Can Polarimetric Radarsat-2 Images Provide a Solution to Quantify Non-Photosynthetic Vegetation Biomass in Semiarid Mixed Grassland?
Bibliographic record
Abstract
Quantifying non-photosynthetic vegetation (NPV) biomass using optical remote sensing in semiarid mixed grassland is challenging. This is due to the combined effects of photosynthetic vegetation, biological soil crust, and bare soil on the canopy spectra. Radarsat-2 provides a new way to quantify NPV biomass. This study investigated the potential of fine quad-pol Radarsat-2 images for quantifying NPV biomass and total aboveground biomass in semiarid mixed grasslands. The parameters used were Radar Vegetation Index, co-polarization ratio (HH/VV), cross-polarization ratios (VH/HH and VH/VV), de-polarization ratio, the Cloude and Pottier decomposition component (Entropy and Alpha angle) and the Freeman-Durden decomposition components (volume, surface, and multiple scattering). The best NPV and total aboveground biomass estimations are achieved with an r2 of 0.70 and 0.51 and relative root mean square error (rRMSE) of 9% and 8.4%, respectively, using the VH/VV cross-polarization ratio of the FQ23 (41.9°–43.3°) image in the middle growing season. The r2 values are 0.65 and 0.70 and the rRMSE are 12.6% and 8.4%, respectively, for NPV and total biomass estimation using the depolarization ratio of the FQ3 (20.9°–22.9°) image in the peak growing season.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".