Downscaling SMOS/SMAP soil moisture product using high resolution Radarsat-2 SAR data: a case study in southern Ontario
Bibliographic record
Abstract
Soil moisture is a key component in the water cycle. Continuous observations of soil moisture over large spatial scales are important in many earth sciences applications. Current soil moisture products derived from SMOS and SMAP satellites can provide global coverage with 2-3 days cycle but have very coarse resolutions (~40 km), which limits the soil moisture products in many applications where a resolution of 1-10 km is generally needed. SAR imagery is available at high resolution and has high sensitivity to soil moisture. However the soil moisture retrieval from SAR depends on high volume of in-situ soil moisture data and is also complicated by their sensitivity to surface roughness and vegetation. This study proposes an algorithm for retrieving high resolution soil moisture by downscaling SMOS/SMAP soil moisture products using time series dual-polarized (HH and HV) Radarsat-2 data. The approach can overcome the effect of vegetation, surface roughness, and change of scales. Specifically, the effect of vegetation is removed by the water-cloud model, in which the conditions of vegetation are characterized by the backscatter coefficient of Radarsat-2 HV polarization. Time series Radarsat-2 data is used to eliminate the dependence of backscattered signal on soil surface roughness. Different mathematical models including wavelet transform are used for scale change. The algorithm is validated using in-situ soil moisture data collected in Southern Ontario in the spring and summer of 2016. The study shows promising results in soil moisture retrieval over large area.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".