Advertisement

Journal of Zhejiang University-SCIENCE A

, Volume 3, Issue 4, pp 461–466 | Cite as

Rice yield estimation using remote sensing and simulation model

  • Huang Jing-feng
  • Tang Shu-chuan
  • Ousama Abou-Ismail
  • Wang Ren-chao
Biotechnology & Lie Sciences
  • 224 Downloads

Abstract

Remote sensing techniques have the potential to provide information on agricultural crops quantitatively, instantaneously and above all nondestructively over large areas. Crop simulation models describe the relationship between physiological processes in plants and environmental growing conditions. The integration between remote sensing data and crop growth simulation model is an important trend for yield estimation and prediction, since remote sensing can provide information on the actual status of the agricultural crop. In this study, a new model (Rice-SRS) was developed based mainly on ORYZA1 model and modified to accept remote sensing data as input from different sources. The model can accept three kinds of NDVI data: NOAA AVHRR(LAC)-NDVI, NOAA AVHRR(GAC)-NDVI and radiometric measurements-NDVI. The integration between NOAA AVHRR (LAC) data and simulation model as applied to Rice-SRS resulted in accurate estimates for rice yield in the Shaoxing area, reduced the estimating error to 1.027%, 0.794% and (−0.787%) for early, single, and late season respectively. Utilizing NDVI data derived from NOAA AVHRR (GAC) as input in Rice-SRS can yield good estimation for rice yield with the average error (−7.43%). Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.

Key words

Rice Remote sensing NOAA(National Oceanic and Atmospheric Adiministration) AVHRR (Advanced Very High Resolution Radiometer) Simulation model LACC (Local Area coverage) GAC(Global Area Coverage) 

Document code

CLC number

TP7.5 S511 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahlrichs, J. S., Bauer, M. E., 1983. Relation of agronomic and multispectral reflectance characteristics of spring wheat canopies.Agron. J. 75:987–993.CrossRefGoogle Scholar
  2. Barnett, T. L., Thompson, D. R., 1982, The use of large-area spectral data in wheat yield estimation.Remote Sens. Environ,12: 509–518.CrossRefGoogle Scholar
  3. Kropff, M. J., Cassman K. G. van Laar, H. H., 1994. Quantitative Understanding of The irrigated Rice Ecosyetem for Increased Yield Potential.In: Proceedings of the 1992 International Rice Research Conference, IRRI (in press).Google Scholar
  4. Kropff, M. J., Spitters, C. J. T. 1992. An eco-physiological model for interspecific competition, applied to the influence of Chenopodium album L on sugar beet. I. Model description and parameterization.Weed Research,32(6), 437–450.CrossRefGoogle Scholar
  5. Patel, N. K., Singh, T. P., Sahai, B., Patel, M. S., 1985. Spectral response of rice crop and its relation to yield and yield attributes.Int. J. Remote sens. 6:657–664.CrossRefGoogle Scholar
  6. Richardson, A. J., Wiegand, C. L., Arkin, G. F., Nixon, P. R., and Gerbermann, A. H., 1982. Remotely-sensed spectral indicators of sorghum development and their use in growth.Agric. Meteorl. 26:11–23.CrossRefGoogle Scholar
  7. Spitters, C.J.T., Van Keulen, H., van Kraalingen, D. W. G. 1989. A simple and universal crop growth simulator: SUCROS87.In: Simulation and Systems Management in Crop Protection. Eds. R. Rabbing, S. A. Waard & H. H. van Laar. Simulation Monograghs, Pudoc, Wageningen, p. 147–181.Google Scholar
  8. Tucker, C. J., Holben, B. N., Elgin, J. H. Jr., McMurtrey, (1980). Remote sensing of dry matter accumulation in winter wheat.Remote Sens. Environ. 11: 171–189.CrossRefGoogle Scholar
  9. Xiong, Z. M., Cai, H. F., Min, S. K., Li, B. C. Baochu, 1992. Rice in China. Chinese Publishing Publishing Press of Agricultural Science and Technology, Beijing (in Chinese, with English summary).Google Scholar

Copyright information

© Zhejiang University Press 2002

Authors and Affiliations

  • Huang Jing-feng
    • 1
  • Tang Shu-chuan
    • 1
  • Ousama Abou-Ismail
    • 2
  • Wang Ren-chao
    • 1
  1. 1.Institute of Agricultural Remote Sensing & Information ApplicationZhejiang UniversityHangzhouChina
  2. 2.the National Remote Sensing Centre of SyriaSyria

Personalised recommendations