气候变化领域集成服务门户(CCPortal): Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods

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DOI	10.1016/j.rse.2021.112316
	Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods
	Liu H.; Li Q.; Bai Y.; Yang C.; Wang J.; Zhou Q.; Hu S.; Shi T.; Liao X.; Wu G.
发表日期	2021
ISSN	00344257
卷号	256
英文摘要	Particulate organic carbon (POC) plays vital roles in marine carbon cycle, serving as a part of “biological pump” moving carbon to the deep ocean. The blue-to-green band ratio algorithm is applied operationally to derive POC concentrations in global oceans; it, however, tends to underestimate high values in optically complex waters. With an attempt to develop accurate and robust oceanic POC models, this study aimed to explore machine learning methods in satellite retrieval of POC concentrations. Three machine learning methods, i.e. extreme gradient boosting (XGBoost), support vector machine (SVM) and artificial neural network (ANN), were tested, and the recursive feature elimination (RFE) method was employed to identify sensitive features. Matchups of global in situ POC measurements and Ocean Colour Climate Change Initiative (OC-CCI) products were used to train and evaluate POC models. Results showed that machine learning methods produced obvious