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Journals / International Journal of Environment and Geoinformatics / 2026 / Volume: 12 Issue: 2

Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions

Neslişah Civelek(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ)0009-0007-6077-7689
Levent Genç(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, MİMARLIK VE TASARIM FAKÜLTESİ, ŞEHİR VE BÖLGE PLANLAMA BÖLÜMÜ)0000-0002-0074-0987
Özgün Akçay(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ)0000-0003-0474-7518
Pages
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Publication Date
2025-06-30
DOI
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AI Summary & Key Takeaways

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

The findings indicated that winter wheat yield could be predicted in April for fields classified as "Low Yield," "Medium Yield," and "High Yield" utilizing all indices except for EVI and SAVI.

📌 3 Key Takeaways

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Objective & Hypothesis:This study investigated the prediction of winter wheat yield in cultivation regions of Kumkale (Batakovası) Plain in Çanakkale Province, Turkiye, utilizing Landsat 8-9 imagery-based Vegetation Indices (VIs) alongside machine learning (ML) methodologies.
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Key Findings & Data:The VIs dataset was created by calculating images collected during the 2022 and 2023 growth seasons.
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Conclusion & Impact:The findings indicated that winter wheat yield could be predicted in April for fields classified as "Low Yield," "Medium Yield," and "High Yield" utilizing all indices except for EVI and SAVI.
🔬Design:Modeling & Simulation
📍Scope:2022 Verileri
⚙️Variables:Winter wheat yield · Landsat 8-9 · Decision tree

Abstract

This study investigated the prediction of winter wheat yield in cultivation regions of Kumkale (Batakovası) Plain in Çanakkale Province, Turkiye, utilizing Landsat 8-9 imagery-based Vegetation Indices (VIs) alongside machine learning (ML) methodologies. The VIs dataset was created by calculating images collected during the 2022 and 2023 growth seasons. The resulting dataset was employed in a C4.5 decision tree (DT) algorithm to predict winter wheat yield. The findings indicated that winter wheat yield could be predicted in April for fields classified as "Low Yield," "Medium Yield," and "High Yield" utilizing all indices except for EVI and SAVI. Interestingly, "High Yield" fields could also be predicted in March using the EVI index and in February using the SAVI index. The accuracy of the predictive models was evaluated based on the performance metrics of the DT algorithm, achieving accuracies ranging from 75.5% to 97.5% across the various indices. The study concluded that winter wheat yields can be predicted using Vegetation Indices (VIs) independently of climate data. Future research will concentrate on assessing yield predictions for additional crops by employing various machine learning algorithms alongside climate data and VIs derived from higher-resolution satellite imagery.

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Cite This Article

Civelek, N., Genç, L., Akçay, Ö. (2025). Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions. International Journal of Environment and Geoinformatics, Volume: 12 Issue: 2. https://ajindex.com/articles/1413091

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