Assessing water clarity status and long-term trends in North America’s largest lakes using ESA’s Ocean Colour Climate Change Initiative (OC-CCI) products
Bibliographic record
Abstract
Water clarity, as measured by Secchi disk depth (ZSD) or diffuse attenuation (Kd), is an important indicator of a lake's ecosystem state and can be reliably retrieved using satellite remote sensing. By combining data from multiple satellite missions, the European Space Agency's Ocean Colour Climate Change Initiative (OC-CCI) aims to deliver stable, long-term, satellite data products suitable for trend assessments. Here we demonstrate the value of OC-CCI products for reporting on water clarity status and long-term trends in North America's largest lakes. Extensive matchups between the OC-CCI Kd at 490 nm (Kd490) and in situ ZSD observations spanning 25 years enabled robust multi-lake validation of ZSD retrievals over a wide range of water clarity conditions (R2 = 0.9, MAPE = 29.6 %, BIAS = 6.6 %, N = 4297) providing a transferable model for large-scale mapping of inland water clarity. Significant differences in ZSD retrieval uncertainty were observed between years, missions, and specific periods marking changes in the sensor datasets contributing to the OC-CCI products. Bias-correction of the OC-CCI Kd490 provided confidence in the assumption of seamless continuity in this multi-mission dataset, thereby allowing long-term time-series analyses. Seasonal, inter-annual and inter-decadal variability and trends in lake-wide average ZSD were subsequently evaluated for nine large lakes across Canada and the U.S. over the 1998–2023 period, capturing the timing and magnitude of significant shifts in water clarity conditions. Observations agree well with documented periods of ecosystem change in response to the cumulative impacts from harmful algal blooms, nutrient status, invasive species, and hydrological events.
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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 | 0.001 |
| 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 | 0.001 |
| 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".