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Rice yield prediction model using normalized vegetation and water indices from Sentinel-2A satellite imagery datasets

  • Climate Change Impacts On Regional Economics In South Asia
  • Published:
Asia-Pacific Journal of Regional Science Aims and scope Submit manuscript

Abstract

Yield predictions prior to harvesting crops is significant for agricultural decision-making. This study aimed to predict rice yield at the stage prior to harvesting using crops and soil phenological properties in the Pathein District of Myanmar. Remote sensing imagery data derived from Sentinel-2A satellite imageries during the month of November at the stage prior to harvest of rice fields were collected and analyzed from 2016 to 2021. Four vegetation indices (VIs): (i) normalized difference vegetation index (NDVI), (ii) normalized difference water index (NDWI), (iii) soil-adjusted vegetation index (SAVI), and (iv) rice growth vegetation index (RGVI) were specified as independent variables for a rice yield prediction model, after which simple and multiple linear regression models were estimated and validated. The accuracy of the estimated models was assessed using observed data from 1790 ground reference points (GRPs) in rice-yielding croplands. The average observed rice yield over 6 years was 1.57 tons per acre, and the average rice yield predictions over 6 years were 1.28, 1.48, 1.28, and 1.17 per acre with simple linear regression models from NDVI, NDWI, SAVI and RGVI, respectively. On the other hand, THE observed rice yield was 1.49 tons per acre with a multiple regression model. This indicates that prediction by the multiple regression model with four vegetation indices is superior to predictions by all other linear regression models. The early predicted yield data is useful for rice-growing farmers to compare expenses against losses after any extreme climatic event.

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Data availability

The datasets analyzed in this study can be available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to acknowledge the Japanese International Cooperation Agency (JICA) and Development of Core Human Resources in Agricultural Sector in Myanmar (Phase-2) for their support through graduate program sponsorship at the University of Tsukuba. The authors also express gratitude to the University of Tsukuba for the support through providing software facilities, the United States Geological Survey (USGS), European Space Agency for Sentinel II products, and Ministry of Agriculture Myanmar for Reference Rice Production Datasets.

Funding

This research did not receive any funding or grants.

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Authors and Affiliations

  1. Graduate School of Science and Technology, University of Tsukuba, Ibaraki, Japan

    Aung Myint Htun

  2. Agricultural Mechanization Department, Ministry of Agriculture, Livestock and Irrigation, Naypyitaw, Myanmar

    Aung Myint Htun

  3. Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan

    Md. Shamsuzzoha

  4. Department of Emergency Management, Faculty of Environmental Science and Disaster Management, Patuakhali Science and Technology University, Patuakhali, Bangladesh

    Md. Shamsuzzoha

  5. Faculty of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan

    Tofael Ahamed

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  1. Aung Myint Htun
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  2. Md. Shamsuzzoha
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Correspondence to Tofael Ahamed.

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Htun, A.M., Shamsuzzoha, M. & Ahamed, T. Rice yield prediction model using normalized vegetation and water indices from Sentinel-2A satellite imagery datasets. Asia-Pac J Reg Sci 7, 491–519 (2023). https://doi.org/10.1007/s41685-023-00299-2

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  • DOI: https://doi.org/10.1007/s41685-023-00299-2

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