Korean Journal of Soil Science and Fertilizer (한국토양비료학회지)

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

DOI QR Code

Estimating Leaf Area Index of Paddy Rice from RapidEye Imagery to Assess Evapotranspiration in Korean Paddy Fields

  • Na, Sang-Il (Soil and Fertilizer Management Division, NAAS, RDA) ;
  • Hong, Suk Young (Soil and Fertilizer Management Division, NAAS, RDA) ;
  • Kim, Yi-Hyun (Soil and Fertilizer Management Division, NAAS, RDA) ;
  • Lee, Kyoung-Do (Soil and Fertilizer Management Division, NAAS, RDA) ;
  • Jang, So-Young (Soil and Fertilizer Management Division, NAAS, RDA)
  • Received : 2013.08.07
  • Accepted : 2013.08.16
  • Published : 2013.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

Leaf area index (LAI) is important in explaining the ability of crops to intercept solar energy for biomass production, amount of plant transpiration, and in understanding the impact of crop management practices on crop growth. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of RapidEye imagery obtained from 2010 to 2012 using empirical models in a rice plain in Seosan, Chungcheongnam-do. Rice plants were sampled every two weeks to investigate LAI, fresh and dry biomass from late May to early October. RapidEye images were taken from June to September every year and corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). Linear, exponential, and expolinear models were developed to relate temporal satellite NDVIs to measured LAI. The expolinear model provided more accurate results to predict LAI than linear or exponential models based on root mean square error. The LAI distribution was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when RapidEye imagery was applied to expolinear model. The spatial trend of LAI corresponded with the variation in the vegetation growth condition.

