Water Extent Monitoring Using Sentinel-2 (Case Study: Latyan Dam Reservoir)

Document Type : Case Study

Authors

Department of Water Engineering, Faculty of Agricultural Technology, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.

Abstract

Monitoring the surface area of reservoirs is crucial for effective water resource management, particularly in arid and semi-arid regions where water availability fluctuates significantly. This study utilizes Sentinel-2 imagery to assess water extent variations in the Latyan Dam reservoir in Iran from 2016 to 2024. By applying three well-established water indices—NDWI, MNDWI, & AWEIsh—water surface areas were delineated. Sentinel-2 Level-2A images, preprocessed using the Sen2Cor algorithm, were analyzed in SNAP software, ensuring high accuracy in atmospheric correction and reflectance values. The derived water area was validated using index intersection and union approaches to refine detection accuracy. The reservoir’s surface area showed a clear declining trend, decreasing from 3.3 km² in 2016 (wettest year) to 1.1 km² in 2023 (driest year), corresponding to a shrinkage of about 2.2 km². Throughout 2016–2024, the uncertainty band between the union and intersection of NDWI, MNDWI, and AWEIsh remained relatively narrow, fluctuating between approximately 0.2 and 0.4 km², with slightly lower values in drier years. Applying Otsu thresholding substantially increased the number of uncertain water pixels compared to the fixed global threshold (index ≥ 0), from 3446 to 8250 pixels in 2016 and from 1718 to 7191 pixels in 2023, indicating that the global threshold provides more stable and conservative water detection for long‑term monitoring. This approach provides an efficient, replicable methodology for large-scale water monitoring, supporting sustainable water resource management and policy decisions.

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Main Subjects

References

Alahacoon, N., & Edirisinghe, M. (2022). A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale. Geomatics, Natural Hazards and Risk, 13(1), 762–799. https://doi.org/10.1080/19475705.2022.2044394.
Assiri, M. E., Ali, M. A., Siddiqui, M. H., AlZahrani, A., Alamri, L., Alqahtani, A. M., & Ghulam, A. S. (2024). Remote Sensing Assessment of Water Resources, Vegetation, and Land Surface Temperature in Eastern Saudi Arabia: Identification, Variability, and Trends. Remote Sensing Applications: Society and Environment, 36, 101296. https://doi.org/10.1016/j.rsase.2024.101296.
Chen, C., Qin, Q., Chen, L., Zheng, H., Fa, W., Ghulam, A., & Zhang, C. (2015). Photometric correction and reflectance calculation for lunar images from the Chang’E-1 CCD stereo camera. Journal of the Optical Society of America A, 32(12), 2409-2422. https://doi.org/10.1364/JOSAA.32.002409.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., ... & Bargellini, P. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026.
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8(4), 354. https://doi.org/10.3390/rs8040354.
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.
Francis, O. I., Anornu, G. K., Adjei, K. A., & Martin, E. O. (2021). Development of water surface area–storage capacity relationship using empirical model for Gurara reservoir, Nigeria. Modeling Earth Systems and Environment, 7(3), 2047-2058. https://doi.org/10.1007/s40808-020-00949-w.
Gu, Z., Zhang, Y., & Fan, H. (2021). Mapping inter-and intra-annual dynamics in water surface area of the Tonle Sap Lake with Landsat time-series and water level data. Journal of Hydrology, 601, 126644. https://doi.org/10.1016/j.jhydrol.2021.126644.
Hameed, F., Qureshi, M. A., & Khalil, R. M. Z. (2022). Establishing level-area-volume relationships of Darawat reservoir using time series remote-sensing images. https://doi.org/10.21203/rs.3.rs-1237194/v1.
Koto, A. G., Syahrial, S., & Asruddin, A. (2026, January). The use of water index transformation for surface water detection based on google earth engine. In AIP Conference Proceedings (Vol. 3337, No. 1, p. 030019). AIP Publishing LLC. https://doi.org/10.1063/5.0296477.
Liu, H., Hu, H., Liu, X., Jiang, H., Liu, W., & Yin, X. (2022). A comparison of different water indices and band downscaling methods for water bodies mapping from Sentinel-2 imagery at 10-M resolution. Water, 14(17), 2696. https://doi.org/10.3390/w14172696.
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.
Melkamu, T., Bagyaraja, M., & Adimaw, M. (2022). Spatiotemporal change assessment of Lake Beseka, Ethiopia using time series Landsat images. Hydrospatial Analysis, 6(1), 27-39. https://doi.org/10.21523/gcj3.2022060103.
Mizuochi, H., Hiyama, T., Ohta, T., & Nasahara, K. N. (2014). Evaluation of the surface water distribution in north-central Namibia based on MODIS and AMSR series. Remote Sensing, 6(8), 7660-7682. https://doi.org/10.3390/rs6087660.
Özelkan, E. (2020). Water body detection analysis using NDWI indices derived from landsat-8 OLI. Polish Journal of Environmental Studies, 29(2), 1759-1769. https://doi.org/10.15244/pjoes/110447.
Phankamolsil, Y., & Kositsakulchai, E. (2020). Revision of Vajiralongkorn Dam’s reservoir characteristic curves using NDWI derived from Landsat 8 Data. Environment and Natural Resources Journal, 18(2), 134-145. https://doi.org/10.32526/ennrj.18.2.2020.13.
Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote sensing, 6(5), 4173-4189. https://doi.org/10.3390/rs6054173.
Sekertekin, A. (2021). A survey on global thresholding methods for mapping open water body using Sentinel-2 satellite imagery and normalized difference water index. Archives of Computational Methods in Engineering, 28(3), 1335–1347. https://doi.org/10.1007/s11831-020-09416-2.
Sharma, R. C., Tateishi, R., Hara, K., & Nguyen, L. V. (2015). Developing superfine water index (SWI) for global water cover mapping using MODIS data. Remote Sensing, 7(10), 13807–13841. https://doi.org/10.3390/rs71013807.
Sorkhabi, O. M., Shadmanfar, B., & Kiani, E. (2022). Monitoring of dam reservoir storage with multiple satellite sensors and artificial intelligence. Results in Engineering, 16, 100542. https://doi.org/10.1016/j.rineng.2022.100542.
Ticehurst, C., Teng, J., & Sengupta, A. (2022). Development of a multi-index method based on Landsat reflectance data to map open water in a complex environment. Remote Sensing, 14(5), 1158. https://doi.org/10.3390/rs14051158.
Tunji, L. A. Q., Sempewo, J. I., & Mbatya, W. (2020). Development of a water surface area-storage capacity relationship for Namodope Reservoir, Uganda. Journal of Applied Water Engineering and Research, 8(3), 183-193. https://doi.org/10.1080/23249676.2020.1787243.
Varghese, D., Radulović, M., Stojković, S., & Crnojević, V. (2021). Reviewing the potential of Sentinel-2 in assessing the drought. Remote Sensing, 13(17), 3355. https://doi.org/10.3390/rs13173355.
Wang, X., Xie, S., Zhang, X., Chen, C., Guo, H., Du, J., & Duan, Z. (2018). A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 68, 73–91. https://doi.org/10.1016/j.jag.2018.01.018.
Water Harvesting Research
Volume 9, Issue 1
Issue in Progress
2026
Pages 28-39

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