From InSAR‐Derived Subsidence to Relative Sea‐Level Rise—A Call for Rigor
Bibliographic record
Abstract
Abstract Coastal subsidence, the gradual sinking of coastal land, considerably exacerbates the impacts of climate change‐driven sea‐level rise (SLR). While global sea levels rise, land subsidence often increases relative SLR locally. Thiéblemont et al. (2024, https://doi.org/10.1029/2024ef004523 ) reached a remarkable milestone by providing a continental‐scale estimate of vertical land motion (VLM) across European coastal zones by utilizing European Ground Motion Service (EGMS) data, obtained from Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel‐1 satellites. Their findings reveal widespread coastal subsidence, with nearly half of the coastal floodplains, including major cities and ports, subsiding at rates exceeding 1 mm/yr, thereby exacerbating relative SLR. The study emphasizes the critical role of InSAR‐data calibration, indicating that the EGMS geodetic reference frame significantly influences VLM estimates. This study highlights the need for a robust InSAR‐data processing framework to accurately interpret VLM and its relationship to relative SLR. The processing pipeline should ensure internal consistency of SAR data and rigorously assess output accuracy, considering also post‐processing effects. Correct interpretation of results is essential as InSAR satellites measure reflector movement, which may not always align with land surface movement, particularly in urban areas. Ignoring these discrepancies can lead to underestimation of subsidence rates. While InSAR data offers valuable research opportunities, it poses risks of oversimplification and misinterpretation, especially when linked to sea‐level change. We call for standardized processing workflows and cross‐disciplinary collaboration, essential for accurate VLM interpretations, particularly in coastal cities and river deltas, to ultimately enhance the reliability of relative SLR projections and inform effective coastal management strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.182 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".