Novel Spectra-Derived Features for Empirical Retrieval of Water Quality Parameters: Demonstrations for OLI, MSI, and OLCI Sensors
Bibliographic record
Abstract
The empirical (regression-based) methods for estimation of water quality parameters are mostly built upon the features derived from the original feature space of optical imagery (e.g., band ratios). This article aims at examining novel features to retrieve in-water constituents including chlorophyll-a (Chl-a), total suspended solids (TSS), and colored dissolved organic matter (CDOM). In this article, direction cosines and transformation of either color space or the coordinate system are applied to the original feature space in order to derive new features. A full-search approach is exploited to identify the optimal band combination for a given type of feature. The proposed analysis seeks for a band combination among all the possible ones that yield the strongest correlation through regressing a given feature against the concentration of the constituent of interest. The effectiveness of the proposed features is examined against standard ones using radiative transfer simulations, in situ measurements, and satellite imagery in a wide range of in-water optical conditions. The simulated and in situ data enabled in-depth analyses on the efficacy of recent satellite sensors with the primary focus of the aquatic science community [Operational Land Imager (OLI), MutiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI)] for retrieval of in-water constituents. TSS and Chl-a concentration of two alpine lakes (Lake Constance and Lake Lucerne) are also mapped using a real OLI image. The results suggest the effectiveness of the proposed features that can be leveraged to estimate the constituents in inland/coastal waters. OLI-based retrievals of in-water constituents proved difficulties in optically complex waters, whereas enhanced spectral resolution of MSI and OLCI permitted accurate estimates.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".