Identification of Water Inundation-Prone Areas in Semarang Regency Using Landsat Frequency Analysis and Google Earth Engine (2020-2025)

Sofy Sugiarto

Sambung Facebook X Threads LinkedIn Mendeley WhatsApp Telegram Copy Link

Keywords:

Abstract

Background: Water inundation is a recurring environmental hazard in Semarang Regency that frequently affects settlements, agricultural land, and infrastructure. Mapping inundation recurrence is essential for improving flood risk management and supporting spatial planning. The availability of cloud-based geospatial platforms such as Google Earth Engine (GEE) enables efficient multitemporal analysis of satellite imagery for monitoring inundation dynamics.
Aims: This study aims to identify and classify water inundation-prone areas in Semarang Regency by analyzing the spatial distribution and recurrence of inundation using Landsat observations acquired from 2020 to 2025.
Methods: Landsat 8 and Landsat 9 surface reflectance imagery, the administrative boundary of Semarang Regency, and SRTM Digital Elevation Model (DEM) data were processed in the Google Earth Engine environment. The Modified Normalized Difference Water Index (MNDWI) was calculated from the green and shortwave infrared bands to produce annual water masks. Pixel-based inundation frequency was summarized over six years and classified into vulnerability categories.
Results: Annual inundation extent fluctuated between 8.63 km² in 2020 and a maximum of 23.77 km² in 2021, before decreasing and increasing again to 15.16 km² in 2025. Recurrently inundated areas were mainly concentrated around Rawa Pening and surrounding lowlands characterized by flat to gently sloping terrain and dominated by agricultural and settlement land cover.
Conclusion: The proposed framework effectively integrates Landsat-derived MNDWI and Google Earth Engine to identify recurrent inundation patterns and produce spatially explicit vulnerability maps. The results provide valuable information for flood risk assessment, land-use planning, and disaster mitigation in Semarang Regency
References
1. Badan Nasional Penanggulangan Bencana. (2025). Data informasi bencana Indonesia (DIBI). https://dibi.bnpb.go.id
2. Badan Penanggulangan Bencana Daerah Kabupaten Semarang. (2025). Informasi kejadian bencana Kabupaten Semarang. https://bpbd.semarangkab.go.id
3. Badan Pusat Statistik Kabupaten Semarang. (2025). Kabupaten Semarang dalam angka 2025. https://semarangkab.bps.go.id
4. Brown, C. F., Brumby, S. P., Guzder-Williams, B., et al. (2022). Dynamic World, near real-time global 10 m land use land cover mapping. Scientific Data, 9, Article 251. https://doi.org/10.1038/s41597-022-01307-4
5. Buchhorn, M., Smets, B., Bertels, L., et al. (2020). Copernicus Global Land Service: Land cover 100 m: Collection 3: Epoch 2019: Globe. Remote Sensing, 12(6), Article 1044. https://doi.org/10.3390/rs12061044
6. Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903. https://doi.org/10.1016/j.rse.2009.01.007
7. Cui, J., Guo, Y., Xu, Q., Li, D., Chen, W., Shi, L., Ji, G., & Li, L. (2023). Extraction of information on the flooding extent of agricultural land in Henan Province based on multi-source remote sensing images and Google Earth Engine. Agronomy, 13(2), Article 355. https://doi.org/10.3390/agronomy13020355
8. DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., & Lang, M. W. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, Article 111664. https://doi.org/10.1016/j.rse.2020.111664
9. Donchyts, G., Baart, F., Winsemius, H. C., Gorelick, N., Kwadijk, J., & van de Giesen, N. (2016). Earth’s surface water change over the past 30 years. Nature Climate Change, 6(9), 810–813. https://doi.org/10.1038/nclimate3111
10. Farr, T. G., Rosen, P. A., Caro, E., et al. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), Article RG2004. https://doi.org/10.1029/2005RG000183
11. Fariz, T. R., Suhardono, S., Sultan, H., Rahmawati, D., & Arifah, E. Z. (2022). Land cover mapping in Lake Rawa Pening using Landsat 9 imagery and Google Earth Engine. Journal of Environmental and Science Education, 2(1), 1–6.
12. 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
13. 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
14. Hidayah, E., Pranadiarso, T., Halik, G., Indarto, I., Lee, W. K., & Maruf, M. F. (2022). Flood mapping based on open-source remote sensing data using an efficient band combination system. Acta Geographica Slovenica, 62(3), 47–62. https://doi.org/10.3986/AGS.10598
