Spectral emissivity of northern land cover types derived with MODIS and ASTER sensors in MWIR and LWIR
Bibliographic record
Abstract
The purpose of this study is to evaluate the potential of satellite-derived emissivity in middle-wave infrared (MWIR) and long-wave infrared (LWIR) for land surface characterization. We compared emissivities derived from advanced spaceborne thermal emission and reflection radiometer (ASTER; temperature emissivity separation (TES) algorithm; validated data V003) and moderate resolution imaging spectroradiometer (MODIS; two different algorithms, classification-based emissivity method and day–night land surface temperature algorithm; provisional data V003) images acquired over northern Canadian regions. We observed disparities in emissivity dynamic range between each algorithm, and a bias also exists for the MODIS day–night algorithm (–0.02 versus ASTER). Lastly, we related MODIS and ASTER emissivity images with land cover type data derived from MODIS visible and near-infrared observations. Emissivity characteristics were determined for each class encountered. However, we generally observed a significant emissivity spatial heterogeneity inside a single land cover class.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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)
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 teacher head, 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".