Evaluation of different atmospheric correction methods prior to the estimation of total dissolved solids concentrations from satellite imagery
Bibliographic record
Abstract
Surface water quality is degraded by the presence of numerous types of pollution produced by anthropogenic activities. Hence, surface water quality monitoring and assessment is essential. Conventional approaches of surface water quality monitoring are costly, time-consuming, and labor-intensive. On the other hand, remote sensing is an effective tool for monitoring surface water quality. Satellite images should be atmospherically corrected prior to using them in the estimation of surface water quality parameters (SWQPs). Therefore, The purpose of this study is to evaluate the outputs from several atmospheric correction methods, such as Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), Fast Line of sight Atmospheric Analysis of Hypercubes (FLAASH), and Atmospheric Correction for OLI lite (ACOLITE) in order to estimate total dissolved solids concentrations (TDS) over the study area of the whole province of New Brunswick, Canada. A TDS acquisition model was calibrated and validated in order to obtain TDS concentrations from atmospherically corrected Operational Land Imager (OLI) data. The results obtained from the TDS retrieval model demonstrated that the DOS method provided the most suitable remote sensing reflectance values for coastal blue, red, and shortwave infrared-2 spectral bands with a coefficient of determination (𝐑𝟐=0.76), Root Mean Square Error (RMSE=0.76 mg/l), and significant value (P-value
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".