Correcting Satellite Passive Microwave Brightness Temperatures in Forested Landscapes Using Satellite Visible Reflectance Estimates of Forest Transmissivity
Bibliographic record
Abstract
Forest cover attenuation of microwave emission is a significant challenge to the estimation of snow accumulation from remote sensing microwave observations because canopy biomass attenuates the understory snowcover emission and produces additional emission to that generated by the snowpack and subnivean surface. Transmissivity of radiation is an important variable that describes how a tree canopy attenuates microwave emission from the ground. Although it can be measured in the field or estimated by models using field data at the in situ scale, the estimation of transmissivity at regional to global scales is a challenge. Following the work of Metsämäki et al. (2005), a transmissivity model that uses reflectance data from the moderate resolution imaging spectroradiometer is applied to estimate transmissivity at global scales. The influence of the vegetation attenuation and the emission on the brightness temperature (Tb), which is observed by advanced microwave scanning radiometer-Earth observing system sensor (Tbvegetation), can be calculated by comparing the Tb of the ground below-canopy (Tbground) with the Tb above the forest canopy (Tbac) during the presnow season. Linear regression models derived between transmissivity estimates and the Tbvegetationhad significant R2values of 0.76 (0.96) at 18 GHz vertical (horizontal) polarization and 0.91 (0.91) at 36 GHz vertical (horizontal) polarization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".