18 Jan 2022
18 Jan 2022
Status: a revised version of this preprint is currently under review for the journal TC.

A sensor-agnostic albedo retrieval method for realistic sea ice surfaces – Model and validation

Yingzhen Zhou1, Wei Li1, Nan Chen1, Yongzhen Fan2, and Knut Stamnes1 Yingzhen Zhou et al.
  • 1Light and Life Laboratory, Department of Physics, Stevens Institute of Technology, Hoboken, NJ 07307, USA
  • 2Cooperative Institute for Satellite Earth System Studies (CISESS), Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20740, USA

Abstract. The cryosphere's surface (snow, sea ice, and water) regulates global climate through several feedback mechanisms. Broadband albedo is a critical parameter determining the radiative energy balance of the complex atmosphere-cryosphere system, but there is currently no reliable, operational albedo retrieval product capable of assessing the global sea-ice albedo with sufficient spatial-temporal resolution for studies of sea-ice dynamics and for use in global climate models.

A framework was established for remote sensing of sea ice albedo that integrates sea-ice physics with high computational efficiency, and can be applied to any optical sensor that measures appropriate radiance data. A scientific machine learning (SciML) approach was developed and trained on a large synthetic dataset (SD) constructed using a coupled atmosphere-surface radiative transfer model (RTM). The resulting RTM/SciML framework combines the RTM with a multi-layer artificial neural network SciML model. In comparison to the NASA MODIS MCD43 albedo product, this framework does not depend on observations from multiple days, and can be applied to single angular observations obtained under clear-sky conditions. Compared to the existing melt pond fraction-based approach for albedo retrieval, the RTM/SciML framework has the advantage of being applicable to a wide variety of cryosphere surfaces, both heterogeneous and homogeneous. Validation of the RTM/SciML albedo product using MODIS and SGLI data against measurements obtained from aircraft campaigns revealed excellent agreement, with mean absolute error of 0.047 for above 2000 clear-sky pixels.

Yingzhen Zhou et al.

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-2021-397', Anonymous Referee #1, 09 Feb 2022
  • RC2: 'Comment on tc-2021-397', Anonymous Referee #2, 13 Feb 2022
  • RC3: 'Comment on tc-2021-397', Anonymous Referee #3, 06 Mar 2022
  • RC4: 'Comment on tc-2021-397', Anonymous Referee #4, 07 Mar 2022

Yingzhen Zhou et al.

Yingzhen Zhou et al.


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Short summary
We present a method to compute albedo (percentage of the light reflected) of the cryosphere surface using observations from any optical satellite sensors. This method surpasses all existing models, may be applied globally (Arctic, Antarctic, Sea of Okhotsk) and to any realistic cryosphere surface: sea ice, snow-covered ice, melt-pond, open-ocean, and their mixing. Evaluation of the albedo values calculated using the approach demonstrated excellent agreement with observations.