The Potential of PALSAR ScanSAR Mode for Soil Moisture Retrieval
Bibliographic record
Abstract
This document is the final report for all activities related to the JAXA ALOS PI agreement 090. The initial research focus was on soil moisture monitoring in semi-arid regions. Attention was shifted to the assessment of landscape heterogeneity in the high latitudes during the extension period of the PI agreement. || Soil moisture monitoring requires frequent acquisitions in order to capture this highly variable parameter. The retrieval approach followed by the PI requires a large sample for each location, representing wet and dry conditions. Currently C-Band ENVISAT ASAR WS (Wide Swath, 150m) and GM (Global Mode, 1km) data are used for the establishment of a near real time processing chain. Similar data with regular intervals will become also available from Sentinal-1. L-Band advantageous compared to C-Band regarding vegetation penetration and sensitivity to changes of near surface soil water content. Regular acquisitions are however not available. The C-band capabilities have been assessed with the ALOS PALSAR data over Africa, but the sample of ALOS data was too small. The potential of the L-band data could be nevertheless demonstrated. || Soil moisture retrieval in tundra regions is impacted by landscape heterogeneity. Especially the abundance of small ponds needs to be taken into consideration. ALOS PALSAR fine beam data are used for the assessment of the small lakes detection capabilities of ENVISAT ASAR WS, which is available for regional to sub-continental analyses. The majority of lakes which are identifiable with fine beam can be also captured with ASAR WS. The difference in total water surface extent can be mostly attributed to rims around larger lakes. || Research in semi-arid as well as permafrost environment is still ongoing and is performed in cooperation with international project partners.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".