Comparing Performances of Crop Height Inversion Schemes From Multifrequency Pol-InSAR Data
Bibliographic record
Abstract
Polarimetric synthetic aperture radar (SAR) interferometry has shown great potential to estimate the height of crops and forests by inverting simple scattering models of the canopy and the underlying soil. The random-volume-over-ground (RVoG) model assumes that the scatterers within the canopy (e.g., stalks and leaves) are not aligned along a preferred direction. If these scatterers are characterized by a correlation of orientations, then the scene is better described by the oriented-volume-over-ground (OVoG) model. This paper investigates the plausibility of the “random volume” and “oriented volume” assumptions, as well as the robustness of single- and dual-baseline inversion schemes in relation to agricultural crop height estimation. To this end, we implemented different single- and dual-baseline techniques for the inversion of the RVoG and OVoG models, and we evaluated their height retrieval performances with the help of simulated observations and experimental F-SAR measurements in L-, C-, and X-Bands. The inversion results revealed a positive relationship between the bias of the estimated height and the differential extinction when the RVoG inversion scheme is applied. By contrast, no such dependence was observed for the OVoG inversion, whose height estimates are on average consistent with the actual values (i.e., median bias below 10% in magnitude). Despite the observed superiority of dual-baseline approaches, the study also pointed out the feasibility of crop height estimation using single-baseline RVoG inversion schemes, provided the appropriate a priori constraints (e.g., on the extinction coefficient) and crop-specific configuration parameters (e.g., C-Band for maize, and C- and X-Bands for wheat).
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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".