Estimating soil moisture at the watershed scale with satellite-based radar and land surface models
Bibliographic record
Abstract
Spatially distributed soil moisture profiles are required for watershed applications such as drought and flood prediction, crop irrigation scheduling, pest management, and determining mobility with lightweight vehicles. Satellite-based soil moisture can be obtained from passive microwave, active microwave, and optical sensors, although the coarse spatial resolution of passive microwave and the inability to obtain vertically resolved information from optical sensors limit their usefulness for watershed-scale applications. Active microwave sensors such as synthetic aperture radar (SAR) currently represent the best approach for obtaining spatially distributed surface soil moisture at scales of 10–100 m for watersheds ranging from 1 000 to 25 000 km2. Although SAR provides surface soil moisture, the applications listed above require vertically resolved soil moisture profiles. To obtain distributed soil moisture profiles, a combined approach of calibration and data assimilation in soil vegetation atmosphere transfer (SVAT) models based on recent advances in soil physics is the most promising avenue of research. This review summarizes the state of the science using current satellite-based sensors to determine watershed-scale surface soil moisture distribution and the state of combining SVAT models with data assimilation and calibration approaches for the estimation of profile soil moisture. The basic conclusion of this review is that currently orbiting SAR sensors combined with available SVAT models could provide distributed profile soil moisture information with known accuracy at the watershed scale. The priority areas for future research should include image-based approaches for mapping surface roughness, determination of soil moisture in densely vegetated sites, active and passive microwave data fusion, and joint calibration and data assimilation approaches for a combined remote sensing – modeling system. For validation, a worldwide in situ soil moisture monitoring program should be implemented. Finally, to realize the full potential of satellite-based soil moisture estimation for watershed applications, it will be necessary to continue sensor development, improve image availability and timely delivery, and reduce image cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".