سابِق|تال

Detection and analysis of television frequency interference from an FY-3C microwave radiation imager

  • role： First author第一作者
  • Affiliation：

    Joint Center for Data Assimilation Research and Applications, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • Email：zehuazhou@foxmail.com
  • Introduction：周泽华(1992—　)，男，硕士研究生，研究方向为卫星资料质量控制。E-mail：zehuazhou@foxmail.com
ZHOU Zehua,  
  • role： Corresponding author通信作者
  • Affiliation：

    Joint Center for Data Assimilation Research and Applications, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • Email：xzou@nuist.edu.cn
  • Introduction：邹晓蕾(1960—　)，女，教授，研究方向为资料同化。E-mail：xzou@nuist.edu.cn
ZOU Xiaolei*,  
  • Affiliation：

    Joint Center for Data Assimilation Research and Applications, Nanjing University of Information Science and Technology, Nanjing 210044, China

QIN Zhengkun

ملخص

Observations of a satellite Microwave Radiation Imager (MWRI) are easily interfered by signals of active remote sensing because of their similar frequency. The primary source of interference over oceans is the reflected signal of geostationary television (TV) satellites by the ocean surface. The accurate detection of such interferences is largely important for the effective use of MWRI observations and an essential preprocessing step of the MWRI data onboard the FY-3C satellite. A Normalized Principal Component Analysis Method (NPCA) is used to detect the TV Frequency Interference (TFI) signals over oceans. High correlations exist among the different MWRI channels, but the RFI signal eliminates these correlations. Hence, NPCA can detect the TFI signal by employing this aspect. The TFI signals of the MWRI at 10.65 GHz horizontal polarization over oceans are distributed widely near the coastal areas of Europe, especially the English Channel and western parts of the Mediterranean Sea. The TFI signal definitively originates from the hot bird, and its intensity is related to the angle between the MWRI incidence and geostationary satellite TV signals reflection. The threshold of the TFI signals is defined to quantify the TFI intensity. The TFI signals at 18.7 GHz are observed over the offshore marine areas of North America. As expected, no RFI signal is detected near the coastal areas of China because the geostationary satellite TV frequency over China is different from those of MWRI channels. The NPCA method can detect TFI signals over oceans. The distribution of TFI signals are in Europe at 10.65 GHz and North America at 18.7 GHz. The intensity of the TFI signals is completely related to the Glint angle. TFI signals evidently affect the result of the retrieval production. Correcting and eliminating TFI signals will be pursued in future studies. Clear brightness temperature data will also be adopted in retrieval production and data assimilation.

مفهوم

FY-3C;Microwave Radiation Imager (MWRI);ocean;television frequency interference;detection

References

  1. 1.
    Adams I S, Bettenhausen M H, Gaiser P W and Johnston W. 2010. Identification of ocean-reflected radio-frequency interference using WindSat retrieval chi-square probability. IEEEGeoscience and Remote Sensing Letters, 7(2): 406–410
  2. 2.
    Dong C H, Yang J, Yang Z D, Lu N M, Shi J M, Zhang P, Liu Y J, Cai B and Zhang W J. 2009. An overview of a new Chinese weather satellite FY-3A. Bulletin of the American Meteorological Society, 90(10): 1531–1544
  3. 3.
    Feng C C and Zhao H. 2015. Identification of radio-frequency interference signal from FY-3B microwave radiation imager over ocean. Journal of Remote Sensing, 19(3): 465–475
  4. 4.
    Gaiser P W, St.Germain KM, Twarog EM, Poe GA, Purdy W, Richardson D, Grossman W, Jones WL, Spencer D, Golba G, Cleveland J, Choy L, Bevilacqua RM and Chang PS 2004. The WindSatspacebornePolarimetric microwave radiometer: sensor description and early orbit performance. IEEE Transactions on Geoscience and Remote Sensing, 42(11): 2347–2361
  5. 5.
    Guan L, Xia S C and Zhang S B. 2015. Identifying the interference of spaceborne microwave radiometer over large water area. Journal of Applied Meteorological Science, 26(1): 22–31
  6. 6.
    Hollinger J P, Peirce J L and Poe G A. 1990. SSM/I instrument evaluation. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 781–790
  7. 7.
    Hu P X. 2011. Evaluation and Retrival of FY-3B Micro Wave Radiation Imager Brightness Temperature. Qingdao: Ocean University of China
  8. 8.
    Kawanishi T, Sezai T, Ito Y, ImaokaK, Takeshima T, Ishido Y, Shibata A, Miura M, Inahata H and Spencer RW. 2003. The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 184–194
  9. 9.
    Kummerow C, Barnes W, KozuT, ShiueJ andSimpson J. 1998. The tropical rainfall measuring mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15(3): 809–817
  10. 10.
    Li L, Njoku E G, Im E, Chang P S and St. Germain K. 2004. A preliminary survey of radio-frequency interference over the U.S. in aqua AMSR-E data.IEEE Transactions on Geoscience and Remote Sensing, 42(2): 380–390 [DOI: 10.1109/TGRS. 2003.817195]
  11. 11.
    Li L, Gaiser P W, Bettenhausen M H and Johnston W. 2006. WindSat radio-frequency interference signature and its identification over land and ocean. IEEE Transactions onGeoscience and Remote Sensing, 44(3): 530–539
  12. 12.
    Njoku E G, Ashcroft P,Chan TK and Li L. 2005. Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Transactions on Geoscience and Remote Sensing, 43(5): 938–947
  13. 13.
    Oki T, Imaoka KandKachi M. 2010. AMSR instruments on GCOM-W1/2: concepts and applications//Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI:IEEE: 1363–1366 [DOI: 10.1109/IGARSS.2010.5650001]
  14. 14.
    Wang Z Z. 2003. A Model for inversing sea surface wind speeds by using SSM/I 19.35 GHZ vertical and horizontal brightness temperatures. Ocean Technology, 22(2): 1–6
  15. 15.
    Zhao J, Zou X L and Weng F Z. 2013. WindSat radio-frequency interference signature and its identification over greenland and antarctic. IEEE Transactions on Geoscience and Remote Sensing, 51(9): 4830–4839
  16. 16.
    Zou X L. 2012. Serial of applications of satellite observations Introduction to microwave imager radiance observations from polar-orbiting meteorological satellites. Advances in Meteorological Science and Technology, 2(3): 45–50
  17. 17.
    Zou X L, Zhao J, Weng F Z and Qin Z K. 2012. Detection of radio-frequency interference signal over land from FY-3B Microwave Radiation Imager (MWRI). IEEE Transactions onGeoscience and Remote Sensing, 50(12): 4994–5003
  18. 18.
    Zou X L, Tian X X and Weng F Z. 2014. Detection of television frequency interference with satellite microwave imager observations over oceans. Journal of Atmospheric and Oceanic Technology, 31(12): 2759–2776

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website