Research on Atmospheric Visibility Grading based on Remote Sensing Data

Research on Atmospheric Visibility Grading based on Remote Sensing Data

Loading document ...
Page
of
Loading page ...

Author(s)

Author(s): Siyu Wang, Xiuguo Zou, Xinfa Qiu

Download Full PDF Read Complete Article

DOI: 10.18483/ijSci.2286 21 83 44-48 Volume 9 - Mar 2020

Abstract

Theoretical research on atmospheric radiative transfer shows that aerosol optical depth (AOD) is positively correlated with atmospheric particulate matter (PM) concentration. Using satellite remote sensing data to retrieve the AOD, and monitor and analyze the atmospheric visibility and atmospheric pollution are gradually being widely applied. In this research, the data of moderate resolution imaging spectroradiometer (MODIS) is used for analysis. Firstly, geometric correction, data quality improvement, image stitching, vector cropping, and masking are performed to process the data. Then, the cloud detection tree algorithm is used to detect cloud, thereby eliminating the cloud interference. Finally, the classic dense dark vegetation (DDV) algorithm is used for the retrieval of AOD, and the distribution characteristics of the obtained AOD values are graded according to the retrieval results. This paper uses remote sensing data to grade the visibility of the atmosphere, which provides a reference for the prediction and assessment of the overall atmospheric environment.

Keywords

Atmospheric Visibility Grading, Remote Sensing Data, MODIS

References

  1. Alam, K., Khan, R., Ali, S., Ajmal, M., Khan, G., Muhammad, W., & Ali, M. A. (2014). Variability of aerosol optical depth over Swat in Northern Pakistan based on satellite data. Arabian Journal of Geosciences, 8(1), 547-555. doi: 10.1007/s12517-013-1237-2
  2. Chauhan, A., de Azevedo, S. C., & Singh, R. P. (2018). Pronounced changes in air quality, atmospheric and meteorological parameters, and strong mixing of smoke associated with a dust event over Bakersfield, California. Environmental Earth Sciences, 77(4). doi: 10.1007/s12665-018-7311-z
  3. Cui, F., Chen, M., Ma, Y., Zheng, J., Yao, L., & Zhou, Y. (2016). Optical properties and chemical apportionment of summertime PM2.5 in the suburb of Nanjing. Journal of Atmospheric Chemistry, 73(2), 119-135. doi: 10.1007/s10874-015-9313-5
  4. Fatima, H., George, J. P., Rajagopal, E. N., & Basu, S. (2017). Seasonal Verification of Dust Forecast over the Indian Region. Pure and Applied Geophysics, 174(11), 4225-4240. doi: 10.1007/s00024-017-1629-4
  5. Gillingham, S. S., Flood, N., Gill, T. K., & Mitchell, R. M. (2012). Limitations of the dense dark vegetation method for aerosol retrieval under Australian conditions. Remote Sensing Letters, 3(1), 67-76. doi: 10.1080/01431161.2010.533298
  6. Grgurić, S., Križan, J., Gašparac, G., Antonić, O., Špirić, Z., Mamouri, R. E., . . . Hadjimitsis, D. (2014). Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia. Central European Journal of Geosciences, 6(1), 2-16. doi: 10.2478/s13533-012-0135-6
  7. Huang, H.-J., Liu, H.-N., Jiang, W.-M., Huang, S.-H., & Zhang, Y.-Y. (2006). Physical and Chemical Characteristics and Source Apportionment of PM2. 5 in Nanjing. Climatic and Environmental Research, 11(6), 713-722.
  8. Huang, H., Huang, B., Yi, L., Liu, C., Tu, J., Wen, G., & Mao, W. (2019). Evaluation of the Global and Regional Assimilation and Prediction System for Predicting Sea Fog over the South China Sea. Advances in Atmospheric Sciences, 36(6), 623-642. doi: 10.1007/s00376-019-8184-0
  9. Kessner, A. L., Wang, J., Levy, R. C., & Colarco, P. R. (2013). Remote sensing of surface visibility from space: A look at the United States East Coast. Atmospheric Environment, 81, 136-147. doi: 10.1016/j.atmosenv.2013.08.050
  10. Kutty, S. G., Agnihotri, G., Dimri, A. P., & Gultepe, I. (2018). Fog Occurrence and Associated Meteorological Factors Over Kempegowda International Airport, India. Pure and Applied Geophysics, 176(5), 2179-2190. doi: 10.1007/s00024-018-1882-1
  11. Peng, N., Yi, W., & Fang, Y. (2008). Retrieval of aerosol optical depth based on 400-1 000 nm dense dark vegetation algorithm. Infrared and Laser Engineering, 37(5), 878-883.
  12. Prabhu, V., Shridhar, V., & Choudhary, A. (2019). Investigation of the source, morphology, and trace elements associated with atmospheric PM10 and human health risks due to inhalation of carcinogenic elements at Dehradun, an Indo-Himalayan city. SN Applied Sciences, 1(5). doi: 10.1007/s42452-019-0460-1
  13. Ramachandran, S., Rajesh, T. A., & Kedia, S. (2018). Influence of Relative Humidity, Mixed-Layer Height, and Mesoscale Vertical-Velocity Variations on Column and Surface Aerosol Characteristics Over an Urban Region. Boundary-Layer Meteorology, 170(1), 161-181. doi: 10.1007/s10546-018-0384-0
  14. Song, Y., Tang, X., Zhang, Y., Hu, M., Fang, C., Zen, L., & Wang, W. (2002). Effects on Fine Particles by the Continued High Temperature Weather in Beijing. Environmental Science, 23(4), 33-36. doi: DOI :10.13227/j.hjkx.2002.04.007
  15. Tan, F., Lim, H. S., Abdullah, K., & Holben, B. (2015). Estimation of aerosol optical depth at different wavelengths by multiple regression method. Environmental Science and Pollution Research, 23(3), 2735-2748. doi: 10.1007/s11356-015-5506-3
  16. Tariq, S., & ul-Haq, Z. (2019). Investigating the Aerosol Optical Depth and Angstrom Exponent and Their Relationships with Meteorological Parameters Over Lahore in Pakistan. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. doi: 10.1007/s40010-018-0575-6
  17. Wallace, L., Lucieer, A., & Watson, C. S. (2014). Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7619-7628. doi: 10.1109/TGRS.2014.2315649
  18. Zhao, S., Wang, Q., Li, Y., Liu, S., Wang, Z., Zhu, L., & Wang, Z. (2017). An overview of satellite remote sensing technology used in China’s environmental protection. Earth Science Informatics, 10(2), 137-148. doi: 10.1007/s12145-017-0286-6

Cite this Article:

  • BibTex
  • RIS
  • APA
  • Harvard
  • IEEE
  • MLA
  • Vancouver
  • Chicago

International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

Search Articles

Issue September 2020

Volume 9, September 2020


Table of Contents



World-wide Delivery is FREE

Share this Issue with Friends:


Submit your Paper