Integration of CERES-rice crop simulation model and MODIS LAI (MOD15A2) for rice yield estimation

Authors

  • K. AJITH Regional Agricultural Research Station, Kerala Agricultural University, Kumarakom, Kerala, India
  • V. GEETHALAKSHMI Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
  • K. BHUVANESWAR Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
  • P. SHAJEESH JAN Regional Agricultural Research Station, Kerala Agricultural University, Ambalavayal, Kerala, India
  • ANU SUSAN SAM Regional Agricultural Research Station, Kerala Agricultural University, Kumarakom, Kerala, India
  • AJAI P. KRISHNA Government College, Mahatma Gandhi University, Kattappana, Kerala, India

DOI:

https://doi.org/10.54386/jam.v27i3.2973

Keywords:

DSSAT-CERES-rice model, remote sensing, rice yield prediction, MODIS LAI, Integration

Abstract

In this study assimilation of MODIS LAI (MOD15A2) into DSSAT-CERES-rice crop simulation model was used to develop advance yield estimates of rice crop during pre-harvest stage (F3) in Palakkad district of Kerala during Mundakan (September- January) season 2022-23 and 2023-24. The free parameters identified as inputs for the DSSAT-CERES-rice crop simulation model were adjusted and optimized sequentially during assimilation process until a minimum value of cost function is reached. This helped to minimize the deviation between MODIS- LAI and model generated LAI and the yield predicted by the model consequently is taken as the predicted yield. The average predicted yield during 2022-23 and 2023-24 was 5590 kgha-1 and 5124 kgha-1 respectively. The yield prediction by simulation model integrated with remote sensing products had higher accuracy than using simulation model alone during both the years with number of panchayats having the BIAS above ± 10 per cent reduced from 20 to 12 and 23 to 11 during 2022-23 and 2023-24 respectively. The findings clearly show that incorporating satellite data into crop simulation models can produce more accurate rice production forecasts than crop simulation techniques used alone.

References

Aschonitis, V. G., Papamichail, D. M., Lithourgidis, A. and Fano, E. A. (2014). Estimation of Leaf Area Index and Foliage Area Index of rice using an indirect gravimetric method. Soil Sci. Plant Anal., 45:1726–1740.https://doi.org/10.1080/00103624.2014.907917

Chaudhari, K.N., Rojalin, T. and Patel, N. K. (2010). Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data. J. Agrometeorol., 12 (2): 174-180. https://doi.org/10.54386/jam.v12i2.1300

Doraiswamy, P. C., Thomas, R. S., Steven, H., Bakhyt, A., Alan, S. and John, P. (2005). Application of MODIS derived parameters for regional crop yield assessment. Remote Sens. Environ., 97: 192-202.http://dx.doi.org/10.1016/j.rse.2005.03.015

Fang, H., Liang, S., Hoogenboom, G.,Teasdale, J. and Cavigelli, M. (2008). Corn yield estimation through assimilation of remote sensed data into the CSM–CERES–Maize model. Int. J. Remote Sens., 29: 3011–3032.https://doi.org/10.1080/01431160701408386

FAOSTAT. (2018). Food and Agriculture Organization of the United Nations

Gumma, M. K., Kadiyala, M. D. M., Panjala, P., Ray, S. S., Akuraju, V. R., Dubey, S., & Whitbread, A. M. (2022). Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. J. Indian Soc. Remote Sens., 50(2):257-270.

Hashimoto, N., Saito, Y., Yamamoto S., Ishibashi, T., Ito, R., Maki, M., and Homma, K. (2023). Relationship between leaf area index and yield components in farmers’ paddy fields. Agri. Engg., 5: 1754–1765https://doi.org/10.3390/agriengineering5040108

Inge, J., Stefan, F., Kris, N., Bart, M. and Pol. (2013). Methods for Leaf Area Index Determination Part I: Theories, Techniques and Instruments Department of Land management, Katholieke Universiteit Leuven, Vital Decosterstraat 102, 3000 Leuven, Belgium.

