Soil Moisture Active Passive Improves Global Soil Moisture Simulation in a Land Surface Scheme and Reveals Strong Irrigation Signals Over Farmlands
Bibliographic record
Abstract
Abstract The successful Soil Moisture Active Passive (SMAP) mission provides operational soil moisture products of high quality; yet its impacts on global carbon and water cycle estimation are yet to be further investigated. Here we assimilated the SMAP enhanced Level‐2 soil moisture product at 9 km resolution into a land surface scheme in order to study the soil moisture control on the functioning of terrestrial ecosystems. We found that SMAP significantly improves soil moisture simulations, especially in the spring. Extensive wetting signals were revealed over croplands in arid and semi‐arid regions and could not be explained using reanalysis meteorological data, indicating an additional water input, for example, irrigation. Stronger impacts on gross primary production and evapotranspiration simulations are found in wetting adjustments than in drying adjustments after data assimilation. This study suggests that the performance of the land surface scheme benefits greatly from assimilating the SMAP soil moisture product.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".