Soil Moisture Retrieval over a Semiarid Area by Means of PCA Dimensionality Reduction
Bibliographic record
Abstract
The main objective of this study is to develop a multifeature soil moisture retrieval method based on the principal component analysis (PCA) dimensionality reduction technique. RADARSAT-2 data were used to compute the backscattering coefficients and polarimetric variables. The optimal input features for soil moisture retrieval were selected by means of PCA dimensionality reduction and least root mean square error (RMSE) criterion. The support vector regression (SVR) model was used to estimate soil moisture content. The results indicated that the optimal features extracted by the PCA dimensionality reduction showed high correlation with soil moisture content. The RMSE, R2 (determination coefficient) and mean relative error (MRE) were (1.4 vol.%, 0.73, 18.2%) and (1.6 vol.%, 0.66, 15.6%) over the low grass cover areas A and B, respectively. For the bare soil areas A and B, the statistic results were (1.3 vol.%, 0.76, 12.1%) and (1.6 vol.%, 0.72, 14.9%), respectively. This case study confirmed the potential of the developed approach to estimate soil moisture over the low grass cover and bare soil areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".