A Method to Derive Tidal Flat Topography in Nantong, China Using MODIS Data and Tidal Levels
Bibliographic record
Abstract
Multispectral remote sensing data have proven to be useful in deriving tidal flat topography. However, limited satellite observations over a certain period have large uncertainties. In this study, we used MODIS time-series data with a high observational frequency to generate accurate tidal flat topography in Nantong, China based on the relationship between the T_Tide-derived tidal levels and the MODIS-derived inundation frequency map. First, 8-day MOD09Q1 data from 2007 to 2008 were used to perform the land-water classification. Second, 92 land-water maps were stacked to generate the inundation frequency map of the tidal flat. Then, the T_Tide package was applied to calculate the tidal levels at Lvsi Tide Gauge Station. Finally, the inundation frequency map and the tidal levels were integrated to derive the tidal flat topography, which agreed well with the in-situ elevation data (RMSE = 0.40 m, r = 0.89) and the Landsat-based elevation data (RMSE = 0.18 m, r = 0.98). In addition, the derived slopes agreed well with the slopes from the in-situ elevation data (RMSE = 1.00‰, r = 0.85). We highlighted the necessity of using all MODIS data for deriving an accurate tidal flat topography. Our proposed method has a potential to derive tidal flat topography and temporal changes over the past 20 years from MODIS data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".