New Approaches for Removing the Effect of Water Damping on SMAP Freeze/Thaw Mapping
Bibliographic record
Abstract
<p>Le paysage du Nord du Québec comprend plusieurs lacs. La température de brillance (Tb) mesurée est dominée par la plus basse émissivité des surfaces d’eau. Ainsi, il est nécessaire d’éliminer cet artifact. L’objectif principal de cette étude est de proposer deux nouvelles approches pour corriger la température de brillance des produits SMAP L1C de l’effet atténuant des masses d’eau pours chacun des pixels de 36 km par 36 km d’images acquises entre janvier et décembre 2016. Le premier algorithme normalise les valeurs de Tb avec l’ordonnée à l’origine de sa régression linéaire par rapport à la fraction de l’eau dans chacun des pixels. Le second algorithme normalise par rapport à la fraction de l’eau par classe de couverture végétale. Pour valider les valeurs corrigées, nous avons utilisé des données de température et d’humidité du sol de sondes installées près d’Umiujaq. La correction proposée de Tb résout les divergences observées avec la correction SMAP de référence lorsque la proportion d’eau au sein d’un pixel dépasse 20%. Le gel/non gel du sol a ensuite été cartographié en utilisant le Rapport de polarisation normalisé. Une concordance jusqu'à 90% (orbite ascendant) et 79% (orbite descendant) a été obtenue par rapport à 64% et 50% pour l’approche de référence.</p><h2>Abstract</h2><p>The Northern Quebec landscape is typically covered by numerous lakes. The lowest emissivity of water bodies dominates the brightness temperature (Tb) data measured over this region. Thus, it is necessary to eliminate the effect of water bodies from Tb measurements. The primary objective of this study is to develop two approaches to correct the Soil Moisture Active Passive brightness temperature (SMAP L1C), collected between January and December of 2016, for the damping effect of water bodies within each 36 km by 36 km pixel. The first algorithm normalizes Tb with the intercept of its linear regression versus the water fraction of each pixel. A second algorithm used Tb regression with water fraction by vegetation classes for each scene. Surface soil temperature and moisture measured near Umiujaq were used to validate Tb correction. The proposed Tb corrections resolve the divergence observed with SMAP standard correction when the water fraction is higher than 20%. Corrected brightness temperature is then tested for mapping the soil freeze/thaw state using the Normalized Polarization Ratio. Agreements of up to 90% (ascending orbit) and 79% (descending orbit) were reached for the proposed approach versus 64% and 50% for the existing approach.</p>
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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 | 0.001 | 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".