TY  - JOUR
T1  - Deep learning-based semantic segmentation for surface water extraction from Sentinel-2 imagery: Case study of Kuş and Uluabat lakes, Türkiye
AU  - Abujayyab, Sohaib K M
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.51489/tuzal.1647078
JF  - Turkish Journal of Remote Sensing
JO  - TJRS
PB  - Osman ORHAN
WT  - DergiPark
SN  - 2687-4997
SP  - 91
EP  - 106
VL  - 7
IS  - 1
LA  - en
AB  - This study presents a deep learning-based approach for high-precision surface water extraction from Sentinel-2 multispectral imagery. A modified U-Net architecture, trained and evaluated on two Turkish lake systems (Kuş and Uluabat Lakes), achieved superior performance compared to traditional methods. The model attained an overall accuracy of 0.9980, precision of 0.9980, recall of 0.9980, F1-score of 0.9980, and Intersection over Union (IoU) of 0.9961, outperforming both Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI). Analysis reveals that the U-Net effectively mitigates spectral confusion in heterogeneous environments, demonstrating its potential for enhanced water resource monitoring, flood mapping, and hydrological modeling applications. While NDWI and MNDWI achieved IoU scores of 0.9956 and 0.9953, respectively, the deep learning model's higher IoU signifies more accurate boundary delineation. The improved performance highlights the value of deep learning in automated surface water mapping for enhanced decision-making in water resource management. These results suggest that while traditional spectral indices are useful for preliminary analysis, deep learning approaches offer a more refined classification, particularly in complex or heterogeneous landscapes.
KW  - Surface water extraction
KW  - Deep learning
KW  - Semantic segmentation
KW  - Sentinel-2
KW  - Kuş and Uluabat lakes
CR  - Al-Najjar, H. A. H., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A. A., Ueda, N., & Mansor, S. (2019). Land cover classification from fused DSM and UAV images using convolutional neural networks. Remote Sensing, 11(12). https://doi.org/10.3390/rs11121461
CR  - Asaro, F., Murdaca, G., & Prati, C. M. (2021, July 11-16). Learning deep models from weak labels for water surface segmentation in Sar images [Paper presentation]. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium. https://doi.org/10.1109/IGARSS47720.2021.9554647
CR  - Atik, Ş. Ö. (2023). Object-Based Integration Using Deep Learning and Multi-Resolution Segmentation in Building Extraction from Very high resolution satellite imagery. Turkish Journal of Remote Sensing, 5(2), 67–77. https://doi.org/10.51489/tuzal.1337656
CR  - Bai, Y., Wu, W., Yang, Z., Yu, J., Zhao, B., Liu, X., Yang, H., Mas, E., & Koshimura, S. (2021). Enhancement of detecting permanent water and temporary water in flood disasters by fusing Sentinel-1 and Sentinel-2 imagery using deep learning algorithms: Demonstration of sen1floods11 benchmark datasets. Remote Sensing, 13(11). https://doi.org/10.3390/rs13112220
CR  - Barlas, N., Akbulut, N., & Aydoğan, M. (2005). Assessment of heavy metal residues in the sediment and water samples of Uluabat Lake, Turkey. Bulletin of Environmental Contamination and Toxicology, 74(2), 286–293. https://doi.org/10.1007/s00128-004-0582-y
CR  - Bioresita, F., Puissant, A., Stumpf, A., & Malet, J.-P. (2018). A method for automatic and rapid mapping of water surfaces from Sentinel-1 imagery. Remote Sensing, 10(2). https://doi.org/10.3390/rs10020217
CR  - Campos-Taberner, M., García-Haro, F. J., Martínez, B., Izquierdo-Verdiguier, E., Atzberger, C., Camps-Valls, G., & Gilabert, M. A. (2020). Understanding deep learning in land use classification based on Sentinel-2 time series. Scientific Reports, 10(1), 17188. https://doi.org/10.1038/s41598-020-74215-5
CR  - Dervisoglu, A. (2021). Analysis of the temporal changes of inland ramsar sites in Turkey using google earth engine. ISPRS International Journal of Geo-Information, 10(8). https://doi.org/10.3390/ijgi10080521
CR  - Digra, M., Dhir, R., & Sharma, N. (2022). Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review. Arabian Journal of Geosciences, 15(10), 1003. https://doi.org/10.1007/s12517-022-10246-8
CR  - Fayaz, M., Nam, J., Dang, L. M., Song, H.-K., & Moon, H. (2024). Land-cover classification using deep learning with high-resolution remote-sensing imagery. Applied Sciences, 14(5). https://doi.org/10.3390/app14051844
