Landsat-7 ETM+ and Landsat-5 TM Cross-Calibration Based on Tandem Data Sets
Bibliographic record
Abstract
Early in its mission, the Landsat-7 spacecraft was temporarily in a tandemorbit that drifted across that of the Landsat-5 spacecraft in order to facilitate sensor cross-calibration based on nearly-coincident scenes acquired by the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM). This paper presents a methodology for radiometric cross-calibration of the solar reflective spectral bands of the Landsat-7 ETM+ and Landsat-5 TM sensors and results based on analysis of two different tandem image pairs for which ground reference data are available. With the well-calibrated ETM+ as a reference, the tandem-based cross-calibrations for the two image pairs yield TM responsivity coefficients that are consistent to each other to within a few percent or better depending on the spectral band. Comparisons with independent methods and results obtained by other groups indicate that the tandem-based cross-calibration is in close agreement with the independent results in spectral bands 1-4 but compares less favourably in bands 5 and 7. The methodology also incorporates adjustments for spectral band differences between the two Landsat sensors. Spectral band difference effects are shown to be more dependent on the surface reflectance spectrum than on atmospheric and illumination conditions. A variety of target types are assessed regarding their suitability for Landsat radiometric cross-calibration in the absence of surface reflectance spectra.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".