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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".