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

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.

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Publication Details

Journal
Journal of Agrometeorology
Published
2025-09-01
DOI
https://doi.org/10.54386/jam.v27i3.2973
Citations
1
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
1.30

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article

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

K. AJITH, V. Geethalakshmi, P. Shajeesh Jan, A. Krishna et al.
1 citations
Journal of Agrometeorology
Remote Sensing and Land Use
1.30
article

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

K. AJITH, V. Geethalakshmi, P. Shajeesh Jan, A. Krishna, Anu Susan Sam, K. Bhuvaneswar
article en
1 citations

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.

Journal of AgrometeorologyVol. 27(3)
Tamil Nadu Agricultural University (IN), Mahatma Gandhi University (IN), Kerala Agricultural University (IN)
Kerala Agricultural University
Openalex Percentile: Top 19%
Remote Sensing and Land Use
1.30
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