토양 표면에서의 편파별 후방 산란 계수 측정을 통한 산란 모델과 Inversion 알고리즘의 검증

Verification of Surface Scattering Models and Inversion Algorithms with Measurements of Polarimetric Backscattering Coefficients of a Bare Soil Surface

  • 홍진영 (홍익대학교 전파통신공학과) ;
  • 정승건 (홍익대학교 전파통신공학과) ;
  • 오이석 (홍익대학교 전파통신공학과)
  • Hong, Jin-Young (Department of Radio Science and Communication Engineering, Hongik University) ;
  • Jung, Seung-Gun (Department of Radio Science and Communication Engineering, Hongik University) ;
  • Oh, Yi-Sok (Department of Radio Science and Communication Engineering, Hongik University)
  • 발행 : 2006.12.31

초록

본 논문은 풀이 없는 지표면에서의 후방 산란 계수(backscattering coefficients)를 측정하고, 이 측정 결과를 이용하여 여러 표면 산란 모델들과 inversion 알고리즘의 성능을 비교, 분석하였다. 우선, R-밴드 주파수($1.7{\sim}2.0GHz$)에서 완전 편파 scatterometer를 이용하여 풀 층이 없는 지표면에 대해서 편파별로 후방 산란 계수를 측정하고, 동시에 수분 함유량과 표면 거칠기를 측정하였다. 그런 다음 측정된 지표면 변수들을 표면 산란 모델들에 입력하여 후방 산란 계수를 계산하고, 이 계산 결과를 측정 결과와 비교 분석하였다. 또한, inversion 알고리즘들을 적용하여 측정된 편파별 후방 산란 계수로부터 수분 함유량을 추출하고, 이 추출된 수분 함유량이 현장에서 측정한 수분 함유량과 잘 맞는지 여부를 확인하였다. 표면 산란 모델들 중에서 정확도가 높은 모델들을 제시하였으며, inversion 모델들의 계산 결과도 나타내었다.

The backscattering coefficients of a bare soil surface were measured using an R-band polarimetric scatterometer, which were used to verify the validities of scattering models and inversion algorithms. The soil moisture contents and the surface roughness parameters (the RMS height and correlation length) were also measured from the soil surface. The backscattering coefficients were obtained from several scattering models with these surface parameters, and the computation results were compared with the measured backscattering coefficients. The soil moisture contents of the surface were retrieved from the measured backscattering coefficients, and compared with the measured surface parameters. This paper shows how well the scattering models agree with the measurements, and also shows the inversion results.

키워드

참고문헌

  1. D. Entekhabi et al., 'The hydrosphere state(hydros) satellite mission: An earth system pathfinder for global mapping of soil moisture and land freeze/thaw', IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 10, pp. 2184-2195, Oct. 2004 https://doi.org/10.1109/TGRS.2004.834631
  2. F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing, Active and Passive, vol. II, Artech House, Norwood, MA, U.S.A., 1982
  3. A. K. Fung, Microwave Scattering and Emission Models and Their Applications, Artech House, Boston, MA, 1994
  4. A. K. Fung, Aongqian Li, and K. S. Chen, 'Backscattering from a randomly rough dielectric surface', IEEE Trans. Geosci. Remote Sensing, vol. 30, no. 2, Mar. 1992
  5. Y. Oh, K. Sarabandi, and F. T. Ulaby, 'Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces', IEEE Trans. Geosci. Remote Sensing, vol. 40, no. 6, pp. 1348-1355, Jun. 2002 https://doi.org/10.1109/TGRS.2002.800232
  6. P. C. Dubois, J. van Zyl, and T. Engman, 'Measuring soil moisture with imaging radars', IEEE Trans. Geosci. Remote Sensing, vol. 33, no. 4, pp. 915-926, Jul. 1995 https://doi.org/10.1109/36.406677
  7. Y. Oh, 'Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces', IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 3, pp. 596-601, Mar. 2004 https://doi.org/10.1109/TGRS.2003.821065
  8. T. Hallikainen, F. T. Ulaby, M. C. Dobson, M. A. El-Rayes, and L. Wu, 'Microwave dielectric behavior of wet soil. Part-I: Empirical models and experimental observation', IEEE Trans. Geosci. Remote Sensing, vol. 23, pp. 25-34, Jan. 1985 https://doi.org/10.1109/TGRS.1985.289497
  9. F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing Active and Passive, vol. III, Artech House, 1986
  10. Jin-Young Hong, Yisok Oh, 'Examination of the semi-empirical polarimetric scattering model using field-measured data and existing theoretical model', models IEEE IGARSS2005, vol. 3, pp. 2211-2214, Jul. 2005