Analysis of Temperature Emissivity Separation (TES) algorithm applicability and sensitivity
Bibliographic record
Abstract
The purpose of this paper is to assess the spectral Temperature Emissivity Separation algorithm (TES) proposed by Gillespie et al. (1998 Gillespie, A. R, Rokugawa, S, Matsunaga, T, Cothern, J. S, Hook, S and Kahle, A. B. 1998. A Temperature and Emissivity Separation algorithm for Advanced Spaceborne Thermal Emission and Reflection radiometer ASTER images. IEEE Transactions on Geoscience and Remote Sensing, 36: 1113–1126. [Crossref], [Web of Science ®] , [Google Scholar]) as a simple method to retrieve surface emissivity from ground-based measurements. First, we validate different empirical relationships for the Minimum Maximum Difference module, on which the TES is based, with a large dataset (about 500 surfaces from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library including man-made materials) for multiband data in the long wave infrared (LWIR: 7.5–14 µm), and hyperspectral data in the middle wave infrared (MWIR: 3.4–5.2 µm) and LWIR. We show the applicability of TES for hyperspectral data using a specific empirical relationship; this is confirmed by experimental measurements. For multiband data, we improve the TES for high contrast emissivity surfaces by integrating broadband 8–14 µm measurements in the iterative algorithm. We also found that metals do not confirm these empirical relationships. TES accuracy, extensively assessed by simulations, remains for multiband simulations (respectively for hyperspectral) within about 0.03 (0.02) for emissivity and within about 1.2 K (0.3 K) for temperature. However, surfaces with low maximum emissivity give higher errors. Except for these particular surfaces, the TES approach, applied on measurements from a portable multiband thermal radiometer, appears as the most efficient and accurate method for emissivity determination in the field without any a priori assumption on the surface nature.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".