Spatial Assessment of Changes in the Normalized Difference Water Index Between 2015 and 2025

Received: 2026-10-13

Published: 2025-12-10

Abstract

This study assessed spatial changes in the Normalized Difference Water Index (NDWI) between 2015 and 2025 in Angor District, Uzbekistan. Landsat 8 Collection 2 Level-2 imagery was processed in Google Earth Engine using cloud, shadow, snow and radiometric saturation masks. NDWI was calculated from green and near-infrared surface reflectance for individual images, followed by annual median compositing. Spatially aligned rasters were compared using pixel-wise subtraction, descriptive statistics and five change intervals. The analysis covered 603,231 valid comparison pixels. Mean NDWI decreased from −0.300216 in 2015 to −0.310154 in 2025, yielding a mean difference of −0.009937. The standard deviation declined from 0.184392 to 0.153999. Negative differences occurred in 57.28% of pixels, while positive differences occurred in 42.72%. Most pixels (97.69%) fell within −0.2 < ΔNDWI ≤ 0.2, indicating predominantly small index differences. Values exceeding 1 occurred in 0.64% of valid pixels in 2015 and were retained in the reported statistics. The findings indicate a modest overall shift towards more negative NDWI values but do not independently demonstrate a reduction in surface-water extent. Additional quality screening, seasonal analysis and validated water classification are required to establish the hydrological significance of the observed differences.

List of references

  1. Huang, C., Chen, Y., Zhang, S., & Wu, J. (2018). Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics, 56(2), 333–360. https://doi.org/10.1029/2018RG000598

  2. 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, 418–422. https://doi.org/10.1038/nature20584

  3. Donchyts, G., Baart, F., Winsemius, H., Gorelick, N., Kwadijk, J., & van de Giesen, N. (2016). Earth’s surface water change over the past 30 years. Nature Climate Change, 6, 810–813. https://doi.org/10.1038/nclimate3111

  4. Roy, D. P., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001

  5. Wulder, M. A., et al. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147. https://doi.org/10.1016/j.rse.2019.02.015

  6. 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

  7. Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

  8. 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

  9. 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

  10. Ji, L., Zhang, L., & Wylie, B. (2009). Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering & Remote Sensing, 75(11), 1307–1317. https://doi.org/10.14358/PERS.75.11.1307

  11. Jones, J. W. (2019). Improved automated detection of subpixel-scale inundation—Revised Dynamic Surface Water Extent (DSWE) partial surface water tests. Remote Sensing, 11(4), 374. https://doi.org/10.3390/rs11040374

  12. 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

  13. Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028

  14. 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

  15. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015

About the Authors

License

Copyright (c) 2025 Journal of Hydraulic and Environmental Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Spatial Assessment of Changes in the Normalized Difference Water Index Between 2015 and 2025. (2025). Journal of Hydraulic and Environmental Engineering, 3(2). https://doi.org/10.35938/jhee-2025-2-00007 (Original work published 2026)

Most read articles by the same author(s)

Similar Articles

You may also start an advanced similarity search for this article.


ISSN 2181-3949 (Online)