Multi‐Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin
Bibliographic record
Abstract
An approach is proposed to estimate soil moisture from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) 6.9 GHz passive microwave observations. The approach was evaluated over two watersheds in the Mackenzie River Basin in northwestern Canada as a contribution to the Canadian Global Energy and Water Cycle Experiment (GEWEX) study and the Mackenzie GEWEX Study (MAGS). Based on the sensitivity of the emitted microwave signal to soil roughness and vegetation parameters, a two‐stage method was applied to calibrate a microwave radiative transfer model. Roughness parameters were determined using observations taken under dry conditions. Vegetation parameters were determined using observations taken under wet conditions. Obtained soil roughness and vegetation parameters were then integrated in the radiative transfer model to retrieve soil moisture. The performances of the proposed approach were evaluated against in situ observations, estimates from the NASA soil moisture product (AMSR‐E), model‐based soil moisture estimates from the NARR and gauge‐based precipitation observations. The lowest RMSE of 0.0254 g/cm 3 was obtained between the retrieved soil moisture and in situ soil moisture. But, the RMSE between the NARR estimates and in situ soil moisture was 0.055 g/cm 3 and between the NASA AMSR‐E product and in situ observation was 0.072 g/cm 3 . This implies that the proposed approach led to an improvement of 55% and 72% in the obtained RMSE over NARR and NASA AMSR‐E soil moisture, respectively. It is noteworthy that the proposed approach is expandable to larger watersheds and very appropriate for remote regions like the Mackenzie River Basin where information on roughness and vegetation are scarce.
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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 | 0.000 |
| 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 | 0.001 | 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".