Preprints
https://doi.org/10.5194/tc-2023-126
https://doi.org/10.5194/tc-2023-126
29 Aug 2023
 | 29 Aug 2023
Status: a revised version of this preprint is currently under review for the journal TC.

Estimating differential penetration of green (532 nm) laser light over sea ice with NASA’s Airborne Topographic Mapper: observations and models

Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling

Abstract. Differential penetration of green laser light into snow and ice has long been considered a possible cause of range and thus elevation bias in laser altimeters. Over snow, ice, and water, green photons can penetrate the surface and experience multiple scattering events in the subsurface volume before being scattered back to the surface and subsequently the instrument’s detector, therefore biasing the range of the measurement. Newly formed sea ice adjacent to open water leads provides an opportunity to identify differential penetration without the need for an absolute reference surface or dual color lidar data. We use co-located, coincident high-resolution natural color imagery and airborne lidar data to identify surface and ice types and evaluate elevation differences between those surfaces. The lidar data reveal that apparent elevations of thin ice and finger-rafted thin ice can be several tens of cm below the water surface of surrounding leads. These lower elevations coincide with broadening of the laser pulse suggesting that subsurface volume scattering is causing the pulse broadening and elevation shift. To complement our analysis of pulse shapes and help interpret the physical mechanism behind the observed elevation biases, we match the waveform shapes with a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse, the surface roughness, and the optical scattering properties of the medium. We parameterize the scattering in our model based on the scattering length Lscat, the mean distance a photon travels between isotropic scattering events. The largest scattering lengths are found for thin ice that exhibits the largest negative elevation biases, where scattering lengths of several cm allow photons to build up considerable range biases over multiple scattering events, indicating that biased elevations exist in lower-level Airborne Topographic Mapper (ATM) data products. Preliminary analysis of ICESat-2 ATL10 data shows that a similar relationship between subsurface elevations (restored negative freeboard) and “pulse width” is present in ICESat-2 data over sea ice, suggesting that biased elevations caused by differential penetration likely also exist in lower-level ICESat-2 data products. The spatial correlation of observed differential penetration in ATM data with surface and ice type suggests that elevation biases could also have a seasonal component, increasing the challenge of applying a simple bias correction.

Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-126', Anonymous Referee #1, 25 Sep 2023
  • RC2: 'Comment on tc-2023-126', Anonymous Referee #2, 08 Dec 2023
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling

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Short summary
We use green lidar data and natural color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.