Earth Observation Sensor Calibration Using a Global Instrumented and Automated Network of Test Sites (GIANTS)
Bibliographic record
Abstract
Calibration is critical for useful long-term data records, as well as independent data quality control. However, in the context of Earth observation sensors, post-launch calibration and the associated quality assurance perspective are far from operational. This paper explores the possibility of establishing a global instrumented and automated network of test sites (GIANTS) for post-launch radiometric calibration of Earth observation sensors. It is proposed that a small number of well-instrumented benchmark test sites and data sets for calibration be supported. A core set of sensors, measurements, and protocols would be standardised across all participating test sites and the measurement data sets would undergo identical processing at a central secretariat. The network would provide calibration information to supplement or substitute for on-board calibration, would reduce the effort required by individual agencies, and would provide consistency for cross-platform studies. Central to the GIANTS concept is the use of automation, communication, co-ordination, visibility, and education, all of which can be facilitated by greater use of advanced in situ sensor and telecommunication technologies. The goal is to help ensure that the resources devoted to remote sensing calibration benefit the intended user community and facilitate the development of new calibration methodologies (research and development) and future specialists (education and training). Keywords: sensor radiometric calibration, test sites, in situ sensing
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".