A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation
A framework was established for remote sensing of sea ice albedo that integrates sea ice physics with high computational efficiency and that can be applied to optical sensors that measure appropriate radiance data. A scientific machine learning (SciML) approach was developed and trained on a large s...
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ftcopernicus:oai:publications.copernicus.org:tc100518 2023-05-15T18:17:29+02:00 A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation Zhou, Yingzhen Li, Wei Chen, Nan Fan, Yongzhen Stamnes, Knut 2023-03-03 application/pdf https://doi.org/10.5194/tc-17-1053-2023 https://tc.copernicus.org/articles/17/1053/2023/ eng eng doi:10.5194/tc-17-1053-2023 https://tc.copernicus.org/articles/17/1053/2023/ eISSN: 1994-0424 Text 2023 ftcopernicus https://doi.org/10.5194/tc-17-1053-2023 2023-03-06T17:23:09Z A framework was established for remote sensing of sea ice albedo that integrates sea ice physics with high computational efficiency and that can be applied to optical sensors that measure 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 contrast to the Moderate Resolution Imaging Spectroradiometer (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 detection (MPD)-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. Excellent agreement was found between the RTM–SciML albedo retrieval results and measurements collected from airplane campaigns. Assessment against pyranometer data ( N =4144 ) yields RMSE = 0.094 for the shortwave albedo retrieval, while evaluation against albedometer data ( N =1225 ) yields RMSE = 0.069, 0.143, and 0.085 for the broadband albedo in the visible, near-infrared, and shortwave spectral ranges, respectively. Text Sea ice Copernicus Publications: E-Journals The Cryosphere 17 2 1053 1087 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
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English |
description |
A framework was established for remote sensing of sea ice albedo that integrates sea ice physics with high computational efficiency and that can be applied to optical sensors that measure 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 contrast to the Moderate Resolution Imaging Spectroradiometer (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 detection (MPD)-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. Excellent agreement was found between the RTM–SciML albedo retrieval results and measurements collected from airplane campaigns. Assessment against pyranometer data ( N =4144 ) yields RMSE = 0.094 for the shortwave albedo retrieval, while evaluation against albedometer data ( N =1225 ) yields RMSE = 0.069, 0.143, and 0.085 for the broadband albedo in the visible, near-infrared, and shortwave spectral ranges, respectively. |
format |
Text |
author |
Zhou, Yingzhen Li, Wei Chen, Nan Fan, Yongzhen Stamnes, Knut |
spellingShingle |
Zhou, Yingzhen Li, Wei Chen, Nan Fan, Yongzhen Stamnes, Knut A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
author_facet |
Zhou, Yingzhen Li, Wei Chen, Nan Fan, Yongzhen Stamnes, Knut |
author_sort |
Zhou, Yingzhen |
title |
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
title_short |
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
title_full |
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
title_fullStr |
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
title_full_unstemmed |
A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
title_sort |
sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-1053-2023 https://tc.copernicus.org/articles/17/1053/2023/ |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-17-1053-2023 https://tc.copernicus.org/articles/17/1053/2023/ |
op_doi |
https://doi.org/10.5194/tc-17-1053-2023 |
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The Cryosphere |
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17 |
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1053 |
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1087 |
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1766191726015283200 |