DEM Elevation Uncertainty due to Sentinel-2 Water Mask Resolution
Bibliographic record
Abstract
Description This repository contains the datasets and intermediate products used to quantify the vertical uncertainty introduced by the 10 m spatial resolution of the Sentinel-2 water mask in the DEM-based estimation of water surface elevation (WSE). Because the indirect WSE validation relies on NDWI-derived water masks, the finite 10 m pixel size of Sentinel-2 can shift the detected shoreline position horizontally, especially in steep, confined valleys. To evaluate how such shifts could affect DEM-derived elevations, we quantified the vertical sensitivity of the DEM to a hypothetical ±10 m displacement of the water mask. A 1 m-resolution cross-valley slope raster (in percent) was generated from the DEM. For each date, the Sentinel-2 NDWI water mask was converted to vector format, and the shoreline polygon boundaries were extracted and sampled as points. At each shoreline point, the local slope value was extracted from the slope raster. Vertical sensitivity was then computed as: Δz = slope % / 10 which represents the change in elevation (in meters) associated with a 10 m horizontal shift, equivalent to one Sentinel-2 pixel. The resulting Δz values describe the point-wise DEM elevation uncertainty attributable exclusively to the spatial resolution of the Sentinel-2 water mask. Summary statistics (minimum, maximum, mean, median, and standard deviation) were computed for each date to characterize uncertainty along the study reach. Contents 1. Excel files (one per date) Contain point shoreline samples with the following fields: `raste_val` : Local slope at shoreline (percent) `delta_z` : Estimated vertical sensitivity (slope% / 10) Files included: `Slope_values_delaz_2024-07-11.xlsx` `Slope_values_delaz_2024-08-12.xlsx` `Slope_values_delaz_2024-09-09.xlsx` 2. Raster file Slope raster (percent) derived from the 1 m LiDAR DEM, accessed via LiDAR Download Portal 3. Vector files Shoreline points shapefile, including extracted slope and Δz values for each date NDWI-derived water mask raster used to delineate shoreline boundaries Purpose These datasets support the uncertainty analysis presented in the manuscript and allow reproducibility of the DEM-based WSE sensitivity assessment relative to Sentinel-2 water-mask resolution.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".