the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Nov 2025
Monitoring the displacement of large alpine rock slope instabilities with L-band SAR interferometric techniques
Abstract. Large rock slope instabilities develop over long periods and creep slowly over hundreds or thousands of years, until they undergo a 'slow to fast' evolution towards catastrophic collapse. Capturing this transition is key to manage related risks, especially considering ongoing climate change scenarios and human activities, that are expected to strongly influence geohazards. However, this is a challenging task due to the complexity of the underlying processes. Long-term, area-wide monitoring of slope movements is essential to understand landslide dynamics and evolution. Despite being widely used for landslide investigations, C-band SAR interferometry datasets suffer from decorrelation in vegetated areas and fast movements, limiting displacement retrieval in alpine regions. Emerging L-band systems, with reduced temporal decorrelation, can complement higher-frequency data by enabling measurements also in vegetated areas and capturing larger displacements. This work aims at analysing the potential benefits and limitations of L-band SAR interferometry applied to alpine landslide monitoring and at understanding if these data can help in mitigating current shortcomings of C-band SAR interferometry. We explore three different scenarios of large alpine slope instabilities in the European Alps, that threaten important economic and societal assets. We perform site-specific analysis, validation and interpretation of L-band SAR interferometry products derived from ALOS-2 PALSAR-2 and SAOCOM-1 satellite imagery, as well as of terrestrial data acquired by the GAMMA L-band SAR (GLSAR) instrument. Our results highlight the contributions of L-band InSAR products to the practical characterisation and interpretation of large rock slope instabilities and provide important recommendations for the recently launched L-band satellite SAR missions ALOS-4 PALSAR-3 and NISAR, as well as for the future L-band satellite SAR mission ROSE-L.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5347', Anonymous Referee #1, 09 Dec 2025
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RC2: 'Comment on egusphere-2025-5347', Anonymous Referee #2, 26 Jan 2026
This manuscript describes an extensive comparison among DInSAR measurements obtained by C- and L- band satellite data for landslide monitoring in alpine regions. Data acquired by a car-borne SAR sensor are also presented, complementing the information obtained from satellites.
The paper is very clear in its concept and structure. Several experimental results support the conclusions that are clear.
I have some minor comments that I think could improve the manuscript.
a) Table 1-2: it is not clear how to identify Stripmap and ScanSAR data. Please, add this info here or in the corresponding appendix tables.
It would also be useful to indicate the mean look angle, which in mountainous regions could be in some case critical.
b) L312: do you mean 2024-2025? In any case, I would detail the temporal span: October 2024-June 2025.
c) In Fig4a-b, 6, 9, A2, B3, C4 some shadow/black areas are present but no information for their interpretation is provided. Please, explain.
d) Fig.5: I suppose Fig. 5 and Fig.4 shares the same view. However, in the case of Fig. 5, the background map is not useful for identifying the area because it is almost completely covered by the interferogram. Therefore, here, and also in general for all the other maps, please add the geographic coordinates to better localize the study area.
As a further suggestion, in Fig. 5b (and in Fig. 11a as well) please consider to make transparent the areas with no phase information, instead of representing them in black.
e) Fig 5a and Fig. 7: Please, explain why some part of the wrapped interferograms is masked out.
f) Fig 13a,c: SqueezeSAR -> SqueeSAR
g) Fig. 13d: Caption states GNSS pt. 5001 while panel shows GNSS pt. 800. Please, correct.
h) L486-487: probably "when a significant acceleration detected by the MTS was not recorded by ALOS-2 PALSAR-2"?
i) L535: I would mention the possibility to use the Pixel Offset approach, if applicable, that anyway has its own limitations.
j) L536-541: "purely satellite-based analysis". I agree with the main point raised by the authors and I also agree with the authors' final statement at lines 568-570. However, I would mention here that this statement is related to the present satellite landscape. Indeed, future SAR missions acquiring with increased repeat pass and/or from multiple look angles would improve the satellite detection capabilities, even if the use of ground sensors will be probably always required.
In any case, having improved satellite constellations would allow us to optimize in situ monitoring resources.
k) Fig B2-B3: review the figure caption. It should be Val Canaria.
Citation: https://doi.org/10.5194/egusphere-2025-5347-RC2
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The paper presents well-structured research with appropriate methods, results, and scientific soundness. The combined use of different-wavelength SAR, together with car-based SAR, is an important added value. However, some minor revisions are needed to be published, especially for the quality of the figures and some other minor issues that could be improved
Monte mater: EGMS, it could also be possible to also add the dataset 2019 - 2023 (or 2020-2024 if it will be published) to have a long time series for comparison
Brienz/Brinzauls: Maybe it is possible to compare the data for DIC/Lidar used in Manconi et al., 2024, as well?
It is possible to resume in a table for each satellite and orbit the mean azimuth and incidence angle.
A table or some consideration showing the max (theoretical) velocity (along LOS) that can be detected by each satellite (I suppose based on acquisition frequency and wavelength) could help visualise the different satellites' upper limits.
For the PS density calculation, it would be better to show where the area is in which PS is calculated, and to include a land use map to better understand what is classified as forest and non-forest. For the plot, use the same scale for A B and C, at least using log axis Y to evidence the different order of density from C to L band
Line 133: collapse on 27 October 2009 (any reference)
Line 194 : as shown in Fig. 6 is 3 ?
Where possible, add the boundaries of DSGSD and landslide/, especially when interferograms cover the entire area.
In some figures (e.g., 6), the aerial photo appears to have a black pixel; is it a shadow mask? If yes, add to the legend.
Figure 4 and others: Please add scale and coordinates
Figure 8: Please use the same scale of velocity displacement
Figure 12: move at the end of par 6.1