EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

An integrated remote-sensing approach for prairie snowpack

Eric A. Sproles1,2, Ross T. Palomaki1, Madison Woodley3, and Samual E. Tuttle3
Eric A. Sproles et al.
  • 1Dept of Earth Sciences, Montana State University, Bozeman MT, United States of America
  • 2Geospatial Core Facility
  • 3Dept of Earth and Environmental Sciences, Syracuse University, Syracuse NY, United States of America

In a low-relief, agricultural landscape we integrate detailed measurements from plane-based L-band SAR (UAVSAR), drone-based LiDAR and photogrammetry, cosmic ray neutron sensor (CRNS), and field assessments to disentangle and quantify how topography, wind, and vegetation influence the spatial distribution snow cover and water equivalent. Seasonal snow in prairie and temperate grasslands environments helps sustain agriculture, socio-environmental systems, and aquifers while also exacerbating flooding in wetter years. Because these expansive landscapes cover roughly 10% of the earth’s surface, quantifying snow and snow water equivalent (SWE) is critical to better resolve water and energy budgets from local to global scales. Present day, remotely-sensed observations and conventional automated ground-based observations (e.g. SWE scales) of seasonal snow in these biomes contain considerable uncertainty. Optical imagery can detect the presence/absence of snowpack, but lacks the capacity to provide estimates of SWE. Synthetic Aperture Radar (SAR) provides a potential path forward to quantify SWE in grassland and agricultural environments, but current measurements are poorly constrained, especially in prairie environments. The Central Agricultural Research Center (CARC) in central Montana, USA (47ºN, 110ºW) served as field site for NASA’s SnowEx 2021 Mission and was distinct from other campaign locations due to its prairie landscape, controlled agricultural vegetation patterns, and ephemeral snow cover. The CRNS measures an integrated snow signal over several hectares, allowing for continuous estimations of SWE that are less influenced than smaller scale observations by the significant spatial heterogeneity of prairie snow. Initial results show that CRNS effectively quantifies an integrated SWE signal at the study site (R2 ≥ 0.90).  Interferometric UAVSAR products and drone flights provide complementary high resolution snow information for narrow time periods that effectively identify snow presence across areas with different crop types (wheat, barley, peas) and stubble heights (0-0.6 m) . The limited number of UAVSAR flights in 2021 preclude a full season or multi-year analysis. However our integrated sensing approach and analysis provides a framework to reduce uncertainty in future efforts, and better constrain measurements from the upcoming L-band NISAR mission that is expected to be launched in January 2024.   

How to cite: Sproles, E. A., Palomaki, R. T., Woodley, M., and Tuttle, S. E.: An integrated remote-sensing approach for prairie snowpack, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16822,, 2023.