Global retrieval of surface soil moisture using L-band SMAP SAR data and its validation
Bibliographic record
Abstract
Surface soil moisture retrievals using radar observations have been challenging due to the strong effects by surface roughness and vegetation scattering. Physically-based forward models for radar scattering are inverted using time-series retrieval algorithm to systematically correct for the effect of the roughness and vegetation. The retrievals are performed for a top 5-cm layer soil moisture at 3-km spatial resolution using the L-band radar data acquired by the Soil Moisture Active Passive (SMAP) satellite globally every three days from mid-April to early July, 2015. These were assessed over 13 rigorously-chosen core validation sites covering a wide range of biomass types and amounts and soil conditions. The soil moisture retrieval performance had an accuracy of approaching the goal of 0.063 m3/m3 unbiased-RMSE (root mean square error), a near zero bias, and a correlation of 0.56. The successful retrieval demonstrates that the physically-based retrieval method is capable of characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation on a global scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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 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".