The Sensitivity of InSAR Closure Phase to Spatial Variations of Soil Structure and Moisture as Revealed by FDTD Simulations
Bibliographic record
Abstract
Large scale monitoring of soil moisture is important for environmental systems and agriculture, with both optical and synthetic aperture radar (SAR) remote sensing having become methods of choice for repeatably imaging the Earth’s surface. Besides SAR backscatter, the repeat pass interferometric SAR (InSAR) phase is also sensitive to soil moisture changes and has been proposed as an observable for soil moisture retrieval algorithms. The phase loop sum between three interferometric SAR images, called closure phase, is of particular interest for soil moisture retrieval due to its insensitivity to topography and atmospheric changes. A specialized 3-D finite-difference time-domain simulation tool is used to simulate SAR pixels containing a variety of different soil structures. Soil parameters such as inhomogeneity size, moisture gradients and surface roughness are investigated, and the observed closure phase is compared against the predicted results from an analytical model. Further, the sensitivity of closure phase to changes in soil moisture is compared for each of the soil structures under investigation. Finally, we construct a simple moisture regression problem and show that including polarimetry can enable the regression to solve for soil structure properties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".