Relationships between Radarsat SAR data and surface moisture content of agricultural organic soils
Bibliographic record
Abstract
A time series of 10 C-band fine beam images were acquired over the West Monteregie region (Quebec) in 1999 to examine the potential of Radarsat to detect surface moisture content of cultivated organic soils. Soil moisture data were simultaneously collected from 10 fields cropped to carrots, potatoes, lettuces, and onions. The analysis was conducted at two levels: (i) a temporal scale (daily average value); and (ii) a spatial scale (10 fields). At the first level, we found that the average radar backscatter ) was quite well correlated to the seasonal change of the average moisture content (R 2=0.75), although the dates with a strong vegetation growth decreased the relationship. Comparison with data acquired for mineral soils indicates that organic soils exhibit a lower backscatter, especially at lower moisture content levels, suggesting that soil types should be considered prior to map soil moisture changes. At the field scale, the relationship was much lower (R 2=0.44). The quality of the relationship was proportional to vegetation cover and appeared to be crop-dependent. Best results were obtained with onions and carrots. Considering only bare organic soils, the result was satisfactory (R 2=0.79) and similar to those published on mineral soils.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 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".