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

Yıl 2025, Cilt: 12 Sayı: 2, 45 - 57, 30.06.2025

Neslişah Civelek , Levent Genç , Özgün Akçay

Öz

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.

Anahtar Kelimeler

Winter wheat yield Landsat 8-9 Decision tree Vegetation indices Machine learning

Etik Beyan

I hereby declare that this study complies with ethical standards.

Destekleyen Kurum

There is no organization supporting the study.

Teşekkür

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.

Kaynakça

  • Adeniyi, D.O., Szabo, A., Tama, J., Nagy, A. (2020). Winter wheat yield forecasting is based on Landsat NDVI and SAVI time series. https://doi.org/10.20944/preprints202007.0065.v
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  • Ashfaq, M., Khan, I., Alzahrani, A., Tariq, M. U., Khan, H., & Ghani, A. (2024). Accurate wheat yield prediction using machine learning and climate-NDVI data fusion. IEEE Access, 12, 40947–40961. https://doi.org/10.1109/ACCESS.2024.3376735
  • Ayub, M., Khan, N.A., Haider, R.Z. (2022). Winter wheat crop field and yield prediction using remote sensing and machine learning. 2nd IEEE International Conference on Artificial Intelligence (ICAI), 158–164. https://doi.org/10.1109/ICAI55435.2022.9773663
  • Bebie, M., Cavalaris, C., Kyparissis, A. (2022). Assessing durum winter wheat yield through Sentinel-2 imagery: A machine learning approach. Remote Sensing Sciences, 14(16). https://doi.org/10.3390/rs14163880
  • Bouras, E. houssaine, Olsson, P. O., Thapa, S., Díaz, J. M., Albertsson, J., & Eklundh, L. (2023). wheat yield estimation at high spatial resolution through the assimilation of Sentinel-2 data into a crop growth model. Remote Sensing, 15(18). https://doi.org/10.3390/rs15184425
  • Cai, W.T., Liu, Y.X., Li, M.C., Zhang, Y., Li, Z. (2010). The best-first multivariate decision tree method used for urban land cover classification.
  • Campos, I., Gonzalez, G.L., Villodre, J., Calera, M., Campoy, J., Jimenez, N., Plaza, C., Sanchez, P.S., Calera, A. (2019). Mapping within-field variability in winter wheat yield and biomass using remote sensing vegetation indices. Precision Agriculture, 20(2), 214–236. https://doi.org/10.1007/s11119-018-9596-z
  • Castaldi, F., Casa, R., Pelosi, F., & Yang, H. (2015). Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data. International Journal of Remote Sensing, 36(9), 2438–2459. https://doi.org/10.1080/01431161.2015.1041174
  • Cavalaris, C., Megoudi, S., Maxouri, M., Anatolitis, K., Sifakis, M., Levizou, E., Kyparissis, A. (2021). Modeling of durum winter wheat yield based on sentinel-2 imagery, Agronomy, 11(8). https://doi.org/10.3390/agronomy11081486
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  • Cheng, E., Zhang, B., Peng, D., Zhong, L., Yu, L., Liu Y, Xiao C, Li C, Li X, Chen Y, Ye H, Wang H, Yu R, Hu J, Yang S (2022). Winter wheat yields estimation using remote sensing data based on machine learning approaches. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.1090970
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Yıl 2025, Cilt: 12 Sayı: 2, 45 - 57, 30.06.2025

