Potential of bias correction for downscaling passive microwave and soil moisture data
Bibliographic record
Abstract
Abstract Passive microwave satellites such as Soil Moisture and Ocean Salinity or Soil Moisture Active Passive observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disaggregate soil moisture to a scale more appropriate for hydrological applications. Temporal stability of soil moisture and TB was demonstrated at the Little Washita and Little River Experimental Watersheds using in situ observations and the Community Microwave Emissions Model. Decomposition of the mean square difference (MSD) between the watershed average soil moisture and TB showed that bias was a major contributor to differences between watershed average and local‐scale soil moisture and TB, particularly at sites with high MSD. The mean RMSD between watershed average and local soil moisture was 0.04 m3 m−3 and 0.06 m3 m−3 at Little River and Little Washita, respectively. Following a simple bias correction the RMSD was reduced to 0.03 m3 m−3 at both sites. Considering multiple incidence angles at both horizontal and vertical polarization, bias correction of watershed average TBV reduced the RMSD by approximately 75% and 45% and TBH RMSD by 68% and 36% for Little River and Little Washita, respectively, at all incidence angles. Therefore, at subsatellite grid scale, bias correction can be considered a viable technique for downscaling passive microwave observations and soil moisture retrievals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".