Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5015-2024
https://doi.org/10.5194/tc-18-5015-2024
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

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Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra
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EGUsphere, https://doi.org/10.5194/egusphere-2024-1681,https://doi.org/10.5194/egusphere-2024-1681, 2024
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Cited articles

Agenzia Spaziale Italiana (ASI): PRecursore IperSpettrale della Missione Applicativa [Hyperspectral Precursor and Application Mission], Agenzia Spaziale Italiana [data set], https://prisma.asi.it, last access: May 2023. 
Bair, E. H., Dozier, J., Stern, C., LeWinter, A., Rittger, K., Savagian, A., Stillinger, T., and Davis, R. E.: Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing, The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, 2022. 
Bair, E. H., Roberts, D. A., Thompson, D. R., Brodrick, P. G., Wilder, B. A., Bohn, N., Crawford, C. J., Carmon, N., Vuyovich, C. M., and Dozier, J.: Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1681, 2024. 
Bair, E. H., Stillinger, T., and Dozier, J.: Snow property inversion from remote sensing (SPIReS): A generalized multispectral unmixing approach with examples from MODIS and Landsat 8 OLI, IEEE T. Geosci. Remote , 59, 7270–7284, https://doi.org/10.1109/TGRS.2020.3040328, 2021. 
Bohn, N., Painter, T. H., Thompson, D. R., Carmon, N., Susiluoto, J., Turmon, M. J., and Guanter, L.: Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements, Remote Sens. Environ., 264, 112613, https://doi.org/10.1016/j.rse.2021.112613, 2021. 
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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.