Neslişah Civelek , Levent Genç , Özgün Akçay

Öz

Kaynakça

  • Adeniyi, D.O., Szabo, A., Tama, J., Nagy, A. (2020). Winter wheat yield forecasting is based on Landsat NDVI and SAVI time series. https://doi.org/10.20944/preprints202007.0065.v
  • Aggarwal, P., Sharma, S.K. (2015). An empirical comparison of classifiers to analyze intrusion detection. International Conference on Advanced Computing and Communication Technologies, 446–450. https://doi.org/10.1109/ACCT.2015.59
  • Aghlmand, M., Kalkan, K., Onur, M.I., Öztürk, G., Ulutak, E. (2021). Google Earth Engine ile arazi kullanımı haritalarının üretimi. Ömer Halisdemir University Journal of Engineering Sciences, 10(1), 038-047. https://doi.org/10.28948/ngumuh.795977
  • Ashfaq, M., Khan, I., Alzahrani, A., Tariq, M. U., Khan, H., & Ghani, A. (2024). Accurate wheat yield prediction using machine learning and climate-NDVI data fusion. IEEE Access, 12, 40947–40961. https://doi.org/10.1109/ACCESS.2024.3376735
  • Ayub, M., Khan, N.A., Haider, R.Z. (2022). Winter wheat crop field and yield prediction using remote sensing and machine learning. 2nd IEEE International Conference on Artificial Intelligence (ICAI), 158–164. https://doi.org/10.1109/ICAI55435.2022.9773663
  • Bebie, M., Cavalaris, C., Kyparissis, A. (2022). Assessing durum winter wheat yield through Sentinel-2 imagery: A machine learning approach. Remote Sensing Sciences, 14(16). https://doi.org/10.3390/rs14163880
  • Bouras, E. houssaine, Olsson, P. O., Thapa, S., Díaz, J. M., Albertsson, J., & Eklundh, L. (2023). wheat yield estimation at high spatial resolution through the assimilation of Sentinel-2 data into a crop growth model. Remote Sensing, 15(18). https://doi.org/10.3390/rs15184425
  • Cai, W.T., Liu, Y.X., Li, M.C., Zhang, Y., Li, Z. (2010). The best-first multivariate decision tree method used for urban land cover classification.
  • Campos, I., Gonzalez, G.L., Villodre, J., Calera, M., Campoy, J., Jimenez, N., Plaza, C., Sanchez, P.S., Calera, A. (2019). Mapping within-field variability in winter wheat yield and biomass using remote sensing vegetation indices. Precision Agriculture, 20(2), 214–236. https://doi.org/10.1007/s11119-018-9596-z
  • Castaldi, F., Casa, R., Pelosi, F., & Yang, H. (2015). Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data. International Journal of Remote Sensing, 36(9), 2438–2459. https://doi.org/10.1080/01431161.2015.1041174
  • Cavalaris, C., Megoudi, S., Maxouri, M., Anatolitis, K., Sifakis, M., Levizou, E., Kyparissis, A. (2021). Modeling of durum winter wheat yield based on sentinel-2 imagery, Agronomy, 11(8). https://doi.org/10.3390/agronomy11081486
  • Chauhan, H., Kumar, V., Pundir, S., Pilli, E.S. (2013). A comparative study of classification techniques for intrusion detection. Proceedings 2013 International Symposium on Computational and Business Intelligence (ISCBI), 40–43. https://doi.org/10.1109/ISCBI.2013.16
  • Cheng, E., Zhang, B., Peng, D., Zhong, L., Yu, L., Liu Y, Xiao C, Li C, Li X, Chen Y, Ye H, Wang H, Yu R, Hu J, Yang S (2022). Winter wheat yields estimation using remote sensing data based on machine learning approaches. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.1090970
  • Deng, S., Gao, M., Ren, C., Li, S., Liang, Y. (2022). Extraction of sugarcane planting area based on similarity of NDVI time series. IEEE Access, 10, 117362–117373. https://doi.org/10.1109/ACCESS.2022.3219841
  • Du, X., Zhu, J., Xu, J., Li, Q., Tao, Z., Zhang, Y., Wang, H., & Hu, H. (2025). Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations. International Journal of Digital Earth, 18(1). https://doi.org/10.1080/17538947.2024.2443470
  • Faqe Ibrahim, G. R., Rasul, A., & Abdullah, H. (2023). Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8. International Journal of Remote Sensing, 44(13), 4115–4136. https://doi.org/10.1080/01431161.2023.2232542
  • Genc, L., Turhan, H., Asar, B., & Smith, S. (2009). Comparision of spectral indices derived from QICKBIRD and ground based hyper-spectral data for winter wheat. World Applied Sciences Journal, 7, 756-762. Genc, L., Demirel, K., Çamoğlu, G., Aşık, S., Smith, S. (2011). Determination of plant water stress using spectral reflectance measurements in watermelon (Citrullus vulgaris). American-Eurasian J. Agric. & Environ. Sci., 11(2), 296-304. Goldberg, K., Herrmann, I., Hochberg, U., Rozenstein, O. (2021). Generating up-to-date crop maps optimized for Sentinel-2 imagery in Israel. Remote Sensing, 13(17). https://doi.org/10.3390/rs13173488
  • Gupta, B., Uttarakhand, P., Rawat, I.A. (2017). Analysis of various decision tree algorithms for classification in data mining. International Journal of Computer Applications, 163(8), 0975-8887.
  • Inalpulat, M., Genc, L. (2019). Monitoring short term seasonal changes in wetlands: Aremote sensing study of Kumkale, Çanakkale (Türkiye). International Symposium on Biodiversity Research.
  • Jamali, M., Bakhshandeh, E., Yeganeh, B., Özdoğan, M. (2023). Development of machine learning models for estimating winter wheat biophysical variables using satellite-based vegetation indices. Advances in Space Research, 73(1), 498–513. https://doi.org/10.1016/j.asr.2023.10.004
  • Khan, H. R., Gillani, Z., Jamal, M. H., Athar, A., Chaudhry, M. T., Chao, H., He, Y., Chen, M. (2023). Early identification of crop type for smallholder farming systems using deep learning on time series Sentinel 2 imagery. Sensors Science, 23(4). https://doi.org/10.3390/s23041779
