Assessing the Characteristic of Bands Combination in Log Ratio Change Detection Using SAR Imagery

Authors

  • Agus Dwi Hartanto Universitas Sriwijaya Indonesia
  • Dwi Setyawan Universitas Sriwijaya Indonesia
  • Ferdinand Hukama Taqwa Universitas Sriwijaya Indonesia

DOI:

https://doi.org/10.22135/sje.2025.10.3.135-142

Abstract

Log ratio is one of the change detection techniques often used in SAR image-based flood inundation analysis where the differences in characteristics between its polarizations are expected to complement each other and provide optimal predictions. This research aimed to identify the characteristics of the output generated from various potential combinations utilizing log ratio change detection. The study utilized Sentinel-1 GRD IW dual polarization mode before and during the flood event  as its main datasource. Briefly, the data processing consists of preprocessing, collocation, and change detection, which were subsequently followed by analysis and evaluation. The analysis results indicated a highly significant difference in characteristics among the four outputs of log ratio change, where the combinations of VH1/VH2 and VV1/VV2 detected much smaller changes compared to the combinations of VV1/VH2 and VH1/VV2. The VV1/VH2 combination acts as a counterpoint to the VH1/VV2 combination, as the changes identified in VV1/VH2 showed a tendency towards positive values, whereas the opposite is true for VH1/VV2. The evaluation results show that the highest frequency of errors in detecting changes sequentially was observed in the combinations of VH1/VV2, VV1/VH2, VV1/VV2, and VH1/VH2.

Keywords:

Flood, Synthetic Aperture Radar, Change Detection

Author Biographies

  • Agus Dwi Hartanto, Universitas Sriwijaya
    Postgraduate Student of Environmental Management
  • Dwi Setyawan, Universitas Sriwijaya
    Lecturer of Soil Science Department, Faculty of Agriculture
  • Ferdinand Hukama Taqwa, Universitas Sriwijaya
    Lecturer of Aquaculture Department, Faculty of Agriculture

How to Cite

Hartanto, A. D., Setyawan, D., & Taqwa, F. H. (2025). Assessing the Characteristic of Bands Combination in Log Ratio Change Detection Using SAR Imagery. Sriwijaya Journal of Environment, 10(3), 135–142. https://doi.org/10.22135/sje.2025.10.3.135-142
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References

J. Zhao, Z. Xiong, and X. X. Zhu, “UrbanSARFloods: Sentinel-1 SLC-based benchmark dataset for urban and open-area flood mapping,†in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Jun. 2024, pp. 419–429. doi: 10.1109/CVPRW63382.2024.00047.

Z. Wang, C. Zhang, and P. M. Atkinson, “Combining SAR images with land cover products for rapid urban flood mapping,†Front. Environ. Sci., no. October, pp. 1–12, 2022, doi: 10.3389/fenvs.2022.973192.

M. Siddique and T. Ahmed, “CCD-Conv1D: A deep learning based coherent change detection technique to monitor and forecast floods using Sentinel-1 images,†Remote Sens. Appl. Soc. Environ., vol. 37, pp. 1–17, 2025, doi: 10.1016/j.rsase.2024.101440.

D. Amitrano, G. Di Martino, A. Di Simone, and P. Imperatore, “Flood detection with SAR : A review of techniques and datasets,†Remote Sens., vol. 16, no. 656, pp. 1–38, 2024, doi: https://doi.org/10.3390/rs16040656.

L. Selmi, “Flood mapping using the Sentinel-1 imagery and the ESA SNAP S1 Toolbox,†2021.

F. Filipponi, “Sentinel-1 GRD preprocessing workflow,†in 3rd International Electronic Conference on Remote Sensing, MDPI, 2019, p. 4. doi: 10.3390/ecrs-3-06201.

A. Braun, Sentinel-1 toolbox time-series analysis with Sentinel-1. SKYWATCH ESA, 2021.

J. Lu and L. Veci, Microwave toolbox change detection with Sentinel-1 Sentinel GRD product. SKYWATCH ESA, 2025.

G. Schumann, L. Giustarini, A. Tarpanelli, B. Jarihani, and S. Martinis, “Flood modeling and prediction using earth observation data,†Surv. Geophys., vol. 44, no. 5, pp. 1553–1578, 2023, doi: 10.1007/s10712-022-09751-y.

F. Lang et al., “Flood mapping of Synthetic Aperture Radar ( SAR ) imagery based on semi-automatic thresholding and change detection,†Remote Sens., vol. 16, no. 2763, pp. 1–18, 2024, doi: https://doi.org/10.3390/ rs16152763.

J. Slesinski, D. Wierzbicki, and M. Kedzierski, “Application of multitemporal change detection in radar satellite imagery using REACTIV-based method for geospatial intelligence,†Sensors, vol. 32, no. 4922, pp. 1–26, 2023, doi: https://doi.org/10.3390/ s23104922.

T. A. Gasica, F. Bioresita, and A. Murtiyoso, “Identification of temporary surface water using Sentinel-1 SAR data, case study: Sentani flash flooding, Indonesia,†Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLIII-B3-2, pp. 55–59, 2020, doi: https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-55-2020.

I. Prasasti, I. Carolita, A. E. Ramdani, and I. Risdiyanto, “The study of ALOS PALSAR data application for soil moisture estimation,†vol. 9, no. 2, pp. 102–113, 2012.

N. Bhogapurapu, S. Dey, S. Homayouni, A. Bhattacharya, and Y. S. Rao, “Field-scale soil moisture estimation using sentinel-1 GRD SAR data,†Adv. Sp. Res., vol. 70, pp. 3845–3858, 2022, doi: https://doi.org/10.1016/j.asr.2022.03.019.

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Published

12/16/2025

Issue

Vol. 10 No. 3 (2025): ENVIRONMENTAL ENGINEERING

Section

Articles

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