Monitoring reservoir water elevation changes using Jason-2/3 altimetry satellite missions: exploring the capabilities of JASTER (Jason-2/3 Altimetry Stand-Alone Tool for Enhanced Research)
Bibliographic record
Abstract
In the absence of in-situ data, satellite radar altimetry, critical for studies requiring water elevation data, faces challenges in outlier removal over reservoirs, influenced by surrounding land. We present JASTER (Jason Altimetry Stand-Alone Tool for Enhanced Research), an open-source, fully automated tool processing Jason-2/3 altimetry data for water elevation time series generation over a user-defined inland water body. JASTER uses two outlier removal approaches. The first method employs interquartile range (IQR)-based filtering and K-means clustering. The second method incorporates water occurrence (WO) and Digital Elevation Model (DEM)-derived elevation thresholds. The Hampel filter embedded in JASTER further removes non-physical peaks in the time series caused by signal noise. We validated JASTER’s capabilities using 44 Jason altimeter crossings over 37 water bodies in the US and Canada. We assessed the significance of applying water occurrence and elevation thresholds, and investigated how the Hampel filter parameters and satellite crossing lengths affect accuracy. In all case studies, the Hampel filter significantly increased the consistency between JASTER-derived water elevations and in-situ data. Moreover, the DEM + WO-based method outperformed the IQR-based outlier removal approach in some cases where the greater number of altimeter footprints belonged to land instead of water.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".