Optical closure in highly absorptive coastal waters: significance of inelastic scattering processes
Bibliographic record
Abstract
In hydrological optics, "optical closure" means consistency between the apparent optical properties (AOPs) determined from radiometric measurements and those derived from radiative transfer modelling based on concurrently measured Inherent optical properties (IOPs) and boundary conditions (sea and sky states). Good optical closure not only provides confidence in the data quality but also informs on the adequacy of the radiative transfer parameterization. Achieving optical closure in highly absorptive coastal waters is challenging due to the low signal-to-noise ratio of radiometric measurements and uncertainties in the measurements of IOPs, namely the spectral absorption and backscattering coefficients. Here, we present an optical closure assessment using a comprehensive set of in situ IOPs acquired in highly absorptive coastal waters optically dominated by chromophoric dissolved organic matter (CDOM). The spectral remote sensing reflectance, Rrs(λ), was modeled using the software HydroLight (HL) with measured IOPs and observed boundary conditions. Corresponding in-water Rrs(λ) was derived from radiometric measurements made with a Compact Optical Profiling System (C-OPS; Biospherical). The assessment revealed that the inclusion of inelastic scattering processes in the model, specifically Sun-induced CDOM Fluorescence (fDOM) and Sun-Induced Chlorophyll Fluorescence (SICF) from Chlorophyll-a ([chl]), significantly improved the optical closure and led to good agreement between measured and modeled Rrs (i.e., for 440 ≤ λ ≤ 710 nm with no inelastic processes: R2=0.90, slope=0.64; with inelastic processes: R2=0.96, slope=0.90). The analysis also indicated that f_DOM and SICF contributed a substantial fraction of the green-red wavelength Rrs in these waters. Specifically, fDOM contributed ~18% of the modeled Rrs in the green region and SICF accounted for ~20% of the modeled Rrs in the red region. Overall, this study points out the importance of accounting for fDOM in remote sensing applications in CDOM-dominated waters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".