Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12
Bibliographic record
Abstract
The SMAPVEX12 (Soil Moisture Active/Passive Validation Experiment 2012) was carried out over the summer of 2012 in Manitoba, Canada. The goal of the project was to improve the accuracy of satellite-based remote sensing of soil moisture. Data were gathered during a 42-day field campaign with surface measurements on 55 different agricultural fields in south-central Manitoba. The extended duration of the campaign, contrast in soil textures, and variety of crop types over the study region provided an excellent range of soil moisture and vegetation conditions. The study fields ranged from bare to fully vegetated, with volumetric soil moisture levels spanning a range of almost 50%. Remotely sensed data were collected on 17 days by aircraft at 1.4 Ghz with a microwave radiometer at two different resolutions. Observed brightness temperatures from the radiometer showed a typical inverse relationship to the near simultaneous soil moisture measurements from the field. Field-by-field relationships using all sampling dates with both soil and emissivity data were all shown to be significant with the exception of two of the pasture fields and a soybean field. Linear regressions across multiple fields and by flight lines also had statistically significant slopes. The significance of all these relationships improved with the removal of pasture fields from the analysis. On most fields, the sensitivity (slope) of the relationship and correlation coefficient (R2) between emissivity and observed soil moisture increased when vegetation and roughness effects were taken into account. The b parameter that relates vegetation water content to optical depth in the tau-omega model was optimized using the collective slope and R2 values of the individual fields. A b parameter value of 0.06 for horizontal polarization and 0.13 for vertical polarization were found to be optimal across the range of all fields in this analysis.
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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".