Reliable Orchard Mapping in Data-Scarce Landscapes

Articles in Press

Document Type : Research Article- En

Authors

N. Ahmadi Sani ¹

S. Moradi ²

J. Henareh ³

M. Pato ³

¹ Institute of Agriculture, Water, Food, and Nutraceuticals, Mah. C., Islamic Azad University, Mahabad, Iran

² Department of Agricultural & Natural Resources Development, Faculty of Engineering, Payam Noor University, Tehran, Iran

³ Forests and Rangelands Research Department, West Azerbaijan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Urmia, Iran

https://doi.org/10.22067/jam.2026.96725.1449

Abstract

This study assesses the capability of Sentinel-2A imagery combined with machine-learning classifiers on the Google Earth Engine platform for accurate orchard mapping in the environmentally stressed Sharviran Plain along the southern margin of Lake Urmia, Iran. Random Forest, Support Vector Machine, and Classification and Regression Trees were applied to multispectral bands and selected spectral and vegetation indices, using 836 systematically collected samples from high-resolution imagery and field surveys for training and validation. The study specifically focuses on orchard mapping, with particular emphasis on reliably distinguishing orchards from other agricultural and non-agricultural land-use classes. Results show that orchards cover approximately 11% of the study area, and that the Random Forest classifier achieved the highest performance (Kappa = 0.84) and the best orchard F1-score, demonstrating superior class-level discrimination. Validation against 2023 ground-based orchard statistics showed a low error margin of 2.2%, confirming the reliability of the approach for operational orchard mapping. These findings confirm that integrating Sentinel-2A imagery with machine-learning classifiers on cloud-based platforms offers a robust, reproducible, and cost-efficient framework for orchard mapping in data-scarce and drought-affected regions. The resulting high-resolution orchard maps can effectively support agricultural planning, irrigation management, and evidence-based policy-making in the Lake Urmia basin. Future work may further improve performance by incorporating multi-temporal imagery, additional ecological and socio-economic variables, and advanced models such as deep learning.

