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Record W2012144838 · doi:10.1117/12.619992

Landsat-4 and Landsat-5 thematic mapper band 6 historical performance and calibration

2005· article· en· W2012144838 on OpenAlexaff
Julia A. Barsi, Gyanesh Chander, Brian L. Markham, Nicholas Higgs

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsThematic MapperRemote sensingCalibrationComputer scienceThematic mapDatabaseSpectral bandsData processingEnvironmental scienceSatellite imageryGeologyGeographyCartography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.202
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicCalibration and Measurement TechniquesFrench-language works237,207