Modeling InSAR closure phase and soil moisture inversion
Bibliographic record
Abstract
Closure phases in SAR interferometry have the potential to carry information about the scatterer and changes in its dielectric properties. We are modeling the effects of scatterer changes on the interferometric phases. In this contribution we present some result and open questions. \nWe start by using the interferometric model in De Zan et al. 2014 to explain the closure phases observed in InSAR with moisture variations and invert them. This model seems to work rather well in L-band and we report inversion results for the CanEX-SM10 campaign by UAVSAR (NASA/JPL) over an agricultural site in Saskatchewan, Canada. Here the field-based correlation between our inversion and moisture probes are higher than 0.7 for most of the fields. The scaling factor between the inversion and the probe signal is, however, highly variable and we do not have a satisfactory model for it yet. \nWe report also encouraging first results with C-band modeling over southern Italy observed with Sentinel-1. The closure phases obey a similar model as for L-band, but they are typically downscaled, i.e. we observed values smaller than the model would predict. \nThere is a possibility that the moisture model is not explaining the totality of the observed closure phase. This idea is suggested also by processing interferometrically stacks of SAR images and limiting the temporal separation to a few months. The retrieved deformation exhibits a spurious drift that amounts to a few millimeters / year. The drift is rather constant and does not seem to be related to the moisture cycles. \nBy adding just a few (3) parameters to the interferometric models it is possible to explain significantly better both the observed closure phases and the drifts when reconstructing a deformation series by limiting the temporal separation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".