A Hybrid Temporal Compositing Algorithm for Multispectral Surface Reflectance Imagery
Bibliographic record
Abstract
Temporal compositing of optical satellite imagery is widely used to improve the identification of representative clear-sky observations and to reduce data volume. This paper presents HybridTC, a hybrid temporal compositing algorithm developed for medium-resolution multispectral satellite imagery. HybridTC integrates the strengths of three established compositing methods: maximum ratio of near-infrared to blue band (MaxRNB), weighted parametric scoring (WPS) and spectral similarity-based scoring used for creating the national land cover database (NLCD), while mitigating their limitations. A key innovation of HybridTC lies in its spectral scoring formulas, which are robust and effective in identifying spectrally optimal compositing observations for both land and water surfaces. HybridTC, along with MaxRNB, WPS and NLCD, was implemented on the Google Earth Engine platform and used to generate 30-day and 90-day Sentinel-2 surface reflectance composites for 42 study sites spanning 14 global terrestrial biomes between June and September of 2022 or 2023. Composites were evaluated qualitatively and quantitatively in terms of spectral accuracy, temporal dispersion and spatial congruency. HybridTC achieved the best spectral accuracy with a median site spectral Euclidean distance of 4.57 and 4.54 for 30-day and 90-day composites, surpassing the second-best NLCD approach by 22.54% and 18.05%, respectively. HybridTC also resulted in the lowest temporal dispersion amongst tested methods, with a median site day of year deviation of 5.35 days for 30-day composites and 5.66 days for 90-day composites, outperforming the second-best WPS approach by 1.24 and 5.62 days, respectively. HybridTC attained the highest spatial congruency, in terms of percentage of same-date neighbouring pixels, of 76.46% for 30-day composites and 75.99% for 90-day composites, exceeding WPS, the second-best method, by 7.28% and 69.02%, respectively. Our results demonstrate that HybridTC consistently outperforms other algorithms across all evaluation metrics, both on average and in extreme cases, suggesting its potential to enhance medium-resolution multispectral image composites produced by current and future global mosaic services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".