Keywords

References

  1. Anderson, M. C., C. M. U. Neale, F. Li, J. M. Norman, W. P. Kustas, H. Jayanthi, and J. Chavez. 2004. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sensing of Environment, 92: 447-464. https://doi.org/10.1016/j.rse.2004.03.019
  2. Gebhardt, S., J. Huth, L. D. Nguyen, A. Roth, and C. Kuenzer. 2012. A comparison of Terra SAR‐X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta, Vietnam. International Journal of Remote Sensing, 33(24): 7644-7661. https://doi.org/10.1080/01431161.2012.702233
  3. Goudriaan, J. and J.L. Monteith. 1990. A mathematical function for crop growth based on light interception and leaf area expansion. Annals of Botany, 66: 695-701 https://doi.org/10.1093/oxfordjournals.aob.a088084
  4. Gower, S., C. Kucharik, and J. Norman. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70: 29-51. https://doi.org/10.1016/S0034-4257(99)00056-5
  5. Hatfield, J. L., C. D. Stanley, and R. E. Carlson. 1976. Evaluation of an electronic foliometer to measure leaf area in corn and soybean. Agronomy Journal, 68: 434-436. https://doi.org/10.2134/agronj1976.00021962006800020063x
  6. Huete, A., and H. Liu. 1994. An error and sensitivity analysis of the atmospheric‐ and soil‐ correcting variants of the NDVI for the MODIS‐EOS. IEEE Transactions on Geoscience and Remote Sensing, 32: 897-905. https://doi.org/10.1109/36.298018
  7. Justice, C. O. et al. 1985. Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6: 1271-1318. https://doi.org/10.1080/01431168508948281
  8. Kim, S. H., S. Y. Hong, A. S. Kenneth, Y. H. Kim, and K. D. Lee. 2012. Comparing LAI estimates of corn and soybean from vegetation indices of multi‐resolution satellite images. Korean Journal of Remote Sensing, 28(6): 597-609. https://doi.org/10.7780/kjrs.2012.28.6.1
  9. Korea Meteorological Administration Homepage, http://www. kma.go.kr/. Accessed 17 June 2012.
  10. Lee, J. H., J. Goudriaan, and H. Challa. 2003. Using the expolinear growth equation for modelling crop growth in yearround cut chrysanthemum. Annals of Botany, 92: 697-708. https://doi.org/10.1093/aob/mcg195
  11. Lee, K. S., S. H. Kim, J. H. Park, T. G. Kim, Y. I. Park, and C. S. Woo. 2006. Estimation of forest LAI in close canopy situation using optical remote sensing data. Korean Journal of Remote Sensing, 22(5): 305-311. https://doi.org/10.7780/kjrs.2006.22.5.305
  12. Leprieur, C., Y. H. Kerr, S. Mastorchio, and J. C. Meunier. 2000. Monitoring vegetation cover across semi‐arid regions : comparison of remote observations from various scales. International Journal of Remote Sensing, 21: 281-300. https://doi.org/10.1080/014311600210830
  13. Na, S. I., and J. H. Park. 2008. Regional scale evapotranspiration mapping using Landsat 7 ETM+ land surface temperature and NDVI Space. Journal of the Korean Society of Agricultural Engineers, 50(3): 115-123. https://doi.org/10.5389/KSAE.2008.50.3.115
  14. Na, S. I., J. H. Park, K. H. Lee, S. J. Kim, and J. W. Lee. 2010. Estimating leaf area index for vegetation indices and its application in paddy. International Symposium on Remote Sensing 2010. Jeju, Korea, October 26-29, 2010
  15. Park, J. H., and S. I. Na. 2007. MODIS and Landsat TM data image fusion based on improved resolution method: Assessing the quality of resulting NDVI images. Proceeding of SPIE The International Society for Optical Engineering 6742, art. No. 67420S.
  16. Price, J. C. 1992. Estimating vegetation amount from visible and near infrared reflectances. Remote Sensing of Environment, 41: 29-34. https://doi.org/10.1016/0034-4257(92)90058-R
  17. Price, J. C., and W. C. Bausch. 1995. Leaf area index estimation from visible and near‐infrared reflectance data, Remote Sensing Environ, 52: 55-65. https://doi.org/10.1016/0034-4257(94)00111-Y
  18. Qi, J., Y. H. Kerr, M. S. Moran, A. R. Huete, S. Sorooshian, and R. Bryant. 2007. Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. Remote Sensing Environ, 73: 18-30.
  19. Qin, J., S. Liang, X. Li. 2008. Development of the adjoint model of a canopy radiative transfer model for sensitivity study and inversion of leaf area index. IEEE Trans Geosci Remote Sens, 46: 2028‐2037. https://doi.org/10.1109/TGRS.2008.916637
  20. Richter, R. 1996. Atmospheric correction of DAIS hyperspectral image data. Computers & Geosciences, 22: 785-793. https://doi.org/10.1016/0098-3004(96)00016-7
  21. Shin, S. C., M. H. Hwang, I. H. Ko, and S. J. Lee. 2006. Suggestion of simple method to estimate evapotranspiration using vegetation and temperature information. Journal of Korea Water Resources Association, 39(4): 363-372. https://doi.org/10.3741/JKWRA.2006.39.4.363
  22. Stenberg, P., M. Rautiainen, T. Manninen, P. Voipio, and H. Smolander. 2004. Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica, 38(1): 3-14.
  23. Tyc, G., J. Tulip, D. Schulten, M. Krischke, and M. Oxfort. 2005. The RapidEye mission design. Acta Astronautica, 56: 213219.
  24. Wang, Q., S. Adiku, J. Tenhunen, and A. Granier. 2005. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment, 94: 244-255. https://doi.org/10.1016/j.rse.2004.10.006
  25. Watson, D. J. 1947. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and with and between years. Annual. Botany, (N.S.), 11: 41-76. https://doi.org/10.1093/oxfordjournals.aob.a083148

Cited by

  1. Evaluation of the Applicability of Rice Growth Monitoring on Seosan and Pyongyang Region using RADARSAT-2 SAR -By Comparing RapidEye- vol.56, pp.5, 2014, https://doi.org/10.5389/KSAE.2014.56.5.055