Soil Moisture Retrieval over Crop Region using Time-Series High-Resolution RCM Data
Bibliographic record
Abstract
Synthetic aperture radar (SAR), as an active microwave sensor, has proven to be effective in retrieving soil moisture (SM) over the past few decades. However, accurately estimating SM over agricultural regions is challenging due to the complex interactions between SM, soil roughness, and vegetation, resulting in mixed backscattering signals. The change detection (CD) method eliminates the influence of soil roughness by employing the ratio of two consecutive SAR images. However, the volume scattering caused by the crop canopy still affects SM estimation. To mitigate this limitation, we propose an advanced change detection method for SM retrieval using the random volume over ground (RVoG) decomposition on time-series compact-polarization SAR data. Experimental results using high-resolution time-series RCM in corn and soybean fields show promising performance, with root-mean-square-error (RMSE) values of 10.34 Vol.% and 7.41 Vol.% for RCH and RCV polarization in the corn field and 6.47 Vol.% and 5.03 Vol.% in the soybean field, respectively. The proposed method outperforms the original CD method, highlighting its potential as a reliable alternative for consistent SM retrieval from the RCM.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".