Predicting and Mapping Daily Lake Surface Topography by Integrating SWOT Swath Observations with Multi-mission Satellite Radar Altimetry
Bibliographic record
Abstract
Large lakes exhibit spatial variations in water levels, leading to distinct lake surface topography. Accurate characterization of lake surface topography enables improved understanding of lake dynamics, hydrological exchanges with surrounding ecosystems, and water balance assessments. Satellite radar altimetry has been widely used in monitoring lake water. Most radar altimetry missions carry a nadir-looking profiling altimeter that measures water level only along their fixed ground tracks, leaving huge data gaps between their ground tracks. The recently launched Surface Water and Ocean Topography (SWOT) mission has produced wide swath 2D elevation measurements at high spatial resolution over the water bodies on a global scale. However, its long repeat cycle of 21 days limits the temporal frequency of observations. In this study, we present a method to map lake surface topography on a near-daily basis by integrating 2D SWOT observations with 1D radar altimetry observations. The method consists of three components. First, the inter-mission biases between each of the four operational radar altimetry missions (i.e., Jason-3, Sentinel-3A, -3B, and Sentinel-6) and SWOT are estimated based on their concurrent and co-located overpasses. Second, the intra-lake spatial pattern of water surface topography is derived by applying an Empirical Orthogonal Function (EOF) to the time series of daily SWOT wide-swath measurements. Third, the SWOT-calibrated profiling altimetry measurements from each radar altimetry mission were then propagated to the entire lake based on the spatial pattern from SWOT observations. Our method has been successfully applied to Lake Ontario, generating near-daily, high-accuracy time series maps of water surface topography.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".