Improving Surface Property Retrievals in Boreal Seasonal Snowpacks Through Multiscale Modeling of Subgrid Reflectance

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), moderate resolution imaging spectroradiometer (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 leverage this information toward improving retrieval of SGS, NDSI, and SAVI. The analysis showed that differences between VIIRS 1 km coarse scale reflectance and Landsat averaged reflectance at the same resolution 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 across scales, a random forest model was trained against high-resolution Landsat data to predict separately the mean and standard deviation of subgrid reflectance within each VIIRS pixel for forested and nonforested areas using VIIRS observations and ancillary data (topography and land-cover). Model results show strong agreement with Landsat for mean near-infrared reflectance (RMSE < 0.07, R² > 0.90) 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 Landsat-derived values and reducing known biases in VIIRS-based retrievals.

Duke Scholars

Author Ana P. Barros Civil and Environmental Engineering