Improving Surface Property Retrievals in Boreal Seasonal Snowpacks through Multiscale Modeling of Subgrid Reflectance
Bibliographic record
Abstract
Time-series of surface reflectance and derived land cover and snowpack properties, including snow grain size (SGS), Normalized Difference Snow Index (NDSI), and Soil-Adjusted Vegetation Index were analyzed across multiple spatial resolutions using Landsat (30 m), MODIS (500 m), and VIIRS (1 km) observations over a 10-year period in the Western Canadian domain of NASA's Arctic and Boreal Vulnerability Experiment (ABoVE). The objective is to demonstrate a scalable framework for decomposing VIIRS reflectance signals and associated surface property estimates by accounting for the subgrid-scale heterogeneity introduced by mixed landcover types such as forests, wetlands, and open snow fields. The approach is to separate snow and vegetation reflectance contributions within coarse-resolution pixels and combine this information to produce a joint (mixed) distribution toward improving retrieval SGS, NDSI, and SAVI. The analysis showed that differences between VIIRS 1 Km coarse scale reflectance and Landsat averaged reflectance are closely linked to subgrid-scale variability in forest fraction, spatial organization of forests (quantified by pixel-based chi square heterogeneity) and the spectral behavior of the subgrid 500 m MODIS and VIIRS bands. To address these discrepancies, a random forest model was trained using high-resolution Landsat data to predict separately the mean and standard deviation of subgrid reflectance within each VIIRS pixel for forested and non-forested areas. Model results strong agreement for mean NIR reflectance (RMSE < 0.07, R² > 0.90) with Landsat and good skill in capturing spatial variability (R² ~ 0.5-0.7), particularly in heterogeneous forested regions. Predicted reflectance distributions were used to estimate NDSI, SAVI, and SGS, demonstrating improved agreement with Landsatderived values and reducing known biases in VIIRS-based retrievals. This work provides an unambiguous pathway to integrate VIIRS daily observations with machine learning based corrections for recovering the statistics of subgrid reflectance necessary to produce physically meaningful retrievals of snow cover properties in the absence of high-resolution data.
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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 | 0.000 | 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 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".