Radar based surface soil moisture retrieval through the combined use of two backscattering models
Bibliographic record
Abstract
Radar based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on actually available space borne sensors. The main difficulty experienced so far consists of the parameterization of other surface characteristics, mainly roughness, which strongly influences the backscattering coefficient and harms the soil moisture inversion. This fact, along with the high spatial variability of the surface roughness parameters, makes it necessary to perform intensive roughness measurements in order to invert soil moisture values with an adequate accuracy, what reduces the applicability of the approach. This paper reviews an approach, proposed by Pauwels et al. 8, in which a combined application of two well documented backscattering models, i.e. the Integral Equation Method model and the Oh model, is carried out following an iterative scheme. The approach can be applied to single configuration scenes acquired over homogeneous roughness conditions and yields estimates of both soil moisture and roughness parameters without performing ground measurements of soil moisture or roughness. The proposed algorithm was applied to a set of five RADARSAT-1 scenes acquired over Navarre (Spain) between February and April 2003. Inverted soil moisture and surface roughness parameters were compared to ground measured reference values over an experimental watershed. Results are encouraging and the possibility of simultaneously estimating both variables opens new application scenarios for radar remote sensing on the study of numerous processes at the soil surface.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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 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".