EGU24-13310, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A data fusion approach to produce daily, high resolution snow albedo using multispectral and hyperspectral imagery

Ross T. Palomaki1, Karl Rittger1, Sebastien J. P. Lenard1, Jeff Dozier2, and S. McKenzie Skiles3
Ross T. Palomaki et al.
  • 1Institute of Arctic and Alpine Research, University of Colorado, Boulder, United States of America (
  • 2Bren School of Environmental Science & Management, University of California, Santa Barbara, United States of America
  • 3Department of Geography, University of Utah, Salt Lake City, United States of America

Snow albedo data are required for various research and applications at a wide range of spatial and temporal scales. Typically, spatially-distributed snow albedo measurements are generated using multispectral satellite data, including MODIS, Sentinel-2, and Landsat imagery. While a number of algorithms can be employed to create snow albedo products from multispectral satellite imagery, a recent MODIS-focused analysis shows that spectrally-based approaches result in the most accurate snow albedo. These approaches use spectral libraries of snow, vegetation, and rock reflectance to solve for snow fraction, grain size, and the impact of light absorbing particles (LAP) on snow albedo; snow albedo is estimated by combining the grain size with darkening due to LAP.

Spectral unmixing algorithms produce more accurate snow albedo measurements when applied to hyperspectral data because the additional spectral information removes ambiguities associated with sparser multispectral imagery. Various airborne sensors and satellite missions EnMAP, EMIT, and PRISMA provide hyperspectral data with spatial resolutions on the order of tens of meters, but depending on the platform have repeat periods between 8-29 days, and may miss important albedo changes related to early season snow accumulation and late season dust events.

In this presentation, we show initial results from a data fusion approach to produce daily snow albedo data at high spatial resolutions using multispectral and hyperspectral imagery. Our model fuses snow albedo measurements directly instead of reflectance data to take advantage of the improved ability of the spectral unmixing algorithm to address mixed pixels and better discern clouds from snow. To demonstrate our approach, we train a random forest model on snow albedo measurements generated from airborne hyperspectral data at 50 m resolution. Predictor variables include daily, 463 m MODIS snow albedo generated using a spectral unmixing algorithm, as well as terrain characteristics and solar illumination. The fused snow albedo data take advantage of the more accurate and finer resolution hyperspectral data will maintaining the daily temporal resolution of multispectral MODIS imagery. Additionally, our fusion approach is flexible and can incorporate snow albedo measurements from additional airborne or satellite sensors, including multispectral VIIRS data and hyperspectral data from the upcoming SBG and CHIME satellite missions.

How to cite: Palomaki, R. T., Rittger, K., Lenard, S. J. P., Dozier, J., and Skiles, S. M.: A data fusion approach to produce daily, high resolution snow albedo using multispectral and hyperspectral imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13310,, 2024.