP) algorithm, was used for dust Aerosol Optical Depth (AOT) retrieval. Multi-angle Imaging SpectroRadiometer (MISR) level 2 Aerosol parameters-MIL2ASAE product and Advanced Himawari Imager (AHI) was used in MLP neural network training. Four Himawari-8 channels (band 8, band 13, band 15), as well as pre-processed (BTD, TVAP, and IDDI) from Himawari-8 satellite data are adopted in the MLP-BP neural model. The correlation and root mean squared error (RMSE) of trained MLP-BP model are 98.4% and 0.0283 respectively. In order to assess the performance of the model, daytime AOT at 550 nm derived from MISR aerosol product were compared with AOT retrieved from MLP-BP NN model at 05:00 (UTC) March 5, 2016. The RMSE is 0.4807 and the correlation coefficient is 0.9561. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022385850103411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.5 MB | Adobe PDF | View/Open |
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