Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements

Wilder, Brenton A.; Meyer, Joachim; Enterkine, Josh; Glenn, Nancy F.

doi:https://doi.org/10.5194/tc-18-5015-2024

Articles | Volume 18, issue 11

https://doi.org/10.5194/tc-18-5015-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-18-5015-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 11

Research article

|

06 Nov 2024

Research article |

| 06 Nov 2024

Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements

Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn

Data sets

Copernicus Global Digital Elevation Model European Space Agency https://doi.org/10.5069/G9028PQB

3D Elevation Program 1-Meter Resolution Digital Elevation Model U.S. Geological Survey https://www.usgs.gov/the-national-map-data-delivery

SnowEx21 Senator Beck Basin and Grand Mesa, CO AVIRIS-NG Surface Spectral Reflectance, Version 1 M. Skiles and C. M. Vuyovich https://doi.org/10.5067/ZAI3M64WWN5V

Model code and software

cryogars/goshawk: GOSHAWK v1.0.5 (v1.0.5) Brent Wilder https://doi.org/10.5281/zenodo.13685440

Short summary

Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.