Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye

Talha Taşkanat

doi:10.53030/tjags.2011508

Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye

Abstract

Satellite-derived reservoir area is useful where continuous in situ records are unavailable, but reported extent can depend on source product, compositing, threshold, and post-processing. This study compared five water-mapping rules for Yamula Dam Lake, Türkiye, using 16 spring and late-summer periods from 2017 to 2024. The rules comprised fixed-threshold and Otsu-thresholded Modified Normalized Difference Water Index composites from Sentinel-2 Level-2A reflectance and Dynamic World median water probabilities thresholded at 0.50, 0.60, and 0.70. Outputs were evaluated over a common final valid-pixel domain and subjected to the same reservoir-component filter, although the source products retained distinct observation support. Mapped area ranged from 49.01 to 76.45 km². The mean within-period range was 5.67 km² (9.34%), the mean absolute pairwise difference was 2.83 km², and the mean Jaccard index was 0.943. The intersection of all five rules averaged 56.50 km², whereas the union-minus-intersection envelope averaged 7.45 km². Sensitivity tests showed material threshold dependence and smaller effects from resolution, resampling, and component filtering. Temporal slopes were uncertain and differed by rule and season. The findings support explicit reporting of the complete mapping workflow and sensitivity range.

Keywords

Ethical Statement

In the study, the author declares that there is no violation of research and publication ethics.

Thanks

This study received no external funding.

References

Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4
Dervisoglu, A. (2022). Investigation of Long and Short-Term Water Surface Area Changes in Coastal Ramsar Sites in Turkey with Google Earth Engine. ISPRS International Journal of Geo-Information, 11(1), 46. https://doi.org/10.3390/ijgi11010046
Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., & Van de Giesen, N. (2016). A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia. Remote Sensing, 8(5), 386. https://doi.org/10.3390/rs8050386
Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Gümüş, M. G., & Kaynak, T. (2026). Identifying priority zones for rainwater harvesting to support sustainable water management in arid and semi-arid regions. Journal of Environmental Management, 404, 129519. https://doi.org/10.1016/j.jenvman.2026.129519
Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research, 18(1), 107–121. https://doi.org/10.1029/WR018i001p00107
Karaman, M., & Özelkan, E. (2022). Comparative assessment of remote sensing–based water dynamic in a dam lake using a combination of Sentinel-2 data and digital elevation model. Environmental Monitoring and Assessment, 194(2), 92. https://doi.org/10.1007/s10661-021-09703-w

