Winter Oilseed-Rape Yield Estimates from Hyperspectral Radiometer Measurements
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Keywords

Remote Sensing
ground hyperspectral measurements
winter oilseed rape
yield

How to Cite

Piekarczyk, J., Sulewska, H., & Szymańska, G. (2011). Winter Oilseed-Rape Yield Estimates from Hyperspectral Radiometer Measurements. Quaestiones Geographicae, 30(1), 77–84. https://doi.org/10.2478/v10117-011-0007-z

Abstract

Spectral reflectance data can be used for estimation of plant biophysical parameters such as seed yield, related to the use of solar energy. A field experiment was conducted to investigate relationships between canopy reflectance and seed yield of winter oilseed rape sown on four different dates. Ground hyperspectral reflectance measurements were made using a hand-held radiometer and multispectral images were taken with a VIS-NIR camera. The different sowing dates generated a wide range of difference in crop spectral response and seed yields. The strongest relationships (R2=0.87) between the yield and spectral data recorded by both sensors occurred at early flowering stages. Later, the presence of flowers caused a decline in the relationship between yield and spectral data especially in the visible (VIS) range. In the full flowering stage the strongest correlation (R2=0.72) with the yield showed vegetation indices of the near-infrared (NIR) bands.

https://doi.org/10.2478/v10117-011-0007-z
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References

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.

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: https://www.doi.org/10.1016/j.eja.2006.06.010.

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: https://www.doi.org/10.1016/j.rse.2005.08.005

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.

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: https://www.doi.org/10.2134/agronj2005.0204.

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.

Dąbrowska-Zielińska K., Ciołkosz A., Budzyńska M., Kowalik W., 2008. Monitorowanie wzrostu i plonowania zbóż metodami teledetekcji. Problemy Inżynierii Rolniczej, 4: 45-54.

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.

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.

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: https://www.doi.org/10.1016/j.rse.2008.12.010.

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, DOI: https://www.doi.org/10.1111/j.1442-8903.2006.00286.x.

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: https://www.doi.org/10.1029/2005GL022688.

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.

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.

Huete A. R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295-309.

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.

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.

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.

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.

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.

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.

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: https://www.doi.org/10.1016/j.jag.2005.06.002.

Price J. C., 1990. On the information content of soil reflectance spectra. Remote Sensing of Environment, 33: 113-121.

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.

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.

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.

Stauss R., 1994. Compendium of growth stage identification keys for mono- and dicotyledonous plants. Extended BBCH scale. Ciba-Geigy AG, Postfach, Basel.

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.

Thomas J. R. & Oerther G. F., 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64: 11-13.

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.

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.

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.

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.

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: https://www.doi.org/10.1016/j.eja.2006.12.001.