Kuwata, K., Wu W. and R. Shibasaki. (2010). A Study of assimilating various satellite data into crop growth model.

Ma, H., Huang, J., Zhu, D., Liu, J., Su, W., Zhang, C. and Fan, J. (2013). Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST-ACRM model with Ensemble Kalman Filter. Math. Comp. Model. 58(3-4):759- 770.

Mishra A., Mehra, B., Rawat S., Gautam, S., Ekta P., Singh M. G. (2020). Utility of gridded data for yield prediction of wheat using DSSAT model. J. Agrometeorol. 22 (3): 377-380. https://doi.org/10.54386/jam.v22i3.302

Noureldin, N. A., Aboelghar, A., Saudy, M. A., Saudy, H. S., and Ali, A. M. (2013). Rice yield forecasting models using satellite imagery in Egypt. The Egyptian J. Remote Sens. Space Sci., 16:125-131.https://doi.org/10.1016/j.ejrs.2013.04.005

Patel, N. R., Pokhariyal, S. and Singh, R. P. (2023). Advancements in remote sensing-based crop yield modelling in India. J. Agrometeorol., 25(3): 343-351. https://doi.org/10.54386/jam.v25i3.2316

Pazhanivelan S., Geethalakshmi V., Tamilmounika R., Sudarmanian N. S., Kaliaperumal R., Ramalingam K., Sivamurugan A. P., Mrunalini K., Yadav M. K., Quicho E. D. (2022). Spatial rice yield estimation using multiple linear regression analysis, Semi-Physical Approach and assimilating SAR satellite derived products with DSSAT crop simulation model. Agronomy. 12(9). https://doi.org/10.3390/agronomy12092008

Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (1992). Numerical recipes in fortran 77: The Art of Scientific Computing, New York: Cambridge University Press.

Setiyono T. D., Quicho E., Gatti, L., Campos-Taberner, M., Busetto L., Collivignarelli F., García-Haro F. J., Boschetti, M., Khan, N. I. and Holecz, F. (2018). Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model. Remote Sens., 10 (2): 293; https://doi.org/10.3390/rs10020293

Singh, Piara. (2023). Crop models for assessing impact and adaptation options under climate change. J. Agrometeorol., 25(1):18–33. https://doi.org/10.54386/jam.v25i1.1969

Shanmugapriya, P., Latha, K.R., Pazhanivelan, S., Kumaraperumal, R., Karthikeyan G. and Sudarmanian, N. S. (2022). Cotton yield prediction using drone derived LAI and chlorophyll content. J. Agrometeorol., 24(4): 348-352. https://doi.org/10.54386/jam.v24i4.1770

Grebmer K. V., Bernstein J., Wiemers, M., Reiner, L., Bachmeier, M., Hanano, A., Towey, O., Ní Chéilleachair, R., Foley, C., Gitter, S., Larocque, G. and Fritschel, H. (2022).

Global hunger index food systems transformation and local governance. Welthungerhilfe, Germany

Yang, P., Tan, G. X. and Shibasaki, Z,. (2010). Integrating remote sensing data with an ecosystem model to estimate crop yield in North China. http://www.isprs.org/proceedings/XXXV/congress/comm7/papers/29.pdf

Yoshida, S., Forno, D. A., Cock, H. J. and Gomez, K. A. (1976). Laboratory manual for physiological studies, 3rd edition. The International Rice Res. Institute, Manila, Philippines: 69.

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Published

01-09-2025

How to Cite

AJITH, K., GEETHALAKSHMI, V., BHUVANESWAR, K., JAN, P. S., SAM, A. S., & KRISHNA, A. P. (2025). Integration of CERES-rice crop simulation model and MODIS LAI (MOD15A2) for rice yield estimation. Journal of Agrometeorology, 27(3), 279–285. https://doi.org/10.54386/jam.v27i3.2973