A neural network-based method for satellite-based mapping of sediment-laden sea ice in the Arctic
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...
| Published in: | Remote Sensing of Environment |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Elsevier
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| Subjects: | |
| Online Access: | http://hdl.handle.net/2115/84777 https://doi.org/10.1016/j.rse.2021.112861 |
| Summary: | 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 ... |
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