LAQUA: a LAndsat water QUality retrieval tool for east African lakes
Bibliographic record
Abstract
East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R2 = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R2 = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R2 = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 0.001 |
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".