Method to Improve the Spatial Resolution of Soil Moisture Data

Leveraging topographic, vegetative, and soil information to refine soil moisture data resolution.

At a Glance

Researchers at Colorado State University have developed a downscaling method that increases the spatial resolution of soil moisture maps by incorporating fine-resolution information on topography, vegetation, and soil patterns. This tool can provide estimated soil moisture patterns (most accurate values possible) and simulated soil moisture patterns (most realistic statistical properties).

Background

Soil moisture can be estimated by satellites over large regions with spatial resolutions greater than 9 km, but many applications require finer resolutions (3 – 30 m grid cells). These applications include water management, vehicle mobility analyses, and agriculture production. While several downscaling methods are available in the scientific literature, they are often poorly tested and have significantly limitations on their implementation (e.g., they can only be applied when skies are clear or when substantial computational resources are available).

Overview

The Equilibrium Moisture from Topography Plus Vegetation and Soil (EMT+VS) method downscales coarse-resolution soil moisture maps to produce fine-resolution maps using fine-resolution information for topography, vegetation, and soil. The method accomplishes this task using mathematical descriptions of the physical processes that control soil moisture such as infiltration, evapotranspiration, deep drainage, and lateral redistribution. The spatial variability of each process is inferred from the topographic, vegetation, and soil datasets. The patented approach allows the soil moisture to be estimated in a way that does not require the model to be simulated through time (any individual date can be downscaled independent of other dates).

Benefits

  • Can accurately estimate soil moisture and simulate realistic soil moisture patterns
  • Applicable to any selected date or hypothetical conditions
  • Rapid generation of results
  • Little specialized expertise required to use this tool means low training costs
  • Produces fine resolutions (grid cells with 3 – 30 m linear dimension)
  • Can be used for large regions (100 x 100 km)
  • Applicable for data-limited environments (performs well without calibration to local observations)
  • Can accept additional data if data are abundant
  • Can reproduce time-varying spatial structures of soil moisture.

Applications

  • Agriculture
  • Land and Water Management
  • Forestry
  • Infectious Disease
  • Military Tactics and Logistics
  • Mobility Assessments
  • Environmental Forecasting (e.g., flood and landslide prediction)

Publications

Niemann, et al (2020) “Stochastic analysis and probabilistic downscaling of soil moisture in small catchments.” Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.124711

Niemann, et al (2017) “Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief.” Water Resources Research. https://doi.org/10.1002/2016WR019907

Niemann et al (2015) “A method to downscale soil moisture to fine resolutions using topographic, vegetation, and soil data.” Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2014.12.003

Last Updated: January 2024
Opportunity

Available for Exclusive Licensing
TRL: 7

IP Status

US Patent: US 11/041,841
US Patent: US 17/308,549

Inventors

Jeffery Niemann

Reference Number
15-027
Licensing Manager

Jessy McGowan
Jessy.McGowan@colostate.edu
970-491-7100