Temporal Soil Moisture Estimation of Pastures from Radarsat Data For Applications in Watershed Modelling
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. <p> 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. <p> This paper addresses the potential of Radarsat to extract information on soil moisture in pastures in a mixed landcover watershed located in eastern Ontario, Canada. A series of 6 Radarsat Standard Beam Mode 1 images covering the watershed for the period of September 2000 through July 2001 were analyzed in relation to ground observations, weather radar and meteorological conditions. A method was then developed to produce soil moisture maps for input to a hydrological model.
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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 | 0.000 | 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 | 0.000 | 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".