Have a personal or library account? Click to login
Winter Oilseed-Rape Yield Estimates from Hyperspectral Radiometer Measurements Cover

Winter Oilseed-Rape Yield Estimates from Hyperspectral Radiometer Measurements

Quaestiones Geographicae
Volume 30 (2011): Issue 1 (March 2011)
By: Jan Piekarczyk,  Hanna Sulewska and  Grażyna Szymańska  
Open Access
|Mar 2011

References

  1. Beck P. S. A., Jonsson P., Høgda K.-A., Karlsen S. R., Eklundh L. & Skidmore A. K., 2007. A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula. International Journal of Remote Sensing, 28(19): 4311-4330.10.1080/01431160701241936
    Search in Google Scholar
  2. Behrens T., Muller J. & Diepenbrock W., 2006. Utilization of canopy reflectance to predict properties of oilseed rape (Brassica napus L.) and barley (Hordeum vulgare L.) during ontogenesis. European Journal of Agronomy, 25: 345-355, DOI:10.1016/j.eja.2006.06.010.10.1016/j.eja.2006.06.010
    Search in Google Scholar
  3. Casa R. & Jones H. G., 2005: LAI retrieval from multiangular image classification and inversion of a ray tracing model. Remote Sensing of Environment, 98: 414-428, DOI: 10.1016/j.rse.2005.08.00510.1016/j.rse.2005.08.005
    Search in Google Scholar
  4. Chang J., Clay D. A., Dalsted K., Clay S. & O'Neill M., 2003. Corn (Zea mays L.) Yield Prediction Using Multispectral and Multidate Reflectance. Agronomy Journal, 95: 1447-1453.10.2134/agronj2003.1447
    Search in Google Scholar
  5. Clay D. A., Kim K., Chang J., Clay S. A. & Dalsted K., 2006. Characterizing Water and Nitrogen Stress in Corn Using Remote Sensing. Agronomy Journal, 98: 579-587, DOI: 10.2134/agronj2005.0204.10.2134/agronj2005.0204
    Search in Google Scholar
  6. Clevers J. G. P. W., De Jong S. M., Epema G. F., Van Der Meer F. D., Bakker W. H., Skidmore A. K., & Scholte K. H., 2002. Derivation of the red edge index using the MERIS standard band setting. International Journal of Remote Sensing, 23(16): 3169-3184.10.1080/01431160110104647
    Search in Google Scholar
  7. Dąbrowska-Zielińska K., Ciołkosz A., Budzyńska M., Kowalik W., 2008. Monitorowanie wzrostu i plonowania zbóż metodami teledetekcji. Problemy Inżynierii Rolniczej, 4: 45-54.
    Search in Google Scholar
  8. Doraiswamy P. C., Hatfield J. L., Jacksona T. J., Akhmedova B., Prueger J. & Sterna A., 2004. Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environmen, 92: 548-559.10.1016/j.rse.2004.05.017
    Search in Google Scholar
  9. Fathi G., Siadat S. A. & Hemaiaty S. S., 2003. Effect of sowing date on yield and yield components of three oilseed rape varieties. Acta Agronomica Hungarica, 51(3): 249-255.10.1556/AAgr.51.2003.3.2
    Search in Google Scholar
  10. Galvão L. S., Roberts D. A., Formaggio A. R., Numata I. & Breunig F. M., 2009. View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data. Remote Sensing of Environment, 113: 846-856, DOI:10.1016/j.rse.2008.12.010.10.1016/j.rse.2008.12.010
    Search in Google Scholar
  11. Gibbons P. & Freudenberger D., 2006. An overview of methods used to assess vegetation condition at the scale of the site. Ecological Management & Restoration, 7(S1): 10-17, 10.1111/j.1442-8903.2006.00286.x.10.1111/j.1442-8903.2006.00286.x
    Search in Google Scholar
  12. Gitelson A. A., Vina A., Rundquist D. C., Ciganda V., & Arkebauer T. J., 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: L08403 DOI:10.1029/2005GL022688.10.1029/2005GL022688
    Search in Google Scholar
  13. Gröll K., Graeff S. & Claupein W., 2007. Use of Vegetation indices to detect plant diseases. Agrarinformatik im Spannungsfeld zwischen Regionalisierung und globalen Wertschöpfungsketten, Referate der 27. GIL Jahrestagung, 5.-7. März 2007, Stuttgart, Germany.
    Search in Google Scholar
  14. Haboudane D., Miller J. R., Tremblay N., Zarco-Tejada P. J., Dextraze L., 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sensing of Environment, 81: 416-426.10.1016/S0034-4257(02)00018-4
    Search in Google Scholar
  15. Huete A. R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295-309.10.1016/0034-4257(88)90106-X
    Search in Google Scholar
  16. Hunt, E. R., & Rock, B. N., 1989. Detection of changes in leaf water content using near and middle-infrared reflectances. Remote Sensing of Environment, 30: 43-54.10.1016/0034-4257(89)90046-1
    Search in Google Scholar
  17. Ji-Hua M. & Bing-Fang W., 2008. Study on the crop condition monitoring methods with remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B8): 945-948.
