Temporal soil moisture estimates from Radarsat-1 and Envisat ASAR for flood forecasting
Bibliographic record
Abstract
Estimating the amount of water stored in a soil profile is essential in most water management projects and for assessing the hydrologic state of a basin. It determines infiltration during a rainfall event and controls evapotranspiration between storms. Rarely, however, are soil moisture data available for model input. In many cases, particularly watershed scale monitoring or modelling, soil moisture is inferred from more easily obtainable hydrologic variables such as rainfall, runoff and temperature. As such, there is a strong need for procedures to estimate soil moisture in a watershed independently from the models. These procedures must provide not only basin average estimates but also the spatial distribution within a basin in order to meet the requirements of emerging distributed models. Active and passive microwave imagery are both candidate sources for these data. Active SAR imagery, with its high resolution, is particularly attractive for use in areas of mixed land cover. This paper addresses the potential of Radarsat and Envisat ASAR Synthetic Aperture Radar (SAR) data to extract information on soil moisture at the watershed scale. Multiple Radarsat and Envisat data acquisitions collected over the Roseau River watershed, located in Manitoba, for the period of September 2002 through June 2003 were analyzed in relation to ground observations and meteorological conditions. A method was then developed to produce soil moisture maps for input to a hydrological model for flood forecasting.
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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) | 0.003 | 0.001 |
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".