Repair wind field in oil contaminated areas with SAR images

Jie Guo; Yijun He; Xiao Long; Chawei Hou; Xin Liu; Junmin Meng

doi:10.1007/s00343-015-3242-7

Your Location：

Home >

Browse articles >

Repair wind field in oil contaminated areas with SAR images

OriginalPaper | Updated：2023-05-16

- Repair wind field in oil contaminated areas with SAR images
- Repair wind field in oil contaminated areas with SAR images
- 海洋湖沼学报（英文） 2015年33卷第2期页码：525-533
- Affiliations：
  
  1. Yantai Institute of Coastal Zone Research Chinese Academy of Sciences
  2. Key Laboratory of Coastal Zone Environmental Processes and Ecological Remediation CAS
  3. School of Marine Sciences Nanjing University of Information Science & Technology
  4. Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Science
  5. Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province College of Atmospheric Sciences Lanzhou University
  6. Yantai Marine Environmental Monitoring Central Station State Oceanic Administration (SOA)
  7. First Institute of Oceanography State Oceanic Administration (SOA)
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China (No. 41176160) and the “135 Program” of Chinese Academy of Sciences (No. Y455011031)
- DOI：10.1007/s00343-015-3242-7
  中图分类号：
- 网络首发：2014-12-03，
  
  纸质出版：2015
- Accepted：
Scan QR Code
Repair wind field in oil contaminated areas with SAR images[J]. 海洋湖沼学报（英文）, 2015,33(2):525-533.

Guo, J., He, Y., Long, X. et al. Repair wind field in oil contaminated areas with SAR images., Chin. J. Ocean. Limnol. 33, 525–533 (2015). https://doi.org/10.1007/s00343-015-3242-7
Repair wind field in oil contaminated areas with SAR images[J]. 海洋湖沼学报（英文）, 2015,33(2):525-533. DOI： 10.1007/s00343-015-3242-7.

Guo, J., He, Y., Long, X. et al. Repair wind field in oil contaminated areas with SAR images., Chin. J. Ocean. Limnol. 33, 525–533 (2015). https://doi.org/10.1007/s00343-015-3242-7 DOI：

摘要

Abstract

In this paper

we compared the normalized radar cross section in the cases of oil spill

biogenic slicks

and clean sea areas with image samples made from 11-pixel NRCS average

and determined their thresholds of the NRCS of the synthetic aperture radar. The results show that the thresholds of oil and biogenic slicks exhibit good consistency with the corresponding synthetic aperture radar images. In addition

we used the normalized radar cross section of clean water from adjacent patches of oil or biogenic slicks areas to replace that of oil or biogenic slicks areas

and retrieve wind field by CMOD5.n and compare wind velocity mending of oil and biogenic slicks areas with Weather Research and Forecasting modeled data

from which the root mean squares of wind speed (wind direction) inversion are 0.89 m/s (20.26°) and 0.88 m/s (7.07°)

respectively. Therefore

after the occurrence of oil spill or biogenic slicks

the real wind field could be repaired using the method we introduced in this paper. We believe that this method could improve the accuracy in assessment of a real wind field on medium and small scales at sea

and enhance effectively the monitoring works on similar oil or biogenic slicks incidents at sea surface.

关键词

Keywords

references

Brekke C, Solberg A H S. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95: 1–13.

Del Frate F, Petrocchi A, Lichtenegger J, Calabresi G. 2000. Neural networks for oil spill detection using ERS SAR data. IEEE Trans. Geosci. Remote Sens., 38(5): 2 282–2 287.

Espedal H A, Johannessen J A. 2000. Detection of oil spills near offshore installations using synthetic aperture radar (SAR). International Journal of Remote Sensing, 11: 2 141–2 144.

Espedal H A, Wahl T. 1999. Satellite SAR oil spill detection using wind history information. International Journal of Remote Sensing, 20: 49–65.

Feng S Z, Li F Q, Li S Q. 2003. An Introduction to Marine Science. Higher Education Press. Beijing. p.242.

Fiscella B, Giancaspro A, Nirchio F, Pavese P, Trivero P. 2000. Oil spill detection using marine SAR images. Int. J. Remote Sens., 21(18): 3 561–3 566.

Gade M Alpers W. 1998. Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR-C/X-SAR. Journal of Geophysical Research, 103(9): 18 851–18 866.

Guo J, Liu X, Xie Q. 2013. Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem. Chinese Science Bulletin, 58(19): 2 276–2 281.

Han J Q, Meng J M, Zhao J S. 2013. Feature extraction and its criticality analysis for oil spill detection in synthetic aperture radar images. Acta Oceanologica Sinica, 35(1): 85–93. (in Chinese with English abstract)

Hersbach H. 2010. Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF. J. Atmos. Ocean. Technol., 27(4): 721–736.

Karathanassi V, Topouzelis K, Pavlakis P, Rokos D. 2006. An object-oriented methodology to detect oil spills. International Journal of Remote Sensing, 27: 5 235–5 251.

Keramitsoglou I, Cartalis C, Kiranoudis C. 2005. Automatic identification of oil spills on satellite images. Environmental Modeling and Software, 21: 640–652.

Keramitsoglou I, Cartalis C, Kiranoudis C. 2006. Automatic identification of oil spills on satellite images. Environ. Model. Softw., 21(5): 640–652.

Migliaccio M, Nunziata F, Gambardella A. 2009. On the copolarized phase difference for oil spill observation. International Journal of Remote Sensing, 30(6): 1 587–1 602.

Quilfen Y, Chapron B, Elfouhaily T, Katsaros K, Tournadre J. 1998. Observation of tropical cyclones by high-resolution scatterometry. J. Geophys. Res., 103(C4): 7 767–7 788

Skamarock W C, Klemp J B. 2008. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227: 3 465–3 485.

Solberg A H S, Storvik G, Solberg R, Volden E. 1999. Automatic detection of oil spills in ERS SAR images. IEEE Trans. Geosci. Remote Sens., 37(4): 1 916–1 924.

Solberg R, Theophilopoulos N A. 1997. ENVISYS—A solution for automatic oil spill detection in the Mediterranean. Proceedings of 4th Thematic Conference on Remote Sensing for Marine and Coastal Environments. p.3–12.

Stoffelen A, Anderson D. 1997. Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. J. Geophys. Res., 102(C3): 5 767–5 780.

Topouzelis K, Karthanassi V, Pavlakis P, Rokos D. 2003. Oil spill detection: SAR multi-scale segmentation & object features evaluation. In Proceedings of SPIE. Remote Sensing of the Ocean and Sea Ice, 4880: 77–87.

Topouzelis K, Stathakis D, Karathanassi V. 2009. Investigation of genetic algorithms contribution to feature selection for oil spill detection. International Journal of Remote Sensing, 30(3): 611–625.

Zhang B, Perrie W, Vachon P W, Li X F, Pichel W G, Guo J, He Y J. 2012. Ocean vector winds retrieval from C-B and fully polarimetric SAR measurements. IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4 252–4 261.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Experimental study on the variation of optical remote sensing imaging characteristics of internal solitary waves with wind speed

Applicability evaluation of ERA5 wind and wave reanalysis data in the South China Sea

Impact of emulsification of crude oil on normalized radar cross section

Rapid detection to long ship wake in synthetic aperture radar satellite imagery

Impact of tropical cyclone Matmo on mixed zone of the Yellow and Bohai Seas