Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-2637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding
Géosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
Odin Marc
Géosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
Dominique Remy
Géosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
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Cited
17 citations as recorded by crossref.
- Automatic detection of earthquake triggered landslides using Sentinel-1 SAR imagery based on deep learning L. Chen et al. https://doi.org/10.1080/17538947.2024.2393261
- Size scaling of large landslides from incomplete inventories O. Korup et al. https://doi.org/10.5194/nhess-24-3815-2024
- Review of landslide inventories for Nepal between 2010 and 2021 reveals data gaps in global landslide hotspot E. Harvey et al. https://doi.org/10.1007/s11069-024-07013-1
- Retrieval of Monsoon Landslide Timings With Sentinel‐1 Reveals the Effects of Earthquakes and Extreme Rainfall K. Burrows et al. https://doi.org/10.1029/2023GL104720
- Automated Dating of Recent Landslides Using Sentinel-2 and Sentinel-1 on Google Earth Engine L. Barbera et al. https://doi.org/10.3390/rs17193270
- Innovative Expert-Based Tools for Spatiotemporal Shallow Landslides Mapping: Field Validation of the GOGIRA System and Ex-MAD Framework in Western Greece M. Licata et al. https://doi.org/10.3390/geosciences15070250
- A semi-supervised multi-temporal landslide and flash flood event detection methodology for unexplored regions using massive satellite image time series A. Deijns et al. https://doi.org/10.1016/j.isprsjprs.2024.07.010
- Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application E. Lindsay et al. https://doi.org/10.3390/rs17193313
- A landslide dating framework using a combination of Sentinel-1 SAR and -2 optical imagery S. Fu et al. https://doi.org/10.1016/j.enggeo.2023.107388
- Preface: Estimating and predicting natural hazards and vulnerabilities in the Himalayan region W. Schwanghart et al. https://doi.org/10.5194/nhess-24-3291-2024
- ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory M. Licata et al. https://doi.org/10.1016/j.envsoft.2025.106363
- InSAR-Supported Spatiotemporal Evolution and Prediction of Reservoir Bank Landslide Deformation C. Wang et al. https://doi.org/10.3390/app152212092
- Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. https://doi.org/10.5194/nhess-22-3679-2022
- From Landslides to Debris Floods: Understanding Cascading Hazard Mechanisms in the Nyamwamba Catchment, Mt. Rwenzori A. Mazimwe et al. https://doi.org/10.1016/j.nhres.2025.11.002
- Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery P. Agram et al. https://doi.org/10.3390/rs15071932
- Detection of landslide timing, reactivation and precursory motion during the 2018, Lombok, Indonesia earthquake sequence with Sentinel-1 K. Burrows et al. https://doi.org/10.5194/esurf-13-1039-2025
- Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques V. Yordanov et al. https://doi.org/10.3390/drones7020085
17 citations as recorded by crossref.
- Automatic detection of earthquake triggered landslides using Sentinel-1 SAR imagery based on deep learning L. Chen et al. https://doi.org/10.1080/17538947.2024.2393261
- Size scaling of large landslides from incomplete inventories O. Korup et al. https://doi.org/10.5194/nhess-24-3815-2024
- Review of landslide inventories for Nepal between 2010 and 2021 reveals data gaps in global landslide hotspot E. Harvey et al. https://doi.org/10.1007/s11069-024-07013-1
- Retrieval of Monsoon Landslide Timings With Sentinel‐1 Reveals the Effects of Earthquakes and Extreme Rainfall K. Burrows et al. https://doi.org/10.1029/2023GL104720
- Automated Dating of Recent Landslides Using Sentinel-2 and Sentinel-1 on Google Earth Engine L. Barbera et al. https://doi.org/10.3390/rs17193270
- Innovative Expert-Based Tools for Spatiotemporal Shallow Landslides Mapping: Field Validation of the GOGIRA System and Ex-MAD Framework in Western Greece M. Licata et al. https://doi.org/10.3390/geosciences15070250
- A semi-supervised multi-temporal landslide and flash flood event detection methodology for unexplored regions using massive satellite image time series A. Deijns et al. https://doi.org/10.1016/j.isprsjprs.2024.07.010
- Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application E. Lindsay et al. https://doi.org/10.3390/rs17193313
- A landslide dating framework using a combination of Sentinel-1 SAR and -2 optical imagery S. Fu et al. https://doi.org/10.1016/j.enggeo.2023.107388
- Preface: Estimating and predicting natural hazards and vulnerabilities in the Himalayan region W. Schwanghart et al. https://doi.org/10.5194/nhess-24-3291-2024
- ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory M. Licata et al. https://doi.org/10.1016/j.envsoft.2025.106363
- InSAR-Supported Spatiotemporal Evolution and Prediction of Reservoir Bank Landslide Deformation C. Wang et al. https://doi.org/10.3390/app152212092
- Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. https://doi.org/10.5194/nhess-22-3679-2022
- From Landslides to Debris Floods: Understanding Cascading Hazard Mechanisms in the Nyamwamba Catchment, Mt. Rwenzori A. Mazimwe et al. https://doi.org/10.1016/j.nhres.2025.11.002
- Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery P. Agram et al. https://doi.org/10.3390/rs15071932
- Detection of landslide timing, reactivation and precursory motion during the 2018, Lombok, Indonesia earthquake sequence with Sentinel-1 K. Burrows et al. https://doi.org/10.5194/esurf-13-1039-2025
- Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques V. Yordanov et al. https://doi.org/10.3390/drones7020085
Saved (final revised paper)
Latest update: 11 Jun 2026
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.
The locations of triggered landslides following a rainfall event can be identified in optical...
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