the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Feb 2026
Evaluating InSAR-derived rates of surface-elevation change along the central U.S. Gulf Coast
Abstract. Interferometric Synthetic Aperture Radar (InSAR) is widely used to monitor surface-elevation change in subsiding coastal regions, but inconsistencies between studies hinder understanding of the processes driving vertical land motion (VLM). Here we compare two recent InSAR datasets from the central U.S. Gulf Coast which yield similar mean rates (−2.8 ± 2.8 and −3.3 ± 1.8 mm yr⁻¹) but show negligible spatial correlation (R² = 0.05), except in medium to highly developed urban areas (R² > 0.5). Using 41 Global Navigation Satellite System records from adjacent Pleistocene uplands with minimal shallow subsidence and sediment accretion, we find a median VLM of −1.2 mm yr⁻¹, largely driven by glacial isostatic adjustment that is higher than previously believed. InSAR data exhibit larger uncertainties and are presently unable to capture this rate. Given the struggles of InSAR in vegetated landscapes, we recommend that vertical velocities below 5 mm yr⁻¹ are interpreted with utmost caution.
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CC1: 'Comment on egusphere-2026-824: Evaluating InSAR derived rates of surface elevation change along the central US Gulf Coast.', Falk Amelung, 11 Mar 2026
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AC1: 'Reply on CC1', Guandong Li, 17 Mar 2026
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We appreciate the comments by Falk Amelung which echo several of the concerns that we raised in our manuscript. We agree that comparisons between InSAR solutions derived from different processing approaches should be carried out with utmost caution, particularly in challenging settings such as densely vegetated environments. This is the main take-away of our paper.
Products such as OPERA and TRE typically apply conservative coherence thresholds and filtering, which prioritize high-confidence pixels but often result in limited spatial coverage in low-coherence areas. In contrast, the approaches used for the two datasets evaluated in our study aim to recover signals in these environments, potentially extending coverage into areas where conventional methods tend to discard pixels. The algorithm used in W24 will be released as a software package hopefully this summer, which will improve the transparency of this approach and allow others to reproduce the results.
A direct comparison with OPERA is not straightforward because the current OPERA products are provided along the Line-Of-Sight direction rather than in the vertical direction. Additionally, OPERA measures relative change between pixels that is not tied to the same vertical reference frame as used in our study. The TRE vertical dataset may be more comparable; however, its spatial coverage in the vegetated coastal zone of our study area is even more limited.
Instead, we use GNSS observations as an independent benchmark. GNSS provides well-established vertical land motion estimates, with long-term velocity uncertainties typically below 1 mm yr⁻¹ at most of the permanent GNSS station. This is confirmed by our finding that subsidence due to glacial isostatic adjustment of just over 1 mm yr-1 can be detected by GNSS but not by InSAR. Thus, we use GNSS observations to evaluate the accuracy of the InSAR results rather than comparing different InSAR solutions that tend to have uncertainties larger than 1 mm yr-1.
We agree that systematic intercomparisons among different InSAR processing frameworks (e.g., OPERA, TRE, plus newer approaches) would be valuable for the community. However, such an analysis is beyond the scope of the present study. We will revise the manuscript to clarify these points.
Citation: https://doi.org/10.5194/egusphere-2026-824-AC1 -
CC2: 'Reply on AC1', Falk Amelung, 17 Mar 2026
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Thank you for your explanation. I don't follow why the comparison with the OPERA products is difficult. You would of course use your radar line-of-sight displacements for the comparison, not the vertical displacements. If the W24 data and OPERA data agree at "difficult" InSAR locations like Grand Isles, Port Fourchon and Venice we know that the W24 InSAR processing is reliable. If they don't agree that does not mean the W24 data are wrong. The OPERA products can also have issues. But it would provide some indication on how much to put into the final results. The integration with the GNSS is a different topic which introduces additional uncertainties.
Citation: https://doi.org/10.5194/egusphere-2026-824-CC2
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CC2: 'Reply on AC1', Falk Amelung, 17 Mar 2026
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AC1: 'Reply on CC1', Guandong Li, 17 Mar 2026
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I am short in time and can't read the entire paper but since this is an important topic I thought I add my 2 cents. Please ignore if this is addressed in the paper.
I don't think it makes much sense to compare data products from two analysis approaches that are not (yet?) well accepted by the community. Below are two screenshots from two accepted approaches, (1) the OPERA displacement data portal at the ASF (displacement.asf.alaska.edu) and (2) the TRE-processed data (2016-2023) available from NOAA (we have ingested them into our data portal for visualization). These maps don't show much data near the coast. This is expected as InSAR does not work in this area, with a few exceptions. That the two studies which are compared in the paper have data in no-data areas is suspicious. I hope there are explanations for this in the original papers -- my apologies that I did not read them in detail. Another possible explanation for discrepancies is that the authors were too generous with threshold values used to separate valid from invalid pixels. In contrast, both TRE and OPERA are conservative with threshold selection.
A useful approach would be (1) a comparison between the two reference datasets, and (2) pointing out differences between both O24 and W24 with respect to the reference datasets. If O24 or W24 have credible explanations this is important to know and will lead to the adaptation of their approaches by the community. If there are no satisfactory explanations, the honorable thing for these authors to do would be to qualify their results with post-publication public letter, or papers retraction.
I recently reviewed to papers which presented InSAR timeseries using new analysis approaches which are not (yet) well established. To be publishable I requested a comparison with well established data products and explanations for the differences -- if here are.
In the present case another complication is that data are referred to GNSS data. I hope/trust that this is not an independent source for discrepancies between datasets.