Radarsat data analysis for monitoring and evaluation of irrigation projects in the monsoon
Bibliographic record
Abstract
The availability of multi-look angle HH polarization C band SAR data of different swaths and resolutions from Radarsat, in addition to VV polarization C band SAR data from ERS, has raised hopes for separating individual crops and for estimating soil moisture, enabling monitoring and evaluation of irrigation projects even under cloud cover conditions. Under the Radarsat ADRO Project, an attempt was made to identify irrigated crops in the Bhadra project command area, Karnataka state, India, using temporal, multi-look angle and dual polarization C-band SAR data from Radarsat and ERS. Preliminary analysis included data quality evaluation, rectification, speckle suppression and separability comparison of different land use and land cover classes. Evaluation of Radarsat data quality showed consistency in spatial resolution. Radarsat data acquired on different dates in shallow (S7 mode) and steep (S2 mode) angles and concurrent ERS-2 data were processed and analysed for possible discrimination of individual crops. It was observed that Radarsat data showed better separability of land use and land cover classes than ERS data. Principal Component Analysis was used on multi-look angle and dual polarization data from both ERS and Radarsat to reduce data dimensionality and the results were better with the first three components.
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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 | 0.000 |
| 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".