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Biswal, A.; Sahay, B.; Ramana, K. V.; Rao, S. V. C. K.; Sesha Sai, M. V. R. (2014)
Languages: English
Types: Article
Subjects:
Satellite remote sensing can provide information on plant status for large regions with high temporal resolution and proved as a potential tool for decision support. It allows accounting for spatial and temporal variations of state and driving variables, influencing crop growth and development, without extensive ground surveys. The crop phenological development and condition can be monitored through multi-temporal reflectance profiles or multi-temporal vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). At the same time, Process based dynamic crop growth simulation models are useful tools for estimating crop growth condition and yield on large spatial domains if their parameters and initial conditions are known for each point. Therefore, combined approaches integrating remote sensing and dynamic crop growth models for regional yield prediction have been developed in several studies. In these models the vegetation state variables, e.g., development phase, dry mass, LAI are linked to driving variables, e.g., weather condition, nutrient availability and management practices. Output of these models is usually final yield or accumulated biomass. The model outputs are a summary containing an overview of the main development events, water and nitrogen variables, yield and yield components. In the present work, IRS P6 AWiFS derived vegetation indices like NDVI and NDWI are computed to study the growth profile of wheat crop in Sirsa district of Haryana along with crop growth simulation model DSSAT-CERES from 2008–09 to 2012–13.several iteration of wheat crop simulation are carried out with four sowing dates and four soil types varying with respect to the fertility parameters to represent the average simulation environment of Sirsa district in Haryana state of India. Four years time series NDVI and NDWI are used to establish the correlation between the spectral vegetation indices and simulated wheat yield attributes at critical growth stages of wheat. This work is a basic investigation towards assimilation of remote sensing derived state variables in to the crop growth model.
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    • Maas, S. J. 1988a. Use of remotely-sensed information in agricultural crop growth models. Ecological Modelling 41(3-4): 247-268.
    • Maas, S. J. 1988b. Using satellite data to improve model estimates of crop yield. Agron. J. 80(4): 655-662.
    • Moran, M. S., Y. Inoue, and E. M. Barnes. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environ. 61(3): 319-346.
    • Moulin, S., A. Bondeau, and R. Delecolle. 1998. Combining agricultural crop models and satellite observations: From field to regional scales. Intl. J. Remote Sensing 19(6): 1021-1036.
    • Nain AS, Dadhwal VK and Singh TP. 2002. Real time wheat yield assessment using technology trend and crop simulation model with minimal dataset. Current Science, Vol. 82, No.10, 1255-1258 Tsuji, G. Y. (2003). DSSAT4.0 User's Guide, 1-4. The University of Hawaii.
    • Weiss, M., Baret, F., Myneni, R.B., Pragnere, A., Knyazikhin, J. (2000): Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie, INRA, Vol.20, p.3-22.
    • Reddy.1986. Crop simulation models in agronomic systems.
    • Advances in Agron. 40: 141-208 Xie, Y., Z. Sha, and M. Yu. 2008. Remote sensing imagery in vegetation mapping: A review. J. Plant Ecology 1(1): 9-23.
    • Xin, J., Zhenrong, Y., van Leeuwenb, L., Driessen, P.M., 2002.
    • Geoinform. 4, 109-117.
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