A Preliminary Study of Global Sea Surface Chlorophyll Concentration Estimation from GNSS-R Data
Bibliographic record
Abstract
Chlorophyll concentration serves as a key indicator of marine primary productivity, and accurate measurement of chlorophyll is essential for monitoring ocean ecosystems and understanding the impact of climate change on oceans. While optical sensors, such as Moderate Resolution Imaging Spectro-radiometer (MODIS), provide valuable chlorophyll concentration information by calculating reflectance values at different bands, they are limited by cloud cover and other environmental factors. In contrast, Global Navigation Satellite System Reflectometry (GNSS-R) is not affected by cloud cover but the use of GNSS-R data for global sea surface chlorophyll concentration retrieval remains unexplored. This study addresses this gap by analyzing the feasibility of using GNSS-R data and conducting experiments with CYGNSS data. The results demonstrate the potential of the random forest model in estimating chlorophyll concentrations from GNSS-R data, achieving a root mean square difference (RMSD) of 0.17 and a correlation coefficient (CC) of 0.89.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".