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2011 | 30 | 1 |

Tytuł artykułu

Winter oilseed-rape yield estimates from hyperspectral radiometer measurements

Wydawca

-

Rocznik

Tom

30

Numer

1

Opis fizyczny

p.77-84,fig.,ref.

Twórcy

  • Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Dziegielowa 27, 61-680 Poznan, Poland
autor
autor

Bibliografia

  • Beck P.S.A., Jonsson P., Hogda 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, D0I:10.1016/j.eja.2006.06.010.
  • Casa R. & Jones H.G., 2005: LAI retrieval from multiangu- lar image classification and inversion of a ray tracing model. Remote Sensing of Environment, 98: 414 - 428, DOI: 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: 14471453.
  • 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.
  • Clevers J.G.P.W., De Jong S.M., Epema G.F., Van Der Meer F.D., Barker W.H., Skidmore A.K., & Schölte 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, D0I: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, 10.1111/j.1442-8903.2006.00286.x.
  • Gitelson A.A., Vina A., Rundquist D.C., Ciganda V., & Arke- bauer T.J., 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: L08403 D0I: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, D0I: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 IG- ARSS 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 vid- eography. 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, D0I:10.1016/j.eja.2006.12.001.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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