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Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data

Author

Listed:
  • Abid Nazir

    (Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan)

  • Saleem Ullah

    (Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan)

  • Zulfiqar Ahmad Saqib

    (Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad 38040, Pakistan
    Agricultural Remote Sensing Laboratory (ARSL), National Centre of GIS and Space Application (NCGSA), University of Agriculture, Faisalabad 38040, Pakistan)

  • Azhar Abbas

    (Institute of Agriculture and Resource Economics, University of Agriculture, Faisalabad 38040, Pakistan)

  • Asad Ali

    (Department of Applied Mathematics and Statistics, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan)

  • Muhammad Shahid Iqbal

    (Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan)

  • Khalid Hussain

    (Department of Agronomy, Faculty of Agriculture, University of Agriculture, Faisalabad 38040, Pakistan)

  • Muhammad Shakir

    (Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan)

  • Munawar Shah

    (Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan)

  • Muhammad Usman Butt

    (Sustainable Rice Production, Galaxy Rice Mills Pvt Ltd., Gujranwala 52230, Pakistan)

Abstract

Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change scenarios, it is crucial to get timely and accurate rice yield estimates and production forecast of the growing season for governments, planners, and decision makers in formulating policies regarding import/export in the event of shortfall and/or surplus. This study aims to quantify the rice yield at various phenological stages from hyper-temporal satellite-derived-vegetation indices computed from time series Sentinel-II images. Different vegetation indices (viz. NDVI, EVI, SAVI, and REP) were used to predict paddy yield. The predicted yield was validated through RMSE and ME statistical techniques. The integration of PLSR and sequential time-stamped vegetation indices accurately predicted rice yield (i.e., maximum R 2 = 0.84 and minimum RMSE = 0.12 ton ha −1 equal to 3% of the mean rice yield). Moreover, our results also established that optimal time spans for predicting rice yield are late vegetative and reproductive (flowering) stages. The output would be useful for the farmer and decision makers in addressing food security.

Suggested Citation

  • Abid Nazir & Saleem Ullah & Zulfiqar Ahmad Saqib & Azhar Abbas & Asad Ali & Muhammad Shahid Iqbal & Khalid Hussain & Muhammad Shakir & Munawar Shah & Muhammad Usman Butt, 2021. "Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data," Agriculture, MDPI, vol. 11(10), pages 1-14, October.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:10:p:1026-:d:660124
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    References listed on IDEAS

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    1. Wardah Qamar & Muhammad Younas & Muhammad Waseem, 2019. "Price Fluctuations of Rice Crop in District Sheikhupura," Journal of Agricultural Studies, Macrothink Institute, vol. 7(3), pages 227-239, September.
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    Cited by:

    1. Mohsin Ramzan & Zulfiqar Ahmad Saqib & Ejaz Hussain & Junaid Aziz Khan & Abid Nazir & Muhammad Yousif Sardar Dasti & Saqib Ali & Nabeel Khan Niazi, 2022. "Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments," Land, MDPI, vol. 11(9), pages 1-19, September.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    3. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    4. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.
    5. Ammouri Bilel, 2024. "Forecasting agricultures security indices: Evidence from transformers method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1733-1746, September.
    6. Muhammet Fatih Aslan & Kadir Sabanci & Busra Aslan, 2024. "Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey," Sustainability, MDPI, vol. 16(18), pages 1-23, September.
    7. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    8. Ismail, AY & Nainggolan, MF & Andayani, SA & Isyanto, AY, 2024. "Sustainable Rice Farming In Indonesia," African Journal of Food, Agriculture, Nutrition and Development (AJFAND), African Journal of Food, Agriculture, Nutrition and Development (AJFAND), vol. 24(1), January.

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