Quantifying bias and uncertainty sources between laser and radar retrievals of surface topography over cryospheric targets

Funding Program: NASA Decadal Survey Incubation Program Science
Start Date: 15 June 2022
End Date: 14 June 2025
Collaborating Institutions: Washington University, St. Louis

Accurate quantification of the primary observables identified by the Surface Topography and Vegetation (STV) working group bare surface land topography, ice topography, vegetation structure, shallow water bathymetry, and snow depth requires geodetic measurements with sub-meter vertical resolution and sub-weekly temporal repeat-sampling. A multi-sensor fusion approach combining lidar, radar, and stereophotogrammetry from both orbital and suborbital platforms will likely be necessary to achieve these resolution requirements. However, a thorough investigation of the biases and uncertainties inherent to each of these geodetic measurements, as well as how these biases and uncertainties might compound in a multi-sensor fusion approach, is needed before any such multi-sensor framework can be developed and optimized.

In this project led by former Mines Glaciology Laboratory postdoctoral scholar Dr. Roger Michaelides, we investigate the sources of measurement bias and uncertainty in orbital laser altimetry, radar altimetry, and interferometric synthetic aperture radar (InSAR) measurements over ice sheet surfaces and periglacial land surfaces. These two surface types are subject to the time- and space-varying surface dielectric properties of snow, ice, and surface water, and subjected to seasonal and interannual geomorphic processes that can reshape surface topography (such as sub- or supra-glacial lake drainage at ice sheet boundaries and permafrost thaw and thermokarst initiation in periglacial regions), all of which complicate the error inherent in and interpretation of surface height measurements. We have three cross-cutting objectives:

  1. Characterize and reduce sources of uncertainty and biases in laser altimetry, radar altimetry, and InSAR measurements over ice sheet surfaces and periglacial land surfaces.
  2. Quantify the effect of these uncertainties on vertical and rate of change accuracies for both cryospheric and solid-earth/hydrologic targets.
  3. Develop and test novel multi-sensor data fusion methods and algorithms that leverage complementary geodetic measurements for improved topographic products and temporal-repeat measurements.
Matthew R. Siegfried
Matthew R. Siegfried
Assistant Professor

Assistant Professor, Department of Geophysics, Colorado School of Mines