Systems and Methods to Downscale Resolution of Coarse-Resolution Soil Moisture Data (EMT+VS Model)

 
Opportunity

Available for Licensing

IP Status

US PCT Patent Pending: WO 2017/070199 A1

Inventor

Jeffery Niemann

The figure shows an example application of the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model to the Reynolds Creek Watershed in Idaho, USA.  Part (a) shows the watershed elevation, and part (b) shows the vegetation cover (represented by the soil adjusted vegetation index or SAVI), which are both inputs to the model.  Part (c) shows the soil moisture pattern that is generated by the model using a single spatial average soil moisture for the whole watershed as the key input (along with the elevation and vegetation data).

 
At A Glance

Researchers at Colorado State University have developed a new method to increase resolution of soil moisture maps by incorporating fine-resolution information on topography, vegetation, and soil. Estimated soil moisture patterns are becoming more readily available at coarse (9 km – 50 km) to intermediate (500 m – 1 km) spatial resolutions. However many applications, such as water management and agriculture production, require finer resolutions (10-100m). It is now possible to estimate fine-scale variations in soil moisture with CSU’s Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model.

Licensing Director

Mandana Ashouri
Mandana.Ashouri@colostate.edu
970-491-7100

Reference No.: 15-027

Background

Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10 – 100 m grid cells).  Several methods use topographic data to downscale, but without the incorporation of vegetation and soil patterns, these methods are problematic and unreliable.

Advantages
  • Provides both estimated soil moisture patterns (most accurate values possible) and simulated soil moisture patterns (most realistic statistics possible)
  • Applicable to any selected date or hypothetical conditions
  • Rapid generation of results (no model spin-up)
  • Little specialized expertise required for use
  • Produces fine spatial resolutions (grid cells with 10-30 m linear dimension)
  • Large spatial extents (100 km by 100 km regions)
  • Applicable for data-limited environments (performs well without calibration to local observations)
  • Can accept additional data if data are abundant
  • Can reproduce time-varying soil moisture structures
Applications
  • Water management
  • Forestry
  • Infectious Disease
  • Military Tactics and Logistics
  • Mobility Assessments
  • Flood Forecasting
  • Landslide Prediction
  • Decrease training costs
  • Land Management

Last updated: April 2020