Evaluation of Nadir Radar altimeter in lakes with an area less that 100km2
Bibliographic record
Abstract
The dataset corresponds to the results of the evaluation of radar nadir altimeter over lakes, wetlands and reservoirs with area less than 100 km2. These results are represented in terms of the Pearson correlation coefficient, R and the unbiased root mean square error, ubRMSE. Data were generated using in-situ observations collected by governmental institutions in the US, Canada, Brazil, and Argentina, as well as data collected by citizens and managed by the project Lake Observations by Citizen Scientists and Satellites. This dataset contains additional properties associated with the lakes. These are lake area, lake water variability over the timeframe evaluated, number of matching observations between the altimeter and the in-situ measurements, number of altimeter data points, transect length, surface roughness surrounding the lakes, and number of water pixels in the vicinity of the lake (5km buffer). Columns are described in the Metadata_Table S2_section2.csv file This project was founded by NASA Citizen Science for Earth Systems (CSESP), grant number 80NSSC22K1913, managed by G. Guala. Data is part of the paper "Evaluation using in-situ observations from national governments and Citizen Scientists suggests nadir altimeters can accurately measure water level changes regardless of lake area" https://doi.org/10.1080/15481603.2025.2543521 Abstract [From the paper]: Water level and water level changes of lentic water bodies such as lakes, wetlands, and reservoirs are rarely available, despite the relevance of these to maintain ecosystems (e.g., by providing drinking water, food, and cultural activities; and by supporting biodiversity). One alternative is to measure water surface elevation with radar nadir altimeters, whose use has increased as the capabilities of measuring inland water bodies have improved. However, their effectiveness has primarily focused on lakes larger than ~100 km2, and the analysis of contributing factors to obtaining high accuracies is typically performed for a few lakes with special cases. We evaluated water surface elevation change from Sentinel-3 and Jason-3 nadir altimeter missions in lakes and reservoirs using in-situ observations from national governmental institutions and the citizen science network from the project Lake Observations by Citizen Scientists and Satellites. Utilizing the accuracy metrics of Pearson correlation coefficient, R, and the unbiased Root Mean Square Error, ubRMSE, over 27 waterbodies, we found a median ubRMSE of 0.15 m and R of 0.88. We combined the results from 61 additional lakes in a Random Forest algorithm with a permutation of importance to evaluate the impact of 7 factors on the accuracy metrics. Although water surface variability was the most contributing factor to the accuracy, we found differences in the order of variables depending on the evaluation metric used. Our results contribute to show the potential of the nadir altimeters to estimate water surface elevation changes in small lakes, present the advantages of using citizen science monitoring, and the relevance of water surface variability in relation to other factors contributing to high altimeter accuracy. Our implementation of the level of importance algorithm shows potential to systematically evaluate the nadir altimeter validation work available in the literature.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".