Combining high-resolution satellite images and altimetry to estimate the volume of small lakes
Bibliographic record
Abstract
Abstract. This study presents an approach to determine the volume of water in small lakes (<100 ha) by combining satellite altimetry data and high-resolution (HR) images. The lake being studied is located in the south-west of France and is only used for agricultural irrigation purposes. The altimetry satellite data are provided by RA-2 sensor on board Envisat, and the high-resolution images (<10 m) are obtained from optical (Formosat-2) and synthetic aperture radar (SAR) sensors (Terrasar-X and Radarsat-2) satellites. The altimetry data (data are obtained every 35 days) and the HR images (45) have been available since 2003 and 2010, respectively. In situ data (for the water levels and volumes) going back to 2003 have been provided by the manager of the lake. Three independent approaches are developed to estimate the lake volume and its temporal variability. The first two approaches are empirical and use synchronous ground measurements of the water volume and the satellite data. The results demonstrate that altimetry and imagery can be effectively and accurately used to monitor the temporal variations of the lake (R2altimetry = 0.97, RMSEaltimetry = 5.2%, R2imagery = 0.90, and RMSEimagery = 7.4%). The third method combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R2 = 0.99) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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 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".