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

Neslişah Civelek; Levent Genç; Özgün Akçay

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

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.

Keywords

Supporting Institution

There is no organization supporting the study.

Ethical Statement

I hereby declare that this study complies with ethical standards.

Thanks

The authors would like to express their gratitude to Neslisah CIVELEK, Levent GENC, and Ozgun AKCAY for their contributions in data processing, analysis, and the development of the yield prediction model. Additionally, the contributions of the team members in the ComAgEnPlan project have made this study possible.

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Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Neslişah Civelek
0009-0007-6077-7689
Türkiye

Levent Genç ^*
0000-0002-0074-0987
Türkiye

Özgün Akçay
0000-0003-0474-7518
Türkiye

Publication Date

June 30, 2025

Submission Date

March 20, 2025

Acceptance Date

May 20, 2025

Published in Issue

Year 2025 Volume: 12 Number: 2

IZ

https://izlik.org/JA26NG44SZ

Cite

RIS / Bibtex

APA

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, 12(2), 45-57. https://izlik.org/JA26NG44SZ

AMA

1.Civelek N, Genç L, Akçay Ö. Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions. IJEGEO. 2025;12(2):45-57. https://izlik.org/JA26NG44SZ

Chicago

Civelek, Neslişah, Levent Genç, and Özgün Akçay. 2025. “Predicting Winter Wheat Yield Using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions”. International Journal of Environment and Geoinformatics 12 (2): 45-57. https://izlik.org/JA26NG44SZ.

EndNote

Civelek N, Genç L, Akçay Ö (June 1, 2025) Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions. International Journal of Environment and Geoinformatics 12 2 45–57.

IEEE

[1]N. Civelek, L. Genç, and Ö. Akçay, “Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions”, IJEGEO, vol. 12, no. 2, pp. 45–57, June 2025, [Online]. Available: https://izlik.org/JA26NG44SZ

ISNAD

Civelek, Neslişah - Genç, Levent - Akçay, Özgün. “Predicting Winter Wheat Yield Using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions”. International Journal of Environment and Geoinformatics 12/2 (June 1, 2025): 45-57. https://izlik.org/JA26NG44SZ.

JAMA

1.Civelek N, Genç L, Akçay Ö. Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions. IJEGEO. 2025;12:45–57.

MLA

Civelek, Neslişah, et al. “Predicting Winter Wheat Yield Using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions”. International Journal of Environment and Geoinformatics, vol. 12, no. 2, June 2025, pp. 45-57, https://izlik.org/JA26NG44SZ.

Vancouver

1.Neslişah Civelek, Levent Genç, Özgün Akçay. Predicting Winter Wheat Yield using Landsat 8-9 Based Vegetation Indices in Semi-Arid Regions. IJEGEO [Internet]. 2025 Jun. 1;12(2):45-57. Available from: https://izlik.org/JA26NG44SZ