2024-03-29T11:00:34Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/847772022-11-17T02:08:08Zhdl_2115_60500hdl_2115_60499A neural network-based method for satellite-based mapping of sediment-laden sea ice in the ArcticWaga, HisatomoEicken, HajoLight, BonnieFukamachi, YasushiArcticSea iceMachine learningRemote sensing450Sediment-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.ElsevierJournal Articlehttp://hdl.handle.net/2115/847770034-4257Remote Sensing of Environment2701128612022-03-01enginfo:doi/10.1016/j.rse.2021.112861none