Remote sensing of snow and its application to hydrometeorological studies in western Canada.
Bibliographic record
Abstract
Snow plays a vital role in the energy and water budgets of drainage basins of western Canada. Various remote sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave/Imager (SSM/I) have been launched to map the snow cover extent (SCE), snow cover fraction (SCF), and snow water equivalent (SWE) across the globe. However, the distribution and variability of snow inferred from remote sensing products have not been comprehensively investigated in western Canada owing to its complex topography and harsh environment. So far, little research has been conducted on SCE-streamflow and SCE-SWE-runoff models focusing on Canadian watersheds where snow cover is very important for human well being. Although microwave remote sensing of snow is widely developed and applied in Canada, the retrieval of SWE in western Canada is not as well established owing to the complex topography in this area. Therefore, the Quesnel River Basin (QRB) of British Columbia is selected as a primary test site to develop and test SCE-streamflow and SCE-SWE-runoff models. Then the Mackenzie River Basin (MRB) is chosen as a secondary test site to apply the Environment Canada (EC) SWE retrieval algorithms to upscale the hydrometeorological research. In this thesis, a new approach referred to as the spatial filter (SF) method is developed to decrease the cloud coverage in the MODIS snow products. At the same time, the new snow products are evaluated based on in-situ observations of snow depth in the QRB. Then the relationships between SCF from MODIS, topography, and hydrometeorology of the QRB are explored. In addition, various retrieval algorithms of SWE from microwave remote sensing are tested in the QRB. At last, the Environment Canada algorithms of SWE from SSM/I are adopted to produce new SCF products evaluated with the MODIS snow products. The relationships between SWE and SCF from SSM/I and hydrometeorology are also investigated in the MRB ...The stud
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".