A novel algorithm of cloud detection for water quality studies using 250 m downscaled MODIS imagery
Bibliographic record
Abstract
This study is part of a project aimed at developing an automated algorithm for algal bloom detection and quantification in inland water bodies using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. An important step is to adequately detect and exclude clouds and haze because their presence affects chlorophyll-a (chl-a) estimations. Currently available cloud masking products appear to be ineffective in turbid coastal waters. The purpose of this study is to develop a cloud masking algorithm based on a probabilistic algorithm (Linear Discriminant Analysis) and designed for water bodies by using MODIS images downscaled at a 250 m spatial resolution (MODIS-D-250). Confusion matrix shows that the new cloud mask algorithm yields very satisfactory results, enabling water classification for heavy turbid conditions with a mean kappa coefficient (κ) of 0.993 and a 95% confidence interval ranging from 0.990 to 0.997. The model also shows a very low commission error (sensitive to the presence of haze), which is essential for accurate water quality monitoring, knowing that the presence of clouds/haze/aerosols leads to major issues in the estimation of water quality parameters. The cloud mask model applied on MODIS-D-250 images improves the sensitivity to haze and the classification of turbid waters located at the edge of urban areas better than the operational MODIS products, and it clearly shows an improvement of the spatial resolution (250 m spatial resolution) compared to other cloud mask algorithms (500 m or 1 km spatial resolution), leading to an increase in exploitable data for water quality studies.
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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 | 0.001 | 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) | 0.001 | 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".