Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Relating Hyperspectral Image Bands and Vegetation Indices to Corn and Soybean Yield

  • Jang Gab-Sue (Chungnam Development Institute) ;
  • Sudduth Kenneth A. (USDA-ARS Cropping Systems and Water Quality Research Unit) ;
  • Hong Suk-Young (National Institute of Agricultural Science and Technology) ;
  • Kitchen Newell R. (USDA-ARS Cropping Systems and Water Quality Research Unit) ;
  • Palm Harlan L. (Department of Agronomy, University of Missouri)
  • Published : 2006.06.01
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Abstract

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

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References

  1. Birrell, S. J., K. A. Sudduth, and S. C. Borgelt, 1996. Comparison of sensors and techniques for crop yield mapping. Computers and Electronics in Agriculture, 14: 215-233 https://doi.org/10.1016/0168-1699(95)00049-6
  2. Birth, G. S. and G. McVey, 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60: 640-643 https://doi.org/10.2134/agronj1968.00021962006000060016x
  3. Deguise, J. C., M. McGovern, and K. Staenz, 1998. Spatial high resolution crop measurements with airborne hyperspectral remote sensing. In Proc. 4th International Conference on Precision Agriculture, edited by P. C. Robert, R. H. Rust, and W. E. Larson. ASA/CSSA/SSSA, Madison, WI, USA, pp. 1603-1608
  4. Drummond, S. T. and K. A. Sudduth, 2005. Analysis of errors affecting yield map accuracy. In: Proc. 7th International Conference on Precision Agriculture, edited by D. J. Mulla. Precision Agriculture Center, University of Minnesota, St. Paul, MN, USA. CDROM
  5. GopalaPillai, S. and L. F. Tian, 1999. In-field variability detection and spatial yield modeling for corn using digital aerial imaging. Transactions of the ASAE, 42(6): 1911-1920 https://doi.org/10.13031/2013.13356
  6. Hong, S. Y., K. A. Sudduth, N. R. Kitchen, H. L. Palm, and W. J. Wiebold, 2001. Using hyperspectral remote sensing data to quantify within-field spatial variability. In: Proc. Third Intl. Conf. on Geospatial Information in Agriculture and Forestry. Veridian, Ann Arbor, MI. CDROM
  7. Huete, A. R., 1988. A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25: 295-309 https://doi.org/10.1016/0034-4257(88)90106-X
  8. Jensen, J. R., 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Prentice Hall, Inc., Upper Saddle River, NJ, USA, pp. 33-35, 364-365
  9. Kitchen, N. R., K. A. Sudduth, D. B. Myers, R. E. Massey, E. J. Sadler, R. N. Lerch, J. W. Hummel, and H. L. Palm, 2005. Development of a conservation-oriented precision agriculture system: Crop production assessment and plan implementation. Journal of Soil and Water Conservation, 60(6): 421-429
  10. Lerch, R. N., N. R. Kitchen, R. J. Kremer, W. W. Donald, E. E. Alberts, E. J. Sadler, K. A. Sudduth, D. B. Myers, and F. Ghidey, 2005. Development of a conservation-oriented precision agriculture system: Water and soil quality assessment. Journal of Soil and Water Conservation, 60(6): 411-420
  11. Lillesand, T. M., R. W. Kiefer, and J. W. Chipman, 2004. Remote Sensing and Image Interpretation. John Wiley & Sons, Inc., Danvers, MA, USA, pp. 330-393
  12. Lorenzen, B. and A. Jensen, 1989. Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing of Environment, 27: 201-209 https://doi.org/10.1016/0034-4257(89)90018-7
  13. Mao, C., 1999. Hyperspectral imaging systems with digital CCD cameras for both airborne and laboratory application. In: Proc. 17th Biennial Workshop on Color Photography and Videography in Resource Assessment. American Society for Photogrammetry and Remote Sensing, Bethesda, MD, USA, pp. 31-40
  14. Neter, J., W. Wasserman, and M. H. Kutner, 1985. Applied Linear Statistical Models. Richard D. Irwin, Inc., Homewood, IL, USA, pp. 391-393
  15. Robinson, T. P. and G. Metternicht, 2005. Comparing the performance of techniques to improve the quality of yield maps. Agricultural Systems, 85: 19-41 https://doi.org/10.1016/j.agsy.2004.07.010
  16. Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering, 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Proc. 3rd Earth Resources Technology Satellite-1 Symposium. SP-351, NASA, Greenbelt, MD, USA, pp. 310-317
  17. SAS Institute, 1999. SAS/STAT user's guide, version 8. SAS Institute. Inc., Cary, NC, USA, pp. 27-49
  18. Thenkabail, P. S., R. B. Smith, and E. D. Pauw, 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71: 158-182 https://doi.org/10.1016/S0034-4257(99)00067-X
  19. Willis, P. R., P. G. Carter, and C. J. Johansen, 1998. Assessing yield parameters by remote sensing techniques. In: Proc. 4th International Conference on Precision Agriculture, edited by P. C. Robert, R. H. Rust and W. E. Larson. ASA/CSSA/SSSA, Madison, WI, USA, pp. 1413-1422
  20. Yang, C. and J. H. Everitt, 2002. Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum. Precision Agriculture, 3: 373-388 https://doi.org/10.1023/A:1021544906167
  21. Yang, C., J. H. Everitt, and J. M. Bradford, 2004. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE, 47(3): 915-924 https://doi.org/10.13031/2013.16111