Landsat-4 and Landsat-5 thematic mapper band 6 historical performance and calibration
Bibliographic record
Abstract
Launched in 1982 and 1984 respectively, the Landsat-4 and -5 Thematic Mappers (TM) are the backbone of an extensive archive of moderate resolution Earth imagery. However, these sensors and their data products were not subjected to the type of intensive monitoring that has been part of the Landsat-7 system since its launch in 1999. With Landsat-4's 11 year and Landsat-5's 20+ year data record, there is a need to understand the historical behavior of the instruments in order to verify the scientific integrity of the archive and processed products. Performance indicators of the Landsat-4 and -5 thermal bands have recently been extracted from a processing system database allowing for a more complete study of thermal band characteristics and calibration than was previously possible. The database records responses to the internal calibration system, instrument temperatures and applied gains and offsets for each band for every scene processed through the National Landsat Archive Production System (NLAPS). Analysis of this database has allowed for greater understanding of the calibration and improvement in the processing system. This paper will cover the trends in the Landsat-4 and -5 thermal bands, the effect of the changes seen in the trends, and how these trends affect the use of the thermal data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".