Korean Journal of Soil Science and Fertilizer (한국토양비료학회지)

Korean Society of Soil Science and Fertilizer (한국토양비료학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Wheat Growth using a Microwave Scatterometer

마이크로파 산란계를 이용한 밀 생육 추정

  • Kim, Yihyun (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration) ;
  • Hong, Sukyoung (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration) ;
  • Lee, Kyungdo (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration) ;
  • Jang, Soyeong (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration)
  • 김이현 (농촌진흥청 국립농업과학원 토양비료과) ;
  • 홍석영 (농촌진흥청 국립농업과학원 토양비료과) ;
  • 이경도 (농촌진흥청 국립농업과학원 토양비료과) ;
  • 장소영 (농촌진흥청 국립농업과학원 토양비료과)
  • Received : 2013.01.21
  • Accepted : 2013.02.06
  • Published : 2013.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Microwave remote sensing can help monitor the land surface water cycle and crop growth. This type of remote sensing has great potential over conventional remote sensing using the visible and infrared regions due to its all-weather day-and-night imaging capabilities. In this paper, a ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was developed. This system was used to monitor the wheat over an entire growth cycle. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. Backscattering coefficients for the crop growing season were compared with biophysical measurements. Backscattering coefficients for all frequencies and polarizations increased until dat of year 137 and then decreased along with fresh weight, dry weight, plant height, and vegetation water content (VWC). The range of backscatter for X-band was lower than for L- and C-band. We examined the relationship between the backscattering coefficients of each band (frequency/polarization) and the various wheat growth parameters. The correlation between the different vegetation parameters and backscatter decreased with increasing frequency. L-band HH-polarization (L-HH) is best suited for the monitoring of fresh weight (r=0.98), dry weight (r=0.96), VWC (r=0.98), and plant height (r=0.96). The correlation coefficients were highest for L-band observations and lowest for X-band. Also, HH-polarization had the highest correlations among the polarization channels (HH, VV and HV). Based on the correlation analysis between backscattering coefficients in each band and wheat growth parameters, we developed prediction equations using the L-HH based on the observed relationships between L-HH and fresh weight, dry weight, VWC and plant height. The results of these analyses will be useful in determining the optimum microwave frequency and polarizations necessary for estimating vegetation parameters in the wheat.

L, C, X-밴드 마이크로파 산란계 자동측정시스템을 이용하여 밀 생육시기에 따른 밴드 및 편파별 후방산란계수와 생육인자 변화를 측정하였다. 모든 안테나 밴드에서 밀 생육 초기에는 VV-편파가 HH, HV-편파보다 후방산란계수가 높게 나타났다. HH-편파가 VV-편파보다 후방산란계수가 높게 나타나는 시기는 밴드에 따라 차이를 보였다. L-밴드의 경우 3월 28일 (DOY 88), C, X-밴드는 4월 2일 (DOY 93)부터 HH-편파가 다른 편파들 보다 후방산란계수가 높게 나타났다. 모든 안테나에서 편파별 후방산란계수가 5월 16일 (DOY 137)에 최대값을 보였고 그 이후 수확기 (DOY 174, 6월 22일)까지 감소하였는데 초장, 생체중, 건물중, 엽면적지수 등 밀 생육인자들에서도 동일한 경향이 나타났다. 밴드별 후방산란계수와 밀 생육인자들과의 상관관계를 분석한 결과 L-밴드 HH-편파에서 생체중 (r=0.98), 건물중 (r=0.96), 식생 수분함량 (r=0.98) 초장 (r=0.96) 등 모든 밀 생육인자들과 상관계수가 가장 높게 나타났다. L-밴드 HH-편파 후방산란계수를 이용하여 밀 생육인자를 추정한 결과 생체중 ($R^2$=0.98), 건물중 ($R^2$=0.95), 식생 수분함량($R^2$=0.98) 초장 ($R^2$=0.95)의 결정계수가 각각 높게 나타났다. L-밴드 HH-편파 후방산란계수를 이용하는 것이 밀 생육을 가장 높게 예측할 수 있었음을 확인하였다.