CR  - Filik Iscen, C., Emiroglu, Ö., Ilhan, S., Arslan, N., Yilmaz, V., & Ahiska, S. (2008). Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat lake, Turkey. Environmental Monitoring and Assessment, 144(1), 269–276. https://doi.org/10.1007/s10661-007-9989-3
CR  - Li, C., Ma, Z., Wang, L., Yu, W., Tan, D., Gao, B., Feng, Q., Guo, H., & Zhao, Y. (2021). Improving the accuracy of land cover mapping by distributing training samples. Remote Sensing, 13(22). https://doi.org/10.3390/rs13224594
CR  - Liong, S. Y., & Sivapragasam, C. (2002). Flood stage forecasting with support vector machines. Journal of the American Water Resources Association, 38(1), 173–186. https://doi.org/10.1111/j.1752-1688.2002.tb01544.x
CR  - Moumane, A., Bahouq, T., Karmaoui, A., Laghfiri, D., Yassine, M., Karkouri, J. Al, Batchi, M., Faouzi, M., Boulakhbar, M., & Youssef, A. A. (2025). Lake iriqui’s remarkable revival: Field observations and a google earth engine analysis of its recovery after over half a century of desiccation. Land, 14(1). https://doi.org/10.3390/land14010104
CR  - Pang, H., Wang, X., Hou, R., You, W., Bian, Z., & Sang, G. (2023). Multiwater index synergistic monitoring of typical wetland water bodies in the arid regions of west-central Ningxia over 30 years. Water, 15(1). https://doi.org/10.3390/w15010020
CR  - Pham-Duc, B., Prigent, C., & Aires, F. (2017). Surface Water Monitoring within Cambodia and the Vietnamese Mekong delta over a year, with Sentinel-1 SAR observations. Water, 9(6). https://doi.org/10.3390/w9060366
CR  - Priyanka, N, S., Lal, S., Nalini, J., Reddy, C. S., & Dell’Acqua, F. (2023). DPPNet: An efficient and robust deep learning network for land cover segmentation from high-resolution satellite images. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(1), 128–139. https://doi.org/10.1109/TETCI.2022.3182414
CR  - Šćepanović, S., Antropov, O., Laurila, P., Rauste, Y., Ignatenko, V., & Praks, J. (2021). Wide-area land cover mapping with Sentinel-1 imagery using deep learning semantic segmentation models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10357–10374. https://doi.org/10.1109/JSTARS.2021.3116094
CR  - Shabbir, A., Ali, N., Ahmed, J., Zafar, B., Rasheed, A., Sajid, M., Ahmed, A., & Dar, S. H. (2021). Satellite and scene image classification based on transfer learning and fine tuning of resnet50. Mathematical Problems in Engineering, 2021(1), 5843816. https://doi.org/https://doi.org/10.1155/2021/5843816
CR  - Solórzano, J. V, Mas, J. F., Gao, Y., & Gallardo-Cruz, J. A. (2021). Land use land cover classification with U-Net: Advantages of combining Sentinel-1 and Sentinel-2 imagery. Remote Sensing, 13(18). https://doi.org/10.3390/rs13183600
CR  - Uzun, M. (2024). Analysis of Manyas lake surface area and shoreline change over various periods with DSAS tool. Turkish Journal of Remote Sensing, 6(1), 35–56. https://doi.org/10.51489/tuzal.1443490
CR  - Xie, Y., Zeng, H., Yang, K., Yuan, Q., & Yang, C. (2023). Water-body detection in Sentinel-1 SAR images with DK-CO network. Electronics, 12(14). https://doi.org/10.3390/electronics12143163
CR  - Yao, P., Fan, H., & Wu, Q. (2025). Optimal drought index selection for soil moisture monitoring at multiple depths in China’s agricultural regions. Agriculture, 15(4). https://doi.org/10.3390/agriculture15040423
CR  - Yilmaz, O. (2023). Monitoring water surfaces by remote sensing: The Case of Manyas Kusgolu, Ulubat, and Iznik lakes in Türkiye. International Research in Engineering Sciences, 52–66. https://doi.org/10.5281/zenodo.7744436
CR  - Zhang, W., Hu, B., & Brown, G. S. (2020). Automatic surface water mapping using polarimetric SAR data for long-term change detection. Water, 12(3). https://doi.org/10.3390/w12030872
CR  - Zhao, S., Tu, K., Ye, S., Tang, H., Hu, Y., & Xie, C. (2023). land use and land cover classification meets deep learning: A review. Sensors, 23(21). https://doi.org/10.3390/s23218966
CR  - Zhao, X., Zhang, J., Tian, J., Zhuo, L., & Zhang, J. (2020). Residual dense network based on channel-spatial attention for the scene classification of a high-resolution remote sensing image. Remote Sensing, 12(11). https://doi.org/10.3390/rs12111887
UR  - https://doi.org/10.51489/tuzal.1647078
L1  - https://dergipark.org.tr/en/download/article-file/4641946
ER  -