SWOT lake processing and products
Bibliographic record
Abstract
The Surface Water and Ocean Topography (SWOT) satellite, launched on December 16th 2022, is a CNES and NASA joint project, in collaboration with the Canadian Space Agency (CSA) and the United Kingdom Space Agency (UKSA). SWOT represents a major breakthrough in space altimetry by using a new technical concept based on interferometric synthetic aperture radar (InSAR): in comparison with conventional altimetry, which provides point data along profiles at resolutions of tens or hundreds of kilometres, wide-swath altimetry provides a two-dimensional image with a horizontal resolution of the order of tens or hundreds of meters. Therefore, this mission will significantly improve both offshore and coastal ocean observation, while enabling global measurement also of the water levels (and their variations over time and space) of rivers, lakes and flood zones, with a repeat period of 21 days.Over land, SWOT is planned to survey lakes with a surface area larger than 250 m by 250 m (objective: 100 m by 100 m). To do so, three main products are available to the user community. The pixel cloud (L2_HR_PIXC) product provides longitude, latitude, height, corrections and uncertainties for pixels classified as water and pixels in a buffer zone around these water bodies, as well as in systematically included areas (defined by an a priori water occurrence mask). The product specific to lakes (L2_HR_LakeSP) is computed from the pixel cloud for each water feature observed by SWOT and not assigned to a regular river. It consists of polygon shapefiles, delineating the lake boundary and providing the area and average height of each observed lake. A Prior Lake Database (PLD) allows to link the SWOT observations to known lakes and help monitoring them over time. The L2_HR_LakeAvg product aggregates L2_HR_LakeSP data over a 21-day cycle.The validation of L2_HR_LakeSP water surface elevations is mainly based on existing gauge networks. It is a challenge to obtain reference height data that have an absolute accuracy well below what is required for the SWOT lake products we are validating (10 cm 1-sigma at the lake level). The validation of water surface areas relies on reference water masks obtained mainly from (Very-) High-Resolution optical or radar satellite images (Pléiades, Sentinel-2, Sentinel-1, RCM…), pre-processed so that comparisons can be made at the lake scale.This presentation will first outline the lake processing and the Prior Lake Database. Then examples of products, preliminary accuracy assessments and associated Cal/Val activities will be presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".