    Search in Google Scholar
  18. Li A., Liang S., Wang A. & Qin J., 2007. Estimating Crop Yield from Multi-temporal Satellite Data Using Multivariate Regression and Neural Network Techniques. Photogrammetric Engineering & Remote Sensing, 73(10): 1149-1157.10.14358/PERS.73.10.1149
    Search in Google Scholar
  19. Malthus T. J., Andrieu B., Danson F. M., Jaggard K. W. & Steven M. D., 1993. Candidate high spectral resolution infrared indices for crop cover. Remote Sensing of Environment, 46: 204-212.10.1016/0034-4257(93)90095-F
    Search in Google Scholar
  20. Nguyen H. T. & Byun-Woo L., 2006. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy, 24: 349-356.10.1016/j.eja.2006.01.001
    Search in Google Scholar
  21. Osborne S. L., Schepers J. S., Francis D. D. & Schlemmer M. R., 2002. Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agronomy Journal, 94: 1215-1221.10.2134/agronj2002.1215
    Search in Google Scholar
  22. Prasad A. K., Chai L., Singh R. P. & Kafatos M., 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8: 26-33, DOI:10.1016/j.jag.2005.06.002.10.1016/j.jag.2005.06.002
    Search in Google Scholar
  23. Price J. C., 1990. On the information content of soil reflectance spectra. Remote Sensing of Environment, 33: 113-121.10.1016/0034-4257(90)90037-M
    Search in Google Scholar
  24. Robinson B. F. & Biehl L. L., 1979. Calibdue to ration procedures for measurements of reflectance factor in remote sensing field research. Proceedings SPIE, 196: 16-26.10.1117/12.957952
    Search in Google Scholar
  25. Rouse J. W. Jr., Haas R. H., Schell J. A., Deering D. W., 1973. Monitoring vegetation systems in the Great Plains with ERTS, In: Proceedings of the Earth Research Technical Satellite-1 Symposium. Goddard Space Flight Center, Washington, DC, pp. 309-317.
    Search in Google Scholar
  26. Serrano L., Fillela J. & Penuelas J., 2000. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, 40: 723-731.10.2135/cropsci2000.403723x
    Search in Google Scholar
  27. Stauss R., 1994. Compendium of growth stage identification keys for mono- and dicotyledonous plants. Extended BBCH scale. Ciba-Geigy AG, Postfach, Basel.
    Search in Google Scholar
  28. Thenkabail P. S., Smith R. B. & De-Pauw E., 2002. Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering, 68(6): 607-621.
    Search in Google Scholar
  29. Thomas J. R. & Oerther G. F., 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64: 11-13.10.2134/agronj1972.00021962006400010004x
    Search in Google Scholar
  30. Ustin S. L., Roberts D. A., Gardner M., & Dennison P., 2002. Evaluation of the potential of Hyperion data to estimate wildfire hazard in the Santa Ynez Front Range, Santa Barbara, California. Proceedings of the 2002 IEEE IGARSS and 24th Canadian Symposium on Remote Sensing, Toronto, Canada, 24-28 June 2002 (Piscataway, NJ: IEEE), pp. 796-798.
    Search in Google Scholar
  31. Yang, C. & Anderson, G. L., 1996. Determining within-field management zones for grain sorghum using aerial videography. 26th Int. Symp on Remote SVIH. Environ. 2529 March. Vancouver, BC, Canada, pp. 606-611.
    Search in Google Scholar
  32. Zhao C-J., Zhou Q., Wang J. & Huang W-J., 2004. Band selection for analysing wheat water status under field conditions using relative depth indices (RDI). International Journal of Remote Sensing, 25(13): 2575-2584.10.1080/01431160310001618419
    Search in Google Scholar
  33. Zhao D., Reddya K. R., Kakani V. G. & Reddy V. R., 2005. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 22: 391-403.10.1016/j.eja.2004.06.005
    Search in Google Scholar
  34. Zhao D., Reddy K. R., Kakani V. G., Read J. J. & Koti S., 2007. Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26:335-344, DOI:10.1016/j.eja.2006.12.001.10.1016/j.eja.2006.12.001
    Search in Google Scholar
DOI: https://doi.org/10.2478/v10117-011-0007-z | Journal eISSN: 2081-6383 | Journal ISSN: 2082-2103
Journal RSS Feed
Language: English
Page range: 77 - 84
Published on: Mar 22, 2011
Published by: Adam Mickiewicz University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Remote Sensing,
ground hyperspectral measurements,
winter oilseed rape,
yield
Related subjects:
Geosciences,
Geography

© 2011 Jan Piekarczyk, Hanna Sulewska, Grażyna Szymańska, published by Adam Mickiewicz University
This work is licensed under the Creative Commons License.

Volume 30 (2011): Issue 1 (March 2011)