Estimation of Chlorophyll-a from Case-2 Inland Waters: Comparing Two Analytical Algorithms
Bibliographic record
Abstract
The paper draws on two different reflective band-ratio algorithms, namely the Maximum Chlorophyll-a Index (MCI) and New Three Band Algorithm (N3B) to estimate Chlorophyll-a (Chl-a) concentrations from Landsat-8 images and spectrometric water samples. Band tuning procedures was performed to find optimal peak wavelengths suitable for the estimation of Chl-a from Landsat-8 satellite image and spectrometric data. Additionally, the MCI and N3B were applied on both in-situ and Landsat-8 data and compared using statistical regression models such as the coefficient of determination (R2), relative mean absolute error (rMAE), and root mean square error (RMSE) to find the best performing algorithm in estimating Chl-a pigments. The results demonstrates that the MCI algorithm performed sensitively in the estimation of Chl-a as compared to the N3B, after data regression. The MCI algorithm obtained a higher R2 of 0.69, with a minimal percentage error (rMAE) of 18.34% and RMSE of 1.85 m-1 when applied on in-situ data. A similar result was obtained when MCI was applied on Landsat-8 data with a higher R2 of 0.75 and a minimal percentage error (rMAE) of 21.29% and an RMSE of 0.97 m-1, respectively. However, the N3B algorithm returned a lower R2 of 0.54 and 0.65 when applied on both in-situ and Landsat-8 data concurrently. The standard errors for MCI were comparatively lower than that of the N3B. Hence, in this study, the MCI algorithm performed better because it has less predictive error. In all, although both algorithms were able to estimate Chl-a pigments, the MCI algorithm is more sensitive in the retrieval of Chl-a concentration from Case-2 inland waters using both in-situ and Landsat-8 data. The results indicate the high potential of analytical algorithms to estimate Chl-a concentration in turbid and eutrophic productive (Case II) waters using satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland aquatic ecosystems.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".