Landsat 7 SLC-Off gap-filling for interim data continuity in northern regions using a robust radiometric normalisation technique
Bibliographic record
Abstract
Much of the current climate change research in Canada involves monitoring northern environments, which require a continuous source of data for change detection. Unfortunately, data continuity of Landsat 7 imagery has been interrupted due to the recent failure of the Scan Line Corrector (SLC), which compensates for the forward motion of the satellite. Although Landsat 7 data are still being acquired, each scene has gaps of no-data values due to the SLC failure. We have created a methodology to produce a consistent source of data for medium-resolution northern biomass study and change detection until a suitable replacement for Landsat 7 is launched. The relatively short growing season and low sun angle in the north provide few opportunities for acquisition of usable earth observation data, and coupled with the failure of the SLC, further reduces the amount of usable data per scene. Fortunately, the large overlap between adjacent orbits in northern Landsat scenes provides a potentially large sample to use for gap infilling. We propose normalization of scenes using overlap regions, semi-invariant targets and a robust regression technique called Thiel-Sen prior to infilling gaps in SLC-Off imagery using the normalised adjacent scene. The new technique enhances the Level 1G (L1G) products provided by the USGS and is modification to gap-filling procedures in the data flow of the Gap-Fill Algorithm currently in use by the USGS. The resulting imagery is much more suitable for biomass modelling using vegetation indices such as the Reduced Simple Ratio (RSR) and Normalised Difference Vegetation Index (NDVI). The current methodology shows great potential for maintaining data continuity of Landsat 7 imagery for northern environmental monitoring procedures requiring the use of vegetation indices.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".