Keywords

Machine learning

Orchard mapping

Sentinel 2

Urmia Lake

Subjects

• Digital, Smart, and Precision Agriculture

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., ..., & Hasanzadeh, E. (2015). Aral Sea syndrome desiccates Lake Urmia: call for action. Journal of Great Lakes Research, 41(1), 307-311. https://doi.org/10.1016/j.jglr.2014.12.007
2. Aldiansyah, S., & Saputra, R. A. (2023). Comparison of machine learning algorithms for land use and land cover analysis using Google Earth engine (Case study: Wanggu watershed). International Journal of Remote Sensing and Earth Sciences (IJReSES), 19(2), 197-210. https://doi.org/10.30536/j.ijreses.2022.v19.a3803
3. Belgiu, M., & Drăguț, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
4. Biswas, J., Jobaer, M. A., Haque, S. F., Islam Shozib, M. S., & Limon, Z. A. (2023). Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh. Heliyon, 9(11), e21245. https://doi.org/10.1016/j.heliyon.2023.e21245
5. Camargo, F. F., Sano, E. E., Almeida, C. M., Mura, J. C., & Almeida, T. (2019). A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images. Remote Sensing, 11(13), 1600. https://doi.org/10.3390/rs11131600
6. Chen, P., Ren, Y., Zhang, B., & Zhao, Y. (2025). Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2025.3555567
7. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), https://doi.org/10.1186/s12864-019-6413-7
8. Fereres, E., & Soriano, M. A. (2007). Deficit irrigation for reducing agricultural water use. Journal of Experimental Botany, 58(2), 147-159. https://doi.org/10.1093/jxb/erl165
9. Ge, G., Shi, Z., Zhu, Y., Yang, X., & Hao, Y. (2020). Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22, e00971. https://doi.org/10.1016/j.gecco.2020.e00971
10. Ghosh, S., Kumar, D., & Kumari, R. (2022). Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with google earth engine (GEE). Environmental Challenges, 9, 100605. https://doi.org/10.1016/j.envc.2022.100605
11. Guzmán, G., Boumahdi, A., & Gómez, J. A. (2022). Expansion of olive orchards and their impact on the cultivation and landscape through a case study in the countryside of Cordoba (Spain). Land Use Policy, 116, 106065. https://doi.org/10.1016/j.landusepol.2022.106065
12. Huang, Y., Chen, Z., Yu, T., Huang, X., & Gu, X. (2018). Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(9), 1915-1931. https://doi.org/10.1016/S2095-3119(17)61859-8
13. Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote sensing, 8(3), 166. https://doi.org/10.3390/rs8030166
14. Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39, 2784-2817. https://doi.org/10.1080/01431161.2018.1433343
15. Modica, G., Messina, G., De Luca, G., Fiozzo, V., & Pratico, S. (2020). Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery. Computers and Electronics in Agriculture, 175, 105500. https://doi.org/10.1016/j.compag.2020.105500
16. Nery, T., Sadler, R., Solis-Aulestia, M., White, B., Polyakov, M., & Chalak, M. (2016, July). Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)(pp. 5165-5168). IEEE. https://doi.org/10.1109/IGARSS.2016.7730346
17. Nigar, A., Li, Y., Jat Baloch, M. Y., Alrefaei, A. F., & Almutairi, M. H. (2024). Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications. Frontiers in Environmental Science, 12, 1378443. https://doi.org/10.3389/fenvs.2024.1378443
18. Oo, T. K., Arunrat, N., Sereenonchai, S., Ussawarujikulchai, A., Chareonwong, U., & Nutmagul, W. (2022). Comparing four machine learning algorithms for land cover classification in gold mining: A case study of Kyaukpahto Gold Mine, Northern Myanmar. Sustainability, 14(17), 10754. https://doi.org/10.3390/su141710754
19. Pande, C. B., Diwate, P., Orimoloye, I. R., Sidek, L. M., Pratap Mishra, A., Moharir, K. N., Pal, S. C., Alshehri, F., & Tolche, A. D. (2024). Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling. Geomatics, Natural Hazards and Risk, 15(1), 2290350. https://doi.org/10.1080/19475705.2023.2290350
20. Patil, A., & Panhalkar, S. (2023). A Comparative analysis of machine learning algorithms for land use and land cover classification using google earth engine platform. Journal of Geomatics, 17(2), 226-233. https://doi.org/10.58825/jog.2023.17.2.96
21. Pereira, S. C., Lopes, C., & Pedroso, J. P. (2022). Mapping cashew orchards in cantanhez national park (Guinea-Bissau). Remote Sensing Applications: Society and Environment, 26, 100746. https://doi.org/10.1016/j.rsase.2022.100746
22. Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12, 2291. https://doi.org/10.3390/rs12142291
23. Revel, C., Lonjou, V., Marcq, S., Desjardins, C., Fougnie, B., Coppolani-Delle Luche, C., Guilleminot, N., Lacamp, A., Lourme, E., Miquel, Ch., & Lenot, X. (2019). Sentinel-2A and 2B absolute calibration monitoring. European Journal of Remote Sensing, 52(1), 122-137. https://doi.org/10.1080/22797254.2018.1562311
24. Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x
25. Schulz, D., Yin, H., Tischbein, B., Verleysdonk, S., Adamou, R., & Kumar, N. (2021). Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 97-111. https://doi.org/10.1016/j.isprsjprs.2021.06.005
26. Sharma, V., Sharma, D., Tsai, M. L., Ortizo, R. G. G., Yadav, A., Nargotra, P., ..., & Dong, C. D. (2023). Insights into the recent advances of agro-industrial waste valorization for sustainable biogas production. Bioresource Technology, 129829. https://doi.org/10.1016/j.biortech.2023.129829
27. Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing, 12(19), 1-36. https://doi.org/10.3390/rs12193136
28. Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135.https://doi.org/10.3390/rs12071135
29. Tan, J., Zuo, J., Xie, X., Ding, M., Xu, Z., & Zhou, F. (2021). MLAs land cover mapping performance across varying geomorphology with Landsat OLI-8 and minimum human intervention. Ecological Informatics, 61, 101227. https://doi.org/10.1016/j.ecoinf.2021.101227
30. Wang, Y., Han, X., Yan, W., Cheng, L., Dang, X., & Liu, W. (2023). Apple trees can extract soil water from both deep layers and neighboring cropland in the tableland region of Chinese Loess Plateau. CATENA, 232, 107396. https://doi.org/10.1016/j.catena.2023.107396
31. Yi, C., Zhao, X., Sun, Q., & Wang, Z. (2024). Assessing the accuracy of remote sensing data products: A multi-granular spatial sampling method. Future Generation Computer Systems, 159, 151-160. https://doi.org/10.1016/j.future.2024.04.062
32. Yimer, S. M., Bouanani, A., Kumar, N., Tischbein, B., & Borgemeister, C. (2024). Comparison of different machine-learning algorithms for land use land cover mapping in a heterogenous landscape over the Eastern Nile River basin, Ethiopia. Advances in Space Research, 74(5), 2180-2199. https://doi.org/10.1016/j.asr.2024.06.010
33. Younesi, B., Ahmadi Sani, N., & Sharafi, S. (2019). Evaluation of IRS-P6 Images for Orchards Area Estimating. Iranian Journal of Remote Sensing and GIS,11(1), 113-126. https://doi.org/10.52547/gisj.11.1.113
34. Zhao, Z., Islam, F., Waseem, L. A., Tariq, A., Nawaz, M., Islam, I. U., Bibi, T., Rehman, N. U., Ahmad, W., Aslam, R. W., Raza, D., & Hatamleh, W. A. (2024). Comparison of three machine learning algorithms using google earth engine for land use land cover classification. Rangeland Ecology & Management, 92, 129-137. https://doi.org/10.1016/j.rama.2023.10.007