Compact Polarimetry for Agricultural Mapping and Inventory: Preparation for Radarsat Constellation Mission
Bibliographic record
Abstract
Agriculture and Agri-Food Canada (AAFC) has combined RADARSAT-2 C-band dual polarization data with optical imagery to map crop types across the agricultural extent of Canada yearly since 2009. In preparation for the launch of the RADARSAT Constellation Mission (RCM) primary research has been focused on incorporating similar-mode dual polarization RCM data in the operational system. The availability of the compact polarimetry (CP) mode with continuous coverage has important implications for crop mapping and inventory. CP mode on RCM has a circular transmit and two orthogonal linear receive structure and maintains phase information. The addition of CP data to AAFC's operational crop type mapping will expand the information that the current dual polarization Synthetic Aperture Radar (SAR) component provides. This will increase the SAR contribution from the simple intensity of backscatter to capturing the scattering characteristics of the target. There are many parameters and decompositions that can be derived from CP data. Many of these parameters are highly correlated or may not provide information that are important for crop type identification. The goal of this research was to derive twenty-four CP parameters and decompositions for a growing season of SAR imagery and to assess the importance and contribution of these features to an overall classification of crops in southern Manitoba, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".