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A neural network-based method for satellite-based mapping of sediment-laden sea ice in the Arctic

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Title: A neural network-based method for satellite-based mapping of sediment-laden sea ice in the Arctic
Authors: Waga, Hisatomo Browse this author →KAKEN DB
Eicken, Hajo Browse this author
Light, Bonnie Browse this author
Fukamachi, Yasushi Browse this author →KAKEN DB
Keywords: Arctic
Sea ice
Machine learning
Remote sensing
Issue Date: 1-Mar-2022
Publisher: Elsevier
Journal Title: Remote Sensing of Environment
Volume: 270
Start Page: 112861
Publisher DOI: 10.1016/j.rse.2021.112861
Abstract: Sediment-laden sea ice is a ubiquitous phenomenon in the Arctic Ocean and its marginal seas. This study presents a satellite-based approach at quantifying the distribution of sediment-laden ice that allows for more extensive observations in both time and space to monitor spatiotemporal variations in sediment-laden ice. A structural-optical model coupled with a four-stream multilayer discrete ordinates method radiative transfer model was used to examine surface spectral albedo for four surface types: clean ice, sediment-laden ice with 15 different sediment loadings from 25 to 1000 g m(-3), ponded ice, and ice-free open water. Based on the fact that the spectral characteristics of sediment-laden ice differ from those other surface types, fractions of sediment-laden ice were estimated from the remotely-sensed surface reflectance by a spectral unmixing algorithm using a least square method. Sensitivity analyses demonstrated that a combination of sediment loads of 50 and 500 g m(-3) effectively represents the areal fraction of sediment-laden ice with a wide range of sediment loads. The estimated fractions of each surface type and corresponding remotely-sensed surface reflectances were used to train an artificial neural network to speed up processing relative to the least squares method. Comparing the fractions of sediment-laden ice derived from these two approaches yielded good agreements for areal fractions of sediment-laden ice, highlighting the superior performance of the neural network for processing large datasets. Although our approach contains potential uncertainties associated with methodological limitations, spatiotemporal variations in sediment-laden ice exhibited reasonable agreement with spatial patterns and seasonal variations reported in the literature on in situ observations of sediment-laden ice. Systematic satellite-based monitoring of sediment-laden ice distribution can provide extensive, sustained, and cost-effective observations to foster our under-standing of the role of sediment-laden ice in a wide variety of research fields including sediment transport and biogeochemical cycling.
Type: article
Appears in Collections:北極域研究センター (Arctic Research Center) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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