Land cover dependence in the detection of contaminated pixels in satellite optical data
Bibliographic record
Abstract
The detection of partially contaminated pixels over land is necessary for quantitative applications of satellite optical measurements to estimate surface biophysical parameters such as leaf area index or vegetation composition. Threshold-based algorithms suffer from the heterogeneity of land cover and the seasonal variability of the radiation reflected and emitted by the land surface. As an alternative, a method based on a Fourier series approximation to the seasonal trajectory of the normalized difference vegetation index (NDVI) had been previously developed (Cihlar 1996). In this paper, we introduce modifications to the basic algorithm to more closely represent NDVI seasonal trends for different land cover types, as well as a simplified way to determine the time- and pixel-specific contamination thresholds. Based on the tests with 1993-1996 Advanced Very High Resolution Radiometer (AVHRR) data over Canada, the modified procedure effectively detects contaminated pixels for boreal ecosystems after the growing season of interest. The modifications also improved its performance while the growing season is in progress; in this case, at least one complete previous growing season coverage is required to provide the temporal series needed to establish the thresholds. The modified procedure also yields a contamination parameter that may be used to estimate the most likely value for NDVI or other variables for each pixel. It is concluded that the procedure would perform effectively in other areas, provided that the NDVI temporal trajectories of the cover types of interest can he represented by a mathematical model.
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".