Retrieving total suspended matter in Lake Taihu from HJ-CCD near-infrared band data
Bibliographic record
Abstract
Among water quality parameters, total suspended matter is important for the evaluation of inland waters. A recently launched satellite sensor, HJ-CCD, by China possesses high temporal resolution, medium spatial resolution, and wide swath, so it is convenient for monitoring this parameter in large inland waters. However, no operational method currently exists for retrieving the total suspended matter concentration of turbid inland waters from HJ-CCD data. Using Lake Taihu in Eastern China as a study area, we obtained and analyzed optical properties of the lake during all seasons, and found that the absorption coefficient of suspended matter, chlorophyll, and colored dissolved organic matter of the near-infrared band may approximate zero. Based on this analysis, we found that a single band method of retrieving concentration using the near-infrared band was suitable using HJ-CCD data. We parameterized the single band method with specific inherent optical properties of Lake Taihu, and validated it using the results retrieved from a HJ-CCD image taken on 14 March 2009, as well as water-surface quasi-synchronous measured data. The concentration retrieved from the image is precise and stable; the single band method uses the established specific optical property database in the study area for input parameters, and does not need support from synchronous data. With this method, HJ-CCD may be applied to retrieve total suspended matter of other highly turbid inland 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".