Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform

Ramazan Güngör; Füsun Balık Şanlı; Ali Murat Ateş; Osman Salih Yılmaz

doi:10.15832/ankutbd.1728949

Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform

Abstract

Accurate mapping of greenhouse areas is critically important for enhancing agricultural productivity and mitigating environmental impacts. Recently, remote sensing technologies have emerged as powerful tools for detailed and accurate detection of greenhouse areas and land use. The main objective of the study, which is to evaluate the effectiveness of spectral indices and Machine Learning (ML) algorithms in detecting greenhouse areas. This study employs Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART) ML algorithms to classify Harmonized Sentinel-2 MSI (Multispectral Instrument) satellite imagery using the Google Earth Engine (GEE) platform. In addition, indices such as the Normalized Difference Vegetation Index (NDVI), Plastic Greenhouse Index (PGI), Retrogressive Plastic Greenhouse Index (RPGI), Plastic Mulched Landcover Index (PMLI), and Greenhouse Vegetable Land Extraction Index (Vi) were calculated and incorporated as bands for classification. In the study, a total of 7 data sets were created using various ML algorithms and indices. The highest overall accuracy (OA) and kappa (Κ) values were obtained as 88.10% and 0.804, respectively, in the classification using the PGI and RF algorithm. To test the significance of the accuracy assessment, the McNemar test was applied. The most significant relationship was observed in comparisons using the PGI and RF classifier, where the calculated statistic was greater than the critical x² value(x²=3.84 at 95% confidence interval).

Keywords

References

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Details

Primary Language

English

Subjects

Agricultural Land Planning, Agricultural Spatial Analysis and Modelling

Journal Section

Research Article

Authors

Ramazan Güngör ^*
0000-0002-6338-8554
Türkiye

Füsun Balık Şanlı
0000-0003-1243-8299
Türkiye

Ali Murat Ateş
0000-0002-2815-1404
Türkiye

Osman Salih Yılmaz
0000-0003-4632-9349
Türkiye

Publication Date

January 20, 2026

Submission Date

June 27, 2025

Acceptance Date

September 20, 2025

Published in Issue

Year 2026 Volume: 32 Number: 1

DOI

https://doi.org/10.15832/ankutbd.1728949

IZ

https://izlik.org/JA75LZ95LX

Cite

RIS / Bibtex

APA

Güngör, R., Balık Şanlı, F., Ateş, A. M., & Yılmaz, O. S. (2026). Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform. Journal of Agricultural Sciences, 32(1), 158-176. https://doi.org/10.15832/ankutbd.1728949

AMA

1.Güngör R, Balık Şanlı F, Ateş AM, Yılmaz OS. Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform. J Agr Sci-Tarim Bili. 2026;32(1):158-176. doi:10.15832/ankutbd.1728949

Chicago

Güngör, Ramazan, Füsun Balık Şanlı, Ali Murat Ateş, and Osman Salih Yılmaz. 2026. “Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform”. Journal of Agricultural Sciences 32 (1): 158-76. https://doi.org/10.15832/ankutbd.1728949.

EndNote

Güngör R, Balık Şanlı F, Ateş AM, Yılmaz OS (January 1, 2026) Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform. Journal of Agricultural Sciences 32 1 158–176.

IEEE

[1]R. Güngör, F. Balık Şanlı, A. M. Ateş, and O. S. Yılmaz, “Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform”, J Agr Sci-Tarim Bili, vol. 32, no. 1, pp. 158–176, Jan. 2026, doi: 10.15832/ankutbd.1728949.

ISNAD

Güngör, Ramazan - Balık Şanlı, Füsun - Ateş, Ali Murat - Yılmaz, Osman Salih. “Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform”. Journal of Agricultural Sciences 32/1 (January 1, 2026): 158-176. https://doi.org/10.15832/ankutbd.1728949.

JAMA

1.Güngör R, Balık Şanlı F, Ateş AM, Yılmaz OS. Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform. J Agr Sci-Tarim Bili. 2026;32:158–176.

MLA

Güngör, Ramazan, et al. “Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform”. Journal of Agricultural Sciences, vol. 32, no. 1, Jan. 2026, pp. 158-76, doi:10.15832/ankutbd.1728949.

Vancouver

1.Ramazan Güngör, Füsun Balık Şanlı, Ali Murat Ateş, Osman Salih Yılmaz. Evaluation of Classification Accuracy of Spectral Indices and Machine Learning Algorithms for Greenhouse Detection Using the Google Earth Engine Platform. J Agr Sci-Tarim Bili. 2026 Jan. 1;32(1):158-76. doi:10.15832/ankutbd.1728949