15. 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
16. 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
17. Kuenzer, C., Guo, H., Huth, J., Leinenkugel, P., Li, X., & Dech, S. (2013). Flood mapping and flood dynamics of the Mekong Delta: ENVISAT-ASAR-WSM based time series analyses. Remote Sensing, 5(2), 687–715. https://doi.org/10.3390/rs5020687
18. Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), Article 1509. https://doi.org/10.3390/rs10101509
19. Markert, K. N., Chishtie, F., Anderson, E. R., et al. (2020). Comparing Sentinel-1 surface water mapping algorithms and radiometric terrain correction processing in Southeast Asia utilizing Google Earth Engine. Remote Sensing, 12(15), Article 2469. https://doi.org/10.3390/rs12152469
20. Martinis, S., Twele, A., & Voigt, S. (2009). Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Natural Hazards and Earth System Sciences, 9(2), 303–314. https://doi.org/10.5194/nhess-9-303-2009
21. 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
22. Mehmood, H., Conway, C., & Perera, D. (2021). Mapping of flood areas using Landsat with Google Earth Engine cloud platform. Atmosphere, 12(7), Article 866. https://doi.org/10.3390/atmos12070866
23. Melesse, A. M., Weng, Q., Thenkabail, P. S., & Senay, G. B. (2007). Remote sensing sensors and applications in environmental resources mapping and modelling. Sensors, 7(12), 3209–3241. https://doi.org/10.3390/s7123209
24. Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Assessment of economic flood damage. Natural Hazards and Earth System Sciences, 10(8), 1697–1724. 146 Review 2.docx
25. Mueller, N., Lewis, A., Roberts, D., et al. (2016). Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174, 341–352. https://doi.org/10.1016/j.rse.2015.11.003
26. 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
27. Pusat Krisis Kesehatan Kementerian Kesehatan Republik Indonesia. (2025). Informasi kejadian krisis kesehatan akibat banjir. https://pusatkrisis.kemkes.go.id
28. Roy, D. P., Wulder, M. A., Loveland, T. R., 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
29. Schumann, G. J.-P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts A/B/C, 83–84, 84–95. https://doi.org/10.1016/j.pce.2015.05.002
30. Sy, H. M., Luu, C., Bui, Q. D., Ha, H., & Nguyen, D. Q. (2023). Urban flood risk assessment using Sentinel-1 on the Google Earth Engine: A case study in Thai Nguyen city, Vietnam. Remote Sensing Applications: Society and Environment, 31, Article 100987. https://doi.org/10.1016/j.rsase.2023.100987
31. Tarboton, D. G. (1997). A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research, 33(2), 309–319. https://doi.org/10.1029/96WR03137
32. Tellman, B., Sullivan, J. A., Kuhn, C., et al. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80–86. https://doi.org/10.1038/s41586-021-03695-w
33. Twele, A., Cao, W., Plank, S., & Martinis, S. (2016). Sentinel-1-based flood mapping: A fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1192304
34. U.S. Geological Survey. (2025). Landsat satellite missions. https://www.usgs.gov/landsat-missions/landsat-satellite-missions
35. Vanama, V. S. K., Mandal, D., & Rao, Y. S. (2020). GEE4FLOOD: Rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform. Journal of Applied Remote Sensing, 14(3), Article 034505. https://doi.org/10.1117/1.JRS.14.034505
36. Wulder, M. A., Loveland, T. R., Roy, D. P., 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
37. Wulder, M. A., Roy, D. P., Radeloff, V. C., et al. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, Article 113195. https://doi.org/10.1016/j.rse.2022.113195
38. 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
39. Yamazaki, D., Ikeshima, D., Tawatari, R., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874
40. Zanaga, M., Van De Kerchove, R., De Keersmaecker, W., et al. (2021). ESA WorldCover 10 m 2020 v100 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5571936
41. 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

How to Cite

Sugiarto, S. (2026). Identification of Water Inundation-Prone Areas in Semarang Regency Using Landsat Frequency Analysis and Google Earth Engine (2020-2025). Journal of Earth and Planetary System Science, 1(1), 31–51. Retrieved from https://journal.gomit.id/jepss/article/view/146

Research Articles | Vol. 1 Issue 1 (2026) | Published in Aug 19, 2026

Identification of Water Inundation-Prone Areas in Semarang Regency Using Landsat Frequency Analysis and Google Earth Engine (2020-2025)

Abstract

References

How to Cite

Similar Articles