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Journals / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 8

Wheat yield prediction for sustainable food security using Sentinel-2 and machine learning

Neslişah Civelek(Çanakkale Onsekiz Mart University, Scholl of Graduate Studies, Department of Geographical Information Technologies, Çanakkale, Türki-ye;)0009-0007-6077-7689
Levent Genç(Çanakkale Onsekiz Mart University Faculty of Architecture and Design, Department of Urban and Regional Planning, Çanakkale, Türkiye)0000-0002-0074-0987
Özgün Akçay(Çanakkale Onsekiz Mart University, Faculty of Engineering, Department of Photogrammetry and Remote Sensing, Çanakkale)0000-0003-0474-7518
Pages
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Publication Date
2026-03-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed that the factors most affecting wheat yield were NDVI, WDRVI, RDVI, and Red Edge 1 in April; Derived Index 3 in December; and LAI, average temperature, and total precipitation in March.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate pre-harvest wheat yield estimation is critically important for sustainable agricultural policies.
📊
Key Findings & Data:In this study, 13 vegetation indices derived from Sentinel-2A/B images (10 from the literature and 3 derived for this study), temperature, precipitation, and sunshine duration data from the ERA-5 dataset, and ground measurements were used.
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Conclusion & Impact:The results showed that the factors most affecting wheat yield were NDVI, WDRVI, RDVI, and Red Edge 1 in April; Derived Index 3 in December; and LAI, average temperature, and total precipitation in March.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine learning · Decision tree · ERA-5

Abstract

Accurate pre-harvest wheat yield estimation is critically important for sustainable agricultural policies. In this study, 13 vegetation indices derived from Sentinel-2A/B images (10 from the literature and 3 derived for this study), temperature, precipitation, and sunshine duration data from the ERA-5 dataset, and ground measurements were used. Pearson correlation analysis was applied to determine the most suitable period for yield estimation, and the most effective factors were identified using the Decision Tree (DT) model. Considering these factors, yield estimation was performed using the Multiple Linear Regression (MLR) model. The results showed that the factors most affecting wheat yield were NDVI, WDRVI, RDVI, and Red Edge 1 in April; Derived Index 3 in December; and LAI, average temperature, and total precipitation in March. The heading-flowering period has been determined as the most suitable time for yield estimation. There is a high degree of agreement between the actual and predicted yields (R = 0.92, R² = 0.86).

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Cite This Article

Civelek, N., Genç, L., Akçay, Ö. (2026). Wheat yield prediction for sustainable food security using Sentinel-2 and machine learning. Türkiye Uzaktan Algılama Dergisi, Volume: 8. https://doi.org/10.51489/tuzal.1760511

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