Keywords

References

  1. Attema, E.P. and F.T. Ulaby. 1978. Vegetation modeled as a water cloud. Radio Science, 13:357-364. https://doi.org/10.1029/RS013i002p00357
  2. Bouvet, A. and T. Le Toan. 2011. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sens. Environ. 115(4):1090-1101. https://doi.org/10.1016/j.rse.2010.12.014
  3. Brisco, B., R.J. Brown, J.A. Koehler, G.J. Sofko, and M.J. Mckibben. 1990. The diurnal pattern of microwave backscattering by wheat. Remote Sens. Environ. 34:37-47. https://doi.org/10.1016/0034-4257(90)90082-W
  4. Chen, C. and H. Mcnairn. 2006. A neural network integrated approach for rice crop monitoring. Int. J. Remote Sens. 27: 1367-1393. https://doi.org/10.1080/01431160500421507
  5. Cuizhen, W., W. Jiaping, Z. Yuan, P. Guangdong, and Q. Jiaguo. 2009. Characterizing L-band scattering of paddy rice in southeast china with radiative transfer model and multi-temporal ALOS/PALSAR imagery. IEEE Trans. Geosci. Remote Sens. 47(4):988-998. https://doi.org/10.1109/TGRS.2008.2008309
  6. Inoue, Y., T. Kurosu, H. Maeno, S. Uratsuka, T. Kowu, K. Dabrowska-Zielinska, and J. Qi. 2002. Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables. Remote Sens. Environ. 81:194-204. https://doi.org/10.1016/S0034-4257(01)00343-1
  7. Hong, S.Y., J.Y. Hong, Y.H. Kim, and Y.S. Oh. 2007. Measurement of backscattering coefficients of rice canopy using a ground polarimetric scatterometer system. Korean J. Remote Sens. 23(2):145-152. https://doi.org/10.7780/kjrs.2007.23.2.145
  8. Kim, S.B., B.W. Kim, Y.K. Kong, and Y.S. Kim. 2000. Radar backscattering measurements of rice crop using X-band scatterometer. IEEE Trans. Geosci. Remote Sens. 38(3): 1467-1471. https://doi.org/10.1109/36.843044
  9. Kim, Y.H., S.Y. Hong, and H.Y. Lee. 2009. Estimation of paddy rice growth parameters using L, C, X-bands polarimetric scatterometer. Korean J. Remote Sens. 25:31-44. https://doi.org/10.7780/kjrs.2009.25.1.31
  10. Kim, Y.H., S.Y. Hong, and H.Y. Lee. 2010. Construction of X-band automatic radar scatterometer measurement system and monitoring of rice growth. Korean Soc. Soil Sci. Fertilizer. 43:374-383.
  11. Kim, Y.H., S.Y. Hong, H.Y. Lee, and J.E. Lee. 2011. Monitoring soybean growth using L, C, and X-bands automatic radar scatterometer measurement. Korean J. Remote Sens. 27(2): 191-201. https://doi.org/10.7780/kjrs.2011.27.2.191
  12. Kim, Y.H., S.Y. Hong, H.Y. Lee, J.E. Lee, and K.D. Lee. 2012. Estimation of rice and soybean growth stage using a microwave scatterometer. Korean Soc. Soil Sci. Fertilizer. 45:503-510. https://doi.org/10.7745/KJSSF.2012.45.4.503
  13. Kurosu, T., M. Fujita, and K. Chiba. 1995. Monitoring of rice crop growth from space using the ERS-1 C-band SAR. IEEE Trans. Geosci. Remote Sens. 33(4): 1092-1096. https://doi.org/10.1109/36.406698
  14. Le Toan, T., H. Laur, E. Mougin, and A. Lopes. 1989. Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images. IEEE Trans. Geosci. Remote Sens. 27(6):709-718. https://doi.org/10.1109/TGRS.1989.1398243
  15. Macelloni, G., S. Paloscia, P. Pampaloni, F. Mariliani, and M. Gai. 2001. The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops. IEEE Trans. Geosci. Remote Sens. 39:873-884. https://doi.org/10.1109/36.917914
  16. National Academy of Agricultural Science, 2010. Methods of soil and crop plant analysis. National Academy of Agricultural Science, RDA.
  17. Singh, D. 2006. Scatterometer performance with polarization discrimination ratio approach to retrieve crop soybean parameter at X-band. Int. J. Remote Sens. 27(19):4101-4115. https://doi.org/10.1080/01431160600735988
  18. Ulaby, F.T. and C. Elachi. 1990. Radar Polarimetry for Geoscience Applications. Artech House Inc., Norwood, MA, USA.
  19. Wigneron, J.P., P. Ferrazzoli, A. Olioso, P. Bertuzzi, and A. Chanzy. 1999. A simple approach to monitor crop biomass from C-band radar data. Remote Sens. Environ. 69(2):179-188. https://doi.org/10.1016/S0034-4257(99)00011-5
  20. Xiao, X., S. Bole., S. Frolking, C. Li, J.Y. Babu, and W. Salas. 2005. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 100:95-113.

Cited by

  1. Estimation of Corn Growth by Radar Scatterometer Data vol.47, pp.2, 2014, https://doi.org/10.7745/KJSSF.2014.47.2.085