The Journal of Korean Institute of Electromagnetic Engineering and Science (한국전자파학회논문지)

The Korean Institute of Electromagnetic Engineering and Science (한국전자파학회)
권호 표지
DOI QR코드

DOI QR Code

Effect of Vegetation Layers on Soil Moisture Measurement Using Radars

레이다를 이용한 토양 수분함유량 측정에서 초목 층의 영향 분석

  • Park, Sinmyong (Department of Electronic Information and Communication Engineering, Hongik University) ;
  • Oh, Yisok (Department of Electronic Information and Communication Engineering, Hongik University)
  • 박신명 (홍익대학교 전자정보통신공학과) ;
  • 오이석 (홍익대학교 전자정보통신공학과)
  • Received : 2015.09.23
  • Accepted : 2016.07.08
  • Published : 2016.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents the effect of vegetation layer and radar parameters on soil moisture measurement using the vegetation layer scattering model and surface scattering model. The database of backscattering coefficients for various vegetation layer densities, incidence angles, frequencies, and polarizations is generated using $1^{st}$-order RTM(Radiative Transfer Model). Then, surface soil moisture contents were estimated from the backscattering coefficients in the database using the WCM(Water Cloud Model) and Oh model. The retrieved soil moisture contents were compared with the soil moisture contents in the input parameters of the RTM to estimate the retrieval errors. The effects of vegetation layer and radar parameters on soil moisture measurement are analyzed using the retrieval errors.

본 논문에서는 초목 층 산란모델과 지표면 산란 모델을 이용하여 초목 층에서 수분함유량 측정에 초목 층과 레이다 파라미터가 갖는 영향에 대하여 분석하였다. $1^{st}$-order RTM(Radiative Transfer Model)을 이용하여 여러 상태의 초목 층 밀도와 입사각, 주파수, 편파를 갖는 데이터베이스를 생성하고, WCM(Water Cloud Model)과 Oh 모델을 이용하여 후방산란계수로부터 지표면 수분함유량을 추출하였다. 수분함유량 추출 에러를 예측하기 위해 추출한 수분함유량과 RTM의 입력 변수인 수분함유량을 비교하였다. 수분함유량 추출 에러로부터 초목 층에서의 수분함유량 측정에서 초목 층 밀도와 입사각, 주파수, 편파에 따른 초목 층과 레이다 파라미터의 영향을 분석하였다.

Keywords

References

  1. H. McNairn et al., "The soil moisture active passive validation experiment 2012(SMAPVEX12): Prelaunch calibration and validation of the SMAP soil moisture algorithms", IEEE Trans. Geosci. Remote Sens,, vol. 53, no. 5, pp. 2784-2801, May 2015. https://doi.org/10.1109/TGRS.2014.2364913
  2. F. T. Ulaby, P. C. Dubois, and J. Van Zyl, "Radar mapping of surface soil moisture", Journal of Hydrology, vol. 184, no. 1, pp. 57-84, Oct. 1996. https://doi.org/10.1016/0022-1694(95)02968-0
  3. S. -K. Kweon, Y. Oh, "A modified water-cloud model with lear angle parameters for microwave backscattering from agricultural fields", IEEE Trans. Geosci. Remote Sens,, vol. 53, no. 5, pp. 2802-2809, May 2015. https://doi.org/10.1109/TGRS.2014.2364914
  4. Y. Oh, K. Sarabandi, and F. T. Ulaby, "Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backsattering from bare soil surfaces", IEEE Trans. Geosci. Remote Sens,, vol. 40, no. 6, pp. 1348-1355, Jun. 2002. https://doi.org/10.1109/TGRS.2002.800232
  5. Y. Oh, S. -Y. Hong, Y. Kim, J. -Y. Hong, and Y. -H. Kim, ""Polarimetric backscattering coefficients of flooded rice fields at L- and C-bands: Measurements, modeling, and data analysis", IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2714-2721, Aug. 2009. https://doi.org/10.1109/TGRS.2009.2014053
  6. S. -K. Kweon, Y. Oh, "Estimation of soil moisture and surface roughness from single-polarized radar data for bare soil surface and comparison with dual- and quadpolarization cases", IEEE Trans. Geosci. Remote Sens,, vol. 52, no. 7, pp. 4056-4064, Jul. 2014. https://doi.org/10.1109/TGRS.2013.2279183