Kaynak, T. (2026). A Bayesian-optimized multimodel machine learning framework to overcome subjectivity in floating photovoltaic site selection. Clean Technologies and Environmental Policy, 28(8), 215. https://doi.org/10.1007/s10098-026-03470-3
Kesikoglu, M. H. (2025a). Comparative analysis of R/SWIR, NIR/SWIR, and NDSI classification methods for determination of snow cover change on Erciyes Mountain, Türkiye using Landsat satellite images. Research on Engineering Structures and Materials. https://doi.org/10.17515/resm2025-524ml1110rs
Kesikoglu, M. H. (2025b). Enhancing Snow Detection through Deep Learning: Evaluating CNN Performance Against Machine Learning and Unsupervised Classification Methods. Water Resources Management, 39(12), 6053–6071. https://doi.org/10.1007/s11269-025-04240-4
Kesikoğlu, M. H., Çiçekli, S. Y., & Kaynak, T. (2020). The Identification Of Seasonal Coastline Changes From Landsat 8 Satellite Data Using Artificial Neural Networks And K-Nearest Neighbor. Turkish Journal of Engineering, 4(1), 47–56. https://doi.org/10.31127/tuje.599359
Kesikoğlu, M. H., & Kaynak, T. (2024). Tracking Seasonal Coastal Dynamics of Akyatan Wetland with Landsat 8 image using Object-Based Classification and Regression Tree. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. https://doi.org/10.28948/ngumuh.1559034
Mann, H. B. (1945). Nonparametric Tests Against Trend. Econometrica, 13(3), 245. https://doi.org/10.2307/1907187
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
Reis, L. G. de M., Souza, W. de O., Ribeiro Neto, A., Fragoso, C. R., Ruiz-Armenteros, A. M., Cabral, J. J. da S. P., & Montenegro, S. M. G. L. (2021). Uncertainties Involved in the Use of Thresholds for the Detection of Water Bodies in Multitemporal Analysis from Landsat-8 and Sentinel-2 Images. Sensors, 21(22), 7494. https://doi.org/10.3390/s21227494
Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Taşkanat, T. (2026). Modeling Salinity-Vegetation Interaction in Semi-Arid Areas: A Google Earth Engine Based Comparative Regression Analysis. Türk Tarım ve Doğa Bilimleri Dergisi, 13(2), 397–412. https://doi.org/10.30910/turkjans.1844748
Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, 14(16), 4101. https://doi.org/10.3390/rs14164101
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Xu, P., Tsendbazar, N.-E., Herold, M., de Bruin, S., Koopmans, M., Birch, T., Carter, S., Fritz, S., Lesiv, M., Mazur, E., Pickens, A., Potapov, P., Stolle, F., Tyukavina, A., Van De Kerchove, R., & Zanaga, D. (2024). Comparative validation of recent 10 m-resolution global land cover maps. Remote Sensing of Environment, 311, 114316. https://doi.org/10.1016/j.rse.2024.114316

Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Talha Taşkanat ^*
0000-0001-6273-9298
Türkiye

Publication Date

September 30, 2026

Submission Date

August 5, 2026

Acceptance Date

September 3, 2026

Published in Issue

Year 2026 Volume: 8 Number: 2

DOI

https://doi.org/10.53030/tjags.2011508

IZ

https://izlik.org/JA85DT36MM

Cite

RIS / Bibtex

APA

Taşkanat, T. (2026). Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye. Turkish Journal of Applied Geoinformation Sciences, 8(2), 106-120. https://doi.org/10.53030/tjags.2011508

AMA

1.Taşkanat T. Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye. Turk. J. Appl. Geoinf. Sci. 2026;8(2):106-120. doi:10.53030/tjags.2011508

Chicago

Taşkanat, Talha. 2026. “Workflow-Dependent Variability in Reservoir Surface-Water Area Estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye”. Turkish Journal of Applied Geoinformation Sciences 8 (2): 106-20. https://doi.org/10.53030/tjags.2011508.

EndNote

Taşkanat T (September 1, 2026) Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye. Turkish Journal of Applied Geoinformation Sciences 8 2 106–120.

IEEE

[1]T. Taşkanat, “Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye”, Turk. J. Appl. Geoinf. Sci., vol. 8, no. 2, pp. 106–120, Sept. 2026, doi: 10.53030/tjags.2011508.

ISNAD

Taşkanat, Talha. “Workflow-Dependent Variability in Reservoir Surface-Water Area Estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye”. Turkish Journal of Applied Geoinformation Sciences 8/2 (September 1, 2026): 106-120. https://doi.org/10.53030/tjags.2011508.

JAMA

1.Taşkanat T. Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye. Turk. J. Appl. Geoinf. Sci. 2026;8:106–120.

MLA

Taşkanat, Talha. “Workflow-Dependent Variability in Reservoir Surface-Water Area Estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye”. Turkish Journal of Applied Geoinformation Sciences, vol. 8, no. 2, Sept. 2026, pp. 106-20, doi:10.53030/tjags.2011508.

Vancouver

1.Talha Taşkanat. Workflow-dependent variability in reservoir surface-water area estimates from Sentinel-2 and Dynamic World: Yamula Dam Lake, Türkiye. Turk. J. Appl. Geoinf. Sci. 2026 Sep. 1;8(2):106-20. doi:10.53030/tjags.2011508