Aerosol Optical Depth of MODIS Imagery over Bright Coastal Water: A Regression Technique
Bibliographic record
Abstract
The contribution of sediment and bottom reflectance in the MODIS imagery over bright coastal water contaminate visible and near infrared channels. This contribution leads to the saturation in the ocean color channels and error in the MODIS derived product over that areas. The saturation in the ocean color channels lead to no aerosol optical depth (AOD) retrieved by MODIS algorithm over very turbid areas. The sediment contributions will increase the reflectance in the visible and near infrared channel that is used in the MODIS aerosol algorithm. This area is then masked using sediment masking algorithm. In this paper, a simple method to estimate the AOD over bright coastal water with high sediment and bottom reflectance contributions is proposed. The first seven MODIS solar channels centered at 0.47, 0.55, 0.66, 0.86, 1.24, 1.64 and 2.13 µm, originally designed for remote sensing over land and cloud properties have been utilized in this study. The algorithm is based on the excess reflectance at 0.55, 0.66 and 0.87 µm wavelength using the power law model that can be associated to the presence of sediment. The excess value of the reflectance is discarded, to obtain the atmospheric contribution in the data. Aerosol contribution over bright coastal water areas is then compared with AOD of 0.869 µm using regression technique. This study shows that the AOD and aerosol retrieved are in high correlation with R is greater than 0.90. The AOD map from the applications of the algorithm were constructed and extrapolated for the saturated channels areas. The sample result of AOD map from several MODIS datasets acquired over the Gulf of Martaban is presented.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".