气候变化领域集成服务门户(CCPortal): Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery

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DOI	10.1016/j.rse.2020.111919
	Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery
	Lee S.; Stroeve J.; Tsamados M.; Khan A.L.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Melt ponds on sea ice play an important role in the seasonal evolution of the summer ice cover. In this study we present two machine learning algorithms, one (multi-layer neural network) for the retrieval of melt pond binary classification and another (multinomial logistic regression) for melt pond fraction using moderate resolution visible satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). To minimize the impact of the anisotropic reflectance characteristics of sea ice and melt ponds, normalized MODIS band reflectance differences from top-of-the-atmosphere (TOA) measured reflectances were used. The training samples for the machine learning were based on MODIS reflectances extracted for sea ice, melt ponds and open water classifications based on high resolution (~2 m) WorldView (WV) data. The accuracy assessment for melt pond binary classification and fraction is further evaluated against WV imagery, showing mean overall accuracy (85.5%), average mean difference (0.09), and mean RMSE (0.18). In addition to cross-validation with WV, retrieved melt pond data are validated against melt pond fractions from satellite and ship-based observations, showing average mean differences (MD), root-mean-square-error (RMSE), and correlation coefficients (R) of 0.05, 0.12, and 0.41, respectively. We further investigate a case study of the spectral characteristics of melt ponds and ice during refreezing, and demonstrate an approach to mask out refrozen pixels by using yearly maps of melt onset and freeze-up data together with ice surface temperatures (IST). Finally, an example of monthly mean pan-Arctic melt pond binary classification and fraction are shown for July 2001, 2004, 2007, 2010, 2013, 2016, and 2019. Bulk processing of the entire 20 years of MODIS data will provide the science community with a much needed pan-Arctic melt pond data set. © 2020 Elsevier Inc.
英文关键词	Machine learning; Melt ponds; MODIS; Remote sensing; Sea ice
语种	英语
scopus关键词	Binary alloys; Lakes; Learning algorithms; Logistic regression; Mean square error; Multilayer neural networks; Network layers; Ponds; Radiometers; Reflection; Satellite imagery; Sea ice; Average mean differences; Correlation coefficient; Machine learning approaches; Moderate resolution imaging spectroradiometer; Multinomial logistic regression; Reflectance differences; Root mean square errors; Spectral characteristics; Machine learning; accuracy assessment; classification; correlation; error analysis; machine learning; melt; melting; model validation; MODIS; observational method; open water; satellite data; satellite imagery; visible spectrum; Arctic
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179259
作者单位	Centre for Polar Observation and Modelling, University College London, Earth Sciences, 5 Gower Place, London, WC1E 6BS, United Kingdom; Centre for Earth Observation Science, University of Manitoba, Winnipeg, MT MB R3T 2N2, Canada; National Snow and Ice Data Center (NSIDC), Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado, Boulder, CO 80309, United States; Department of Environmental Sciences, Huxley College of the Environment, Western Washington University, Washington, United States
推荐引用方式 GB/T 7714	Lee S.,Stroeve J.,Tsamados M.,et al. Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery[J],2020,247.
APA	Lee S.,Stroeve J.,Tsamados M.,&Khan A.L..(2020).Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery.Remote Sensing of Environment,247.
MLA	Lee S.,et al."Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery".Remote Sensing of Environment 247(2020).