Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2637-2022
https://doi.org/10.5194/nhess-22-2637-2022
Research article
 | 
17 Aug 2022
Research article |  | 17 Aug 2022

Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding

Katy Burrows, Odin Marc, and Dominique Remy

Related authors

Detection of landslide timing, reactivation and precursory motion during the 2018, Lombok, Indonesia earthquake sequence with Sentinel-1
Katy Burrows, David G. Milledge, and Maria Francesca Ferrario
EGUsphere, https://doi.org/10.5194/egusphere-2024-3264,https://doi.org/10.5194/egusphere-2024-3264, 2024
Short summary
Integrating empirical models and satellite radar can improve landslide detection for emergency response
Katy Burrows, David Milledge, Richard J. Walters, and Dino Bellugi
Nat. Hazards Earth Syst. Sci., 21, 2993–3014, https://doi.org/10.5194/nhess-21-2993-2021,https://doi.org/10.5194/nhess-21-2993-2021, 2021
Short summary
A systematic exploration of satellite radar coherence methods for rapid landslide detection
Katy Burrows, Richard J. Walters, David Milledge, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 20, 3197–3214, https://doi.org/10.5194/nhess-20-3197-2020,https://doi.org/10.5194/nhess-20-3197-2020, 2020
Short summary

Related subject area

Landslides and Debris Flows Hazards
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025,https://doi.org/10.5194/nhess-25-709-2025, 2025
Short summary
Identifying unrecognised risks to life from debris flows
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025,https://doi.org/10.5194/nhess-25-647-2025, 2025
Short summary
Predicting the thickness of shallow landslides in Switzerland using machine learning
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025,https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary
Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025,https://doi.org/10.5194/nhess-25-451-2025, 2025
Short summary
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025,https://doi.org/10.5194/nhess-25-183-2025, 2025
Short summary

Cited articles

Aimaiti, Y., Liu, W., Yamazaki, F., and Maruyama, Y.: Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data, Remote Sensing, 11, 2351, https://doi.org/10.3390/rs11202351, 2019. a
Ao, M., Zhang, L., Dong, Y., Su, L., Shi, X., Balz, T., and Liao, M.: Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal with multi-source SAR data, Sci. Rep., 10, 1–12, 2020. a, b
Baghdadi, N., Choker, M., Zribi, M., Hajj, M. E., Paloscia, S., Verhoest, N. E., Lievens, H., Baup, F., and Mattia, F.: A new empirical model for radar scattering from bare soil surfaces, Remote Sensing, 8, 920, https://doi.org/10.3390/rs8110920, 2016. a, b
Ban, Y., Zhang, P., Nascetti, A., Bevington, A. R., and Wulder, M. A.: Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning, Sci. Rep., 10, 1–15, 2020. a, b
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration, J. Geophys. Res.-Ea. Surf., 115, F03013, https://doi.org/10.1029/2009JF001321, 2010. a
Download
Short summary
The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 d globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
Share
Altmetrics
Final-revised paper
Preprint