The Influence of Snow Cover Variability and Tundra Lakes on Passive Microwave Remote Sensing of Late Winter Snow Water Equivalent in the Hudson Bay Lowlands
Bibliographic record
Abstract
Current North American operational satellite passive microwave snow water equivalent (SWE) retrieval algorithms consistently underestimate SWE levels for tundra environments when compared to four years of regional snow surveys conducted in the Northwest Territories and northern Manitoba, Canada. Almost all contemporary SWE algorithms are based on the brightness temperature difference between the 37GHz and 19GHz frequencies found onboard both past and present spaceborne sensors. This underestimation is likely a result of the distribution and deposition of the tundra snow, coupled with the influence of tundra lakes on brightness temperatures at the 19GHz frequency. To better our understanding concerning the underestimation of passive microwave SWE retrievals on the tundra, Environment Canada collected in situ measurements of SWE, snow depth, and density at 87 sites within a 25km by 25km study domain located near Churchill, Manitoba in March 2006. Coincident multi-scale passive microwave airborne (70m & 500m resolution) and spaceborne (regridded to 12.5km & 25km resolution depending on frequency) data were measured at 6.9GHz, 19GHz, 37GHz and 89 GHz frequencies during the same time period. \n \nThe snow survey data highlighted small-scale localized patterns of snow distribution and deposition on the tundra that likely influences current SWE underestimation. Snow from the open tundra plains is re-distributed by wind into small-scale vegetated features and micro-topographic depressions such as narrow creekbeds, lake edge willows, small stands of coniferous trees and polygonal wedge depressions. The very large amounts of snow deposited in these spatially-constrained features has little influence on the microwave emission measured by large-scale passive microwave spaceborne sensors and is therefore unaccounted for in current methods of satellite SWE estimation. The analysis of the passive microwave airborne data revealed that brightness temperatures at the 19GHz were much lower over some tundra lakes, effectively lowering SWE at the satellite scale by reducing the 37-19GHz brightness temperature difference used to estimate SWE. The unique emission properties of lakes in the wide open expanse of the tundra plains, coupled with an insensitivity to the large amounts of SWE deposited in small-scale features provides an explanation for current passive microwave underestimation of SWE in the tundra environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".