Comparison of surface reflectance derived by relative radiometric normalization versus atmospheric correction for generating large-scale Landsat mosaics
Bibliographic record
Abstract
Generating large-scale Landsat mosaics of surface reflectance is challenging because of the tediousness arising from atmospheric correction for a large number of scenes. To find out an alternative approach, we conducted an empirical investigation to compare the surface reflectance derived by relative radiometric normalization versus atmospheric correction using four pairs of adjoining Landsat Thematic Mapper/Enhanced Thematic Mapper Plus scenes in northern Canada. Each image was first atmospherically corrected to convert top-of-atmosphere radiance to surface reflectance. One of the converted images in each pair was then respectively used as a reference to radiometrically normalize the other original one for deriving surface reflectance. Comparison of the surface reflectance derived by these two different approaches indicates that they can match reasonably well for different landscapes, atmospheric conditions, and sensors, and the difference measured by root mean square error is no more than 0.0098 for the visible band (Band 3), 0.0271 for the near-infrared band (Band 4), and 0.022 for the middle-infrared band (Band 5). Given such a small difference, we would expect that relative radiometric normalization may be used as an alternative approach for reliable and fast retrieval of surface reflectance from Landsat data for generating mosaics of surface reflectance over large areas, overcoming the tediousness arising from atmospheric correction for a large number of scenes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".