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Estimation of PM2.5 concentration from GF-1 data in Kaifeng City |
| HOU Aihua1, GAO Wei2, WANG Zhongting3, WANG Lin4 |
1. The Faculty of High Vocational Education,Xi’an University of Technology, Xi’an 710048, China;
2. Shaanxi Local Taxation Bureau, Xi’an 710048, China;
3. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China;
4. School of Information Science and Technology, Northwest University, Xi’an 710127, China |
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Abstract PM2.5is the key air pollution for air quality of Kaifeng City. With remote sensing technology, the distribution of PM2.5 concentration could be determined quickly. In this paper, the authors collected the aerosol optical depth (AOD) of GF-1, height of planetary boundary layer (HPBL), relative humidity (RH) and air temperature (AT) over Kaifeng City and then, with multiple regression analysis, revised the coefficients of all variables. After that, the authors built the PM2.5 retrieving model from GF-1 in Kaifeng City. The validation from June to September in 2015 showed that the PM2.5concentration from remote sensing was similar to that from four ground-level monitoring sites, and the correlation coefficient was higher than 0.8. The result of geographically weighted regression (GWR) was obviously better than that of no GWR. Nevertheless, when PM2.5 concentration was high, the model would underestimate PM2.5concentration.
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| Keywords
cloud extraction
feature extraction
support vector machine
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Issue Date: 04 December 2017
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2017,
Vol. 29
