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L, C, X-밴드 레이더 산란계 자동측정시스템을 이용한 콩 생육 모니터링

Monitoring soybean growth using L, C, and X-bands automatic radar scatterometer measurement system

  • 김이현 (농촌진흥청 국립농업과학원 토양비료관리과) ;
  • 홍석영 (농촌진흥청 국립농업과학원 토양비료관리과) ;
  • 이훈열 (강원대학교 자연과학대학 지구물리학과) ;
  • 이재은 (농촌진흥청 국립식량과학원 전작과)
  • Kim, Yi-Hyun (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration) ;
  • Hong, Suk-Young (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration) ;
  • Lee, Hoon-Yol (Department of Geophysics, Kangwon National University) ;
  • Lee, Jae-Eun (Soil Upland Crop Research Division, National Institute of Crop Science, Rural Development Administration)
  • 투고 : 2011.04.05
  • 심사 : 2011.04.25
  • 발행 : 2011.04.30

초록

본 연구에서는 다편파 레이더 산란계 자동 측정시스템 이용하여 콩 생육변화를 관측하고 레이더 시스템에서 얻어진 후방산란계수과 콩 생육인자들과의 관계분석을 통하여 콩 생육추정 가능성을 모색하고자 하였다. 2010년도 농촌진흥청 국립식량과학원 연구지역에 다편파 레이더 산란계 관측시스템 (L, C, X-밴드 안테나, 네트워크분석기, RF switch, 입사각 $40^{\circ}$)을 구축하고 콩 파종시기에서 수확기까지 10분단위로 콩 생육변화를 자동 측정하였다. 모든 안테나 밴드, 편파에서 콩 생육초기 (6월초~7월 중순)에는 VV-편파가 HH-, HV-편파보다 후방산란계수가 높게 나타났고, 그 이후 HH-편파와 다른 편파들 간의 cross-over 현상이 일어났는데 그 시기는 L-밴드가 7월 20일 (DOY 200), C-, X-밴드의 경우에는 7월 30일 (DOY 210)로 밴드에 따라 차이를 보였다. 모든 밴드 및 편파에서 9월 29일 (DOY 271)까지 후방산란계수가 증가하다가 그 이후 감소하였고 특히 종실비대기 (DOY 277, R6) 이후 감소폭이 크게 나타났는데 이 현상은 콩 생육인자 (초장, 엽면적지수, 건물중 등)변화와 일치하였다. 밴드에 따른 후방산란계수와 콩 생육인자들과의 관계를 분석한 결과 L-밴드 HH-편파에서 LAI ($r=0.93^{***}$), 초장 ($r=0.95^{***}$), 건물중 ($r=0.94^{***}$), 꼬투리중 ($r=0.92^{***}$)등 콩 생육인자들과의 상관계수가 가장 높게 나타났고 이에 비해 X-밴드 편파에서는 콩 생육인자들과의 상관계수가 상대적으로 낮게 나타났다. 후방산란계수 (L-밴드 HH-편파)를 이용하여 콩 생육인자 추정을 위한 회귀식을 작성하였다.

Soybean has widely grown for its edible bean which has numerous uses. Microwave remote sensing has a great potential over the conventional remote sensing with the visible and infrared spectra due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the crop conditions of a soybean field. Polarimetric backscatter data at L, C, and X-bands were acquired every 10 minutes on the microwave observations at various soybean stages. The polarimetric scatterometer consists of a vector network analyzer, a microwave switch, radio frequency cables, power unit and a personal computer. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. The backscattering coefficients were calculated from the measured data at incidence angle $40^{\circ}$ and full polarization (HH, VV, HV, VH) by applying the radar equation. The soybean growth data such as leaf area index (LAI), plant height, fresh and dry weight, vegetation water content and pod weight were measured periodically throughout the growth season. We measured the temporal variations of backscattering coefficients of the soybean crop at L, C, and X-bands during a soybean growth period. In the three bands, VV-polarized backscattering coefficients were higher than HH-polarized backscattering coefficients until mid-June, and thereafter HH-polarized backscattering coefficients were higher than VV-, HV-polarized back scattering coefficients. However, the cross-over stage (HH > VV) was different for each frequency: DOY 200 for L-band and DOY 210 for both C and X-bands. The temporal trend of the backscattering coefficients for all bands agreed with the soybean growth data such as LAI, dry weight and plant height; i.e., increased until about DOY 271 and decreased afterward. We plotted the relationship between the backscattering coefficients with three bands and soybean growth parameters. The growth parameters were highly correlated with HH-polarization at L-band (over r=0.92).

키워드

참고문헌

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피인용 문헌

  1. Estimation of Soybean Growth Using Polarimetric Discrimination Ratio by Radar Scatterometer vol.44, pp.5, 2011, https://doi.org/10.7745/KJSSF.2011.44.5.878
  2. Estimation of Soil Moisture Content from Backscattering Coefficients Using a Radar Scatterometer vol.45, pp.2, 2012, https://doi.org/10.7745/KJSSF.2012.45.2.127
  3. Estimation of Rice and Soybean Growth Stage Using a Microwave Scatterometer vol.45, pp.4, 2012, https://doi.org/10.7745/KJSSF.2012.45.4.503
  4. Monitoring Wheat Growth by COSMO-SkyMed SAR Images vol.29, pp.1, 2013, https://doi.org/10.7780/kjrs.2013.29.1.4
  5. Estimation of Wheat Growth using a Microwave Scatterometer vol.46, pp.1, 2013, https://doi.org/10.7745/KJSSF.2013.46.1.023
  6. Estimation of Corn Growth by Radar Scatterometer Data vol.47, pp.2, 2014, https://doi.org/10.7745/KJSSF.2014.47.2.085
  7. Estimation of Soil Moisture Content in Corn Field Using Microwave Scatterometer Data vol.47, pp.4, 2014, https://doi.org/10.7745/KJSSF.2014.47.4.235