SNOPOST - Bayesian snow estimation under a vegetation gradient using SnowEx remote sensing data

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Context and objectives

The biggest gap in our knowledge of the global surface water budget relates to snow. Remote sensing of snow water equivalents (SWE) has been notoriously difficult, especially in vegetated areas. Innovative concepts are needed to solve this problem. NASA has launched a multi-year airborne campaign, SnowEx, to collect a wealth of data over snow covered regions with varying vegetation densities. The first campaign was held in February 2017 in Grand Mesa, Colorado (US), a flat region with varying forest densities. The SNOPOST project will use this unique dataset of airborne and terrestrial remote sensing data, and collaborate with the SnowEx team, to unlock the potential of remote sensing for snow estimation and prepare the next generation of snow scientists.

SNOPOST has three objectives:

  1. First, the spatial and temporal distribution of snow will be characterized based on remote sensing data and in situ SWE and snow depth observations, as well as using model simulations.
  2. Second, background model simulations will be merged with the multitude of remote sensing observations to estimate a posteriori SWE and snow depth, along with the uncertainty, by Bayesian inference.
  3. Finally, the sensors used in SnowEx will be objectively ranked based on their information content, i.e. reflecting their observation impact on a posteriori snow estimates. Ultimately, the sensor ranking and the stratification by physiographic indicators will serve as invaluable design recommendations for future spaceborne snow missions.

Project outcome

Expected scientific results

  • Insight into the benefits and shortcomings of various types of satellite sensors over non-vegetated and vegetated areas
  • Insight into physical snow processes via land surface and radiative transfer modeling at various wavelengths
  • A posteriori snow estimates and their uncertainty estimates over Grand Mesa, during SnowEx 2017

Expected products and services


Bayesian merging of snow products, validation tools


  • A posteriori snow estimates and their uncertainty estimates
  • Peer reviewed publications


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