Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: none
- Teacher disagreement score
- 0.802
- Threshold uncertainty score
- 0.911
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.238 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.
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.
The record
- Venue
- Remote Sensing
- Topic
- Urban Heat Island Mitigation
- Field
- Environmental Science
- Canadian institutions
- University of Saskatchewan
- Funders
- National Oceanic and Atmospheric AdministrationU.S. Geological SurveyChina Scholarship Council
- Keywords
- Remote sensingEmissivityRadiative transferInversion (geology)Environmental scienceAtmospheric radiative transfer codesAlgorithmThermal infraredEarth's energy budgetComputer scienceMeteorologyInfraredRadiationPhysicsGeologyOptics
- Has abstract in OpenAlex
- yes