  • Khechba, K., Belgiu, M., Laamrani, A., Stein, A., Amazirh, A., & Chehbouni, A. (2025). The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning. International Journal of Applied Earth Observation and Geoinformation, 136. https://doi.org/10.1016/j.jag.2025.104367
  • Kobayashi, N., Tani, H., Wang, X., Sonobe, R. (2020). Crop classification using spectral indices derived from Sentinel 2A imagery. Journal of Information and Telecommunication, 4(1), 67–90. https://doi.org/10.1080/24751839.2019.1694765
  • Liu, S., Peng, D., Zhang, B., Chen, Z., Yu, L., Chen, J., Pan, Y., Zheng, S., Hu, J., Lou, Z., Chen, Y., Yang, S. (2022). The accuracy of winter winter wheat identification at different growth stages using remote sensing. Remote Sensing, 14(4). https://doi.org/10.3390/rs14040893
  • Ma, C., Liu, M., Ding, F., Li, C., Cui, Y., Chen, W., & Wang, Y. (2022). Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-09535-9
  • Mallissery, S., Kolekar, S., Ganiga, R. (2013). Accuracy analysis of machine learning algorithms for intrusion detection system using NSL-KDD dataset. https://doi.org/10.13140/RG.2.1.5018.0247
  • Mashonganyika, F., Mugiyo, H., Svotwa, E., Kutywayo, D. (2021). Mapping of winter winter wheat using Sentinel-2 NDVI data a case of Mashonaland Central Province in Zimbabwe. Frontiers in Climate, 3. https://doi.org/10.3389/fclim.2021.715837
  • Memon, M. S., Chen, S., Niu, Y., Zhou, W., Elsherbiny, O., Liang, R., Du, Z., Guo, X. (2023). Evaluating the efficacy of Sentinel-2B and Landsat-8 for estimating and mapping winter wheat straw cover in rice winter wheat fields. Agronomy, 13(11). https://doi.org/10.3390/agronomy13112691
  • Nagy, A., Szabo, A., Adeniyi, O. D., Tamas, J. (2021). Winter wheat yield forecasting for the Tisza River catchment using Landsat 8 NDVI and SAVI time series and reported crop statistics. Agronomy, 11(4). https://doi.org/10.3390/agronomy11040652
  • Navada, A., Ansari, A. N., Patil, S., Sonkamble, B. A. (2011). Overview of use of decision tree algorithms in machine learning. In 2011 IEEE Control and System Graduate Research Colloquium, Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.
  • Naqvi, S. M. Z. A., Tahir, M. N., Shah, G. A., Sattar, R. S., Awais, M. (2018). Remote estimation of winter wheat yield based on vegetation indices derived from time series data of Landsat 8 imagery. Applied Ecology and Environmental Research, 17(2), 3909–3925. https://doi.org/10.15666/aeer/1702_39093925
  • Newete, S. W., Abutaleb, K., Chirima, G. J., Dabrowska-Zielinska, K., & Gurdak, R. (2024). Phenology-based winter wheat classification for crop growth monitoring using multi-temporal Sentinel-2 satellite data. Egyptian Journal of Remote Sensing and Space Science, 27(4), 695–704. https://doi.org/10.1016/j.ejrs.2024.10.001
  • Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote sensing, 2(3), 673-696.
  • Purwanto, A.D., Wikantika, K., Deliar, A., Darmawan, S. (2022). Decision tree and random forest classification algorithms for mangrove forest mapping in Sembilang National Park Indonesia. Remote Sensing, 15(1). https://doi.org/10.3390/rs15010016
  • Qi, X., Wang, Y., Peng, J., Zhang, L., Yuan, W. (2022). The 10-meter winter winter wheat mapping in Shandong province using Sentinel-2 data and coarse resolution maps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 9760–9774. https://doi.org/10.1109/JSTARS.2022.3220698
  • Saad El Imanni, H., El Harti, A., & El Iysaouy, L. (2022). Winter wheat yield estimation using remote sensing indices derived from Sentinel-2 time series and Google Earth Engine in a highly fragmented and heterogeneous agricultural region. Agronomy, 12(11). https://doi.org/10.3390/agronomy12112853
  • Segarra, J., Gonzalez Torralba, J., Aranjuelo, I., Araus, J. L., Kefauver, S. C. (2020). Estimating winter wheat grain yield using Sentinel-2 imagery and exploring topographic features and rainfall effects on winter wheat performance in Navarre, Spain. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142278
  • Skakun, S., Franch, B. E., Vermote, J. C., Roger, C., Justice, J., Masek, E., Murphy. (2018). Winter winter wheat yield assessment using Landsat 8 and Sentinel-2 data. In Proc. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.10.1109/IGARSS.2018.8518885
  • Esfandabadi, H. S., Asl, G.M., Esfandabadi, Z. S., Gautam, S., Ranjbari, M. (2021). Drought assessment in paddy rice fields using remote sensing technology towards achieving food security and SDG2. British Food Journal, 124(12), 4219–4233. https://doi.org/10.1108/BFJ-08-2021-0872
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  • Yucebas, S., Dogan, M., Genc, L. (2022). A C4.5 – Cart decision tree model for real estate price prediction and the analysis of the underlying features. Konya Journal of Engineering Sciences, 10(1), 147–161. https://doi.org/10.36306/konjes.1013833
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Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Research Articles
Yazarlar

Neslişah Civelek 0009-0007-6077-7689

Levent Genç 0000-0002-0074-0987

Özgün Akçay 0000-0003-0474-7518

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 20 Mart 2025
Kabul Tarihi 20 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 2

Kaynak Göster

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.

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