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Estimation of Rubber Yield Using Sentinel-2 Satellite Data

Author

Listed:
  • Niwat Bhumiphan

    (Faculty of Engineering, Mahasarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand)

  • Jurawan Nontapon

    (Faculty of Engineering, Mahasarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand)

  • Siwa Kaewplang

    (Faculty of Engineering, Mahasarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand)

  • Neti Srihanu

    (Faculty of Engineering, Northeastern University, Muang District, Khon Kaen 40000, Thailand)

  • Werapong Koedsin

    (Faculty of Technology and Environment, Phuket Campus, Prince of Songkla University, Phuket 83120, Thailand)

  • Alfredo Huete

    (Faculty of Technology and Environment, Phuket Campus, Prince of Songkla University, Phuket 83120, Thailand
    School of Life Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

Rubber is a perennial plant grown to produce natural rubber. It is a raw material for industrial and non-industrial products important to the world economy. The sustainability of natural rubber production is, therefore, critical for smallholder livelihoods and economic development. To maintain price stability, it is important to estimate the yields in advance. Remote sensing technology can effectively provide large-scale spatial data; however, productivity estimates need to be processed from high spatial resolution data generated from satellites with high accuracy and reliability, especially for smallholder livelihood areas where smaller plots contrast with large farms. This study used reflectance data from Sentinel-2 satellite imagery acquired for the 12 months between December 2020 and November 2021. The imagery included 213 plots where data on rubber production in smallholder agriculture were collected. Six vegetation indices (Vis), namely Green Soil Adjusted Vegetation Index (GSAVI), Modified Simple Ratio (MSR), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Normalized Green (NR), and Ratio Vegetation Index (RVI) were used to estimate the rubber yield. The study found that the red edge spectral band (band 5) provided the best prediction with R 2 = 0.79 and RMSE = 29.63 kg/ha, outperforming all other spectral bands and VIs. The MSR index provided the highest coefficient of determination, with R 2 = 0.62 and RMSE = 39.25 kg/ha. When the red edge reflectance was combined with the best VI, MSR, the prediction model only slightly improved, with a coefficient determination of (R 2 ) of 0.80 and an RMSE of 29.42 kg/ha. The results demonstrated that the Sentinel-2 data are suitable for rubber yield prediction for smallholder farmers. The findings of this study can be used as a guideline to apply in other countries or areas. Future studies will require the use of reflectance and vegetation indices derived from satellite data in combination with meteorological data, as well as the application of complex models, such as machine learning and deep learning.

Suggested Citation

  • Niwat Bhumiphan & Jurawan Nontapon & Siwa Kaewplang & Neti Srihanu & Werapong Koedsin & Alfredo Huete, 2023. "Estimation of Rubber Yield Using Sentinel-2 Satellite Data," Sustainability, MDPI, vol. 15(9), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7223-:d:1133493
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    References listed on IDEAS

    as
    1. P.K. Viswanathan, 2008. "Emerging Smallholder Rubber Farming Systems in India and Thailand: A Comparative Economic Analysis," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 5(2), pages 1-19, December.
    2. Paratta Promme & John K.M. Kuwornu & Damien Jourdain & Ganesh P. Shivakoti & Peeyush Soni, 2017. "Factors influencing rubber marketing by smallholder farmers in Thailand," Development in Practice, Taylor & Francis Journals, vol. 27(6), pages 865-879, August.
    3. Viswanathan, P.K., 2008. "Emerging Smallholder Rubber Farming Systems in India and Thailand: A Comparative Economic Analysis," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 5(2), pages 1-19, December.
    4. Manivong, Vongpaphane & Cramb, Rob A., 2007. "Economics of Smallholder Rubber Production in Northern Laos," 2007 Conference (51st), February 13-16, 2007, Queenstown, New Zealand 10380, Australian Agricultural and Resource Economics Society.
    5. Mohamad M. Awad, 2019. "Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques," Agriculture, MDPI, vol. 9(3), pages 1-13, March.
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    Cited by:

    1. 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.

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