气候变化领域集成服务门户(CCPortal): Improved machine-learning-based open-water-sea-ice-cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

CCPortal

DOI	10.5194/tc-15-1551-2021
	Improved machine-learning-based open-water-sea-ice-cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
	Paul S.; Huntemann M.
发表日期	2021
ISSN	19940416
起始页码	1551
结束页码	1565
卷号	15 期号:3
英文摘要	The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of (i) present clouds as sea ice or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual polynya area and subsequently derived information. Here, we present a novel machine-learning-based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20% in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44%through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-The-Art cloud-cover compensation methods. © 2021 American Institute of Physics Inc.. All rights reserved.
英文关键词	artificial neural network; cloud cover; ice thickness; land-sea interaction; machine learning; MODIS; polar region; satellite data; satellite imagery; satellite sensor; sea ice; snow cover; winter; Antarctica; Brunt Ice Shelf; East Antarctica
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202402
作者单位	Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany; Deutsches Geodätisches Forschungsinstitut (DGFI), Technical University of Munich, Munich, Germany; Department of Environmental Physics, University of Bremen, Bremen, Germany
推荐引用方式 GB/T 7714	Paul S.,Huntemann M.. Improved machine-learning-based open-water-sea-ice-cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery[J],2021,15(3).
APA	Paul S.,&Huntemann M..(2021).Improved machine-learning-based open-water-sea-ice-cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery.Cryosphere,15(3).
MLA	Paul S.,et al."Improved machine-learning-based open-water-sea-ice-cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery".Cryosphere 15.3(2021).