Advertisement

Journal of Meteorological Research

, Volume 28, Issue 4, pp 645–655 | Cite as

Applications of an AMSR-E RFI detection and correction algorithm in 1-DVAR over land

  • Ying Wu (吴 莹)Email author
  • Fuzhong Weng (翁富忠)
Articles

Abstract

Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference (RFI) signals emanating from man-made microwave radiation transmitters can result in large errors in land retrievals. RFI in C- and X-band channels can contaminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer (AMSR-E) and the WindSat sensor. In this work, applications of an RFI detection and correction algorithm in retrieving a comprehensive suite of geophysical parameters from AMSR-E measurements using the one-dimensional variational retrieval (1-DVAR) method are described. The results indicate that the values of retrieved parameters, such as land skin temperature (LST), over these areas contaminated by RFI are much higher than those from the global data assimilation system (GDAS) products. The results also indicate that the differences between new retrievals and GDAS products are decreased evidently through taking into account the RFI correction algorithm. In addition, the convergence metric (χ2) of 1-DVAR is found to be a new method for identifying regions where land retrievals are affected by RFI. For example, in those regions with much stronger RFI, such as Europe and Japan, χ 2 of 1-DVAR is so large that convergence cannot be reached and retrieval results may not be reliable or cannot be obtained. Furthermore, χ 2 also decreases with the RFI-corrected algorithm for those regions with moderate or weak RFI. The results of RFI detected by χ 2 are almost consistent with those identified by the spectral difference method.

Key words

microwave remote sensing radio-frequency interference (RFI) AMSR-E 1-DVAR 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams, I. S, M. H. Bettenhausen, P. W. Gaiser, et al., 2010: Identiflcation of ocean-reflected radiofrequency interference using WindSat retrieval chisquare probability. IEEE Geosci. Remote Sens. Lett., 7, 406–410.CrossRefGoogle Scholar
  2. Anterrieu, E., 2011: On the detection and quantification of RFI in L1a signals provided by SMOS. IEEE Trans. Geosci. Remote Sens., 49, 3986–3992.CrossRefGoogle Scholar
  3. Boukabara, S. A., and F. Weng, 2008: Microwave emissivity over ocean in all-weather conditions: Validation using WindSat and airborne GPS-dropsondes. IEEE Trans. Geosci. Remote Sens., 46, 376–384CrossRefGoogle Scholar
  4. —, K. Garrett, W. Chen, et al., 2011: MiRS: An allweather 1DVAR satellite data assimilation and retrieval system. IEEE Trans. Geosci. Remote Sens., 49, 3249–3272.CrossRefGoogle Scholar
  5. Camps, A. A., J. Gourrion, J. M. Tarongi, et al., 2010: RIF Ranalysis in SMOS imagery. 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Honolulu, HI, IEEE, 2007–2010, doi: 10.1109/IGARSS.2010.5654268.CrossRefGoogle Scholar
  6. Castro, R., A. Gutiérrez, and J. Barbosa, 2012: A first set of techniques to detect radio frequency interferences and mitigate their impact on SMOS data. IEEE Trans. Geosci. Remote Sens., 50, 1440–1447.CrossRefGoogle Scholar
  7. Ding, S., P. Yang, F. Weng, et al., 2011: Validation of the community radiative transfer model. J. Quant. Spectrosc. & Radiative Transfer., 112, 1050–1064.CrossRefGoogle Scholar
  8. Ellingson, S. W., and J. T. Johnson, 2006: A polarimetric survey of radio-frequency interference in Cand X-bands in the continental United States using WindSat radiometry. IEEE Trans. Geosci. Remote Sens., 44, 540–548.CrossRefGoogle Scholar
  9. Eyre, J. R., G. A. Kelly, A. P. NcNally, et al., 1993: Assimilation of TOVS radiance information through one-dimensional variational analysis. Quart. J. Roy. Meteor. Soc., 119, 1427–1463.CrossRefGoogle Scholar
  10. Gasiewski, A. J., M. Klein, A. Yevgrafov, et al., 2002: Interference mitigation in passive microwave radiometry. IEEE International Geoscience and Remote Sensing Symposium. Toronto, Canada, IEEE, 1682–1684.CrossRefGoogle Scholar
  11. Hallikainen, M., J. Kainulainen, J. Seppanen, et al., 2010: Studies of radio frequency interference at L-band using an airborne 2-D interferometric radiometer. 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Honolulu, HI, IEEE, 2490–2491, doi: 10.1109/IGARSS.2010.5651866.CrossRefGoogle Scholar
  12. Han, Y., van P. Delst, Q. Liu, et al., 2006: Community Radiative Transfer Model (CRTM): Version 1, NOAA Technical Report NESDIS 122. NOAA, Washington, DC, 33 pp.Google Scholar
  13. Japan Aerospace Exploration Agency (JAXA) Press, 2012: Launch Result of the Global Changing Observation Mission 1st-Water “SHIZUKU” (GCOM-W1) and the Korean Multi-Purpose Satellite 3 (KOMPSAT-3) by H-IIA Launch Vehicle No. 21. Available online at: http://www.jaxa.jp/press/2012/05/20120518_h2af21_e.html, 2012.Google Scholar
  14. Johnson, J. T., A. J. Gasiewski, B. Guner, et al., 2006: Airborne radio-frequency interference studies at Cband using a digital receiver. IEEE Trans. Geosci. Remote. Sens., 44, 1974–1985.CrossRefGoogle Scholar
  15. Kachi, M., K. Imaoka, H. Fujii, et al., 2008: Status of GCOM-W1/AMSR2 development and science activities Sensors, Systems, and Next-Generation Satellites XII. Proceedings of the SPIE, 7106, 71060P–71060P-8, Meynart, R., S. P. Neeck, H. Shimoda, et al., doi: 10.1117/12.801228.CrossRefGoogle Scholar
  16. Kidd, C., 2006: Radio frequency interference at passive microwaves observation frequencies. Int. J. Remote Sens., 27, 3853–3865.CrossRefGoogle Scholar
  17. Lacava, T., I. Coviello, M. Faruolo, et al., 2013: A multitemporal investigation of AMSR-E Cband radio-frequency interference. IEEE Trans. Geosci. Remote Sens., 51, 2007–2015, doi: 10.1109/TGRS.2012.2228487.CrossRefGoogle Scholar
  18. Le Vine, D. M., G. S. E. Lagerloef, F. R. Colomb, et al., 2007: Aquarius: An instrument to monitor sea surface salinity from space. IEEE Trans. Geosci. Remote Sens., 45, 2040–2050.CrossRefGoogle Scholar
  19. Li, L., E. G. Njoku, E. Im, et al., 2004: A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data. IEEE Trans. Geosci. Remote Sens., 42, 380–390.CrossRefGoogle Scholar
  20. —, P. W. Gaiser, M. H. Bettenhausen, et al., 2006: WindSat radio-frequency interference signature and its identification over land and ocean. IEEE Trans. Geosci. Remote Sens., 44, 530–539.CrossRefGoogle Scholar
  21. Mecklenburg, S., M. Drusch, Y. H. Kerr, et al., 2012: ESA’s soil moisture and ocean salinity mission: Mission performance and operations. IEEE Trans. Geosci. Remote Sens., 50, 1354–1366.CrossRefGoogle Scholar
  22. Misra, S., and C. S. Ruf, 2008: Detection of radiofrequency interference with the aquarius radiometer. IEEE Trans. Geosci. Remote Sens., 46, 3123–3128.CrossRefGoogle Scholar
  23. —, and —, 2012: Analysis of radio frequency interference detection algorithms in the angular domain for SMOS. IEEE Trans. Geosci. Remote Sens., 50, 1448–1457.CrossRefGoogle Scholar
  24. Njoku, E. G., P. Ashcroft, T. K. Chan, et al., 2005: Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans. Geosci. Remote Sens., 43, 938–947.CrossRefGoogle Scholar
  25. Oliva, R., E. Daganzo-Eusebio, Y. H. Kerr, et al., 2012: SMOS radio frequency interference scenario: Status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band. IEEE Trans. Geosci. Remote Sens., 50, 1427–1439.CrossRefGoogle Scholar
  26. Piepmeier, J. R., P. N. Mohammed, and J. J. Knuble, 2008: A double detector for RFI mitigation in microwave radiometers. IEEE Trans. Geosci. Remote Sens., 46, 458–465.CrossRefGoogle Scholar
  27. Ruf, C., S. M. Gross, and S. Misra, 2006: RFI detection and mitigation for microwave radiometry with an agile digital detector. IEEE Trans. Geosci. Remote Sens., 44, 694–706.CrossRefGoogle Scholar
  28. Skou, N., S. Misra, J. E. Balling, et al., 2010: L-band RFI as experienced during airborne campaigns in preparation for SMOS. IEEE Trans. Geosci. Remote Sens., 48, 1398–1407.CrossRefGoogle Scholar
  29. Weng, F., B. Yan, and N. C. Grody, 2001: A microwave land emissivity model. J. Geophys. Res., 106, 20115–20123.CrossRefGoogle Scholar
  30. Wu Ying and Weng Fuzhong, 2011: Detection and correction of AMSR-E radio-frequency interference. Acta Meteor. Sinica, 25, 669–681.CrossRefGoogle Scholar
  31. Yan, B., and F. Weng, 2011: Effects of microwave desert surface emissivity on AMSU-A data assimilation. IEEE Trans Geosci. Remote Sens., 49, 1263–1276.CrossRefGoogle Scholar
  32. Yang, H., and F. Weng, 2011: Error sources in remote sensing of microwave land surface emissivity. IEEE Trans. Geosci. Remote Sens., 49, 3437–3442.CrossRefGoogle Scholar
  33. Zheng, W., J. Meng, H. Wei, et al., 2009: Improvement of satellite data utilization in NCEP operational NWP modeling and data assimilation systems. Proc. 2nd Workshop Remote Sens. Model. Surf. Properties, Toulouse, France, June 9–11, 14 pp.Google Scholar
  34. Zou, X., J. Zhao, F. Weng, et al., 2012: Detection of radio-frequency interference signal over land from FY-3B Microwave Radiation Imager (MWRI). IEEE Trans. Geosci. Remote Sens., 40, 4994–5003.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological AdministrationNanjing University of Information Science & TechnologyNanjingChina
  2. 2.School of Atmospheric PhysicsNanjing University of Information Science & TechnologyNanjingChina
  3. 3.NOAA/NESDIS/Center for Satellite Application and ResearchCollege ParkUSA

Personalised recommendations