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Use of spectral data in Markov chain model for crop yield forecasting

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Abstract

Crop yield is mainly dependent on weather, soil and technological inputs. Yield forecasting models have been developed mainly using multiple regression techniques based on biometrical characters of the plants and/or weather parameters. Matiset al. (1985) proposed another approach of crop yield modelling using Markov Chain theory based on biometrical characters. The integration of remote sensing with other technologies has provided an immense scope to improve upon the existing crop yield models. In the present study, multi date spectral data during crop growth period was used in Markov Chain Model to forecast wheat yield. The results indicate that the use of spectral data near the maximum vegetative growth of wheat crop improves the efficiency and reliability of yield forecast about a month before its actual harvest.

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Authors and Affiliations

  1. Indian Agricultural Statistics Research Institute, Library Aveneu, 110012, New Delhi

    Randhir Singh & A Ei Ibrahim

Authors
  1. Randhir Singh
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  2. A Ei Ibrahim
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Singh, R., Ibrahim, A.E. Use of spectral data in Markov chain model for crop yield forecasting. J Indian Soc Remote Sens 24, 145–152 (1996). https://doi.org/10.1007/BF03007327

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  • DOI: https://doi.org/10.1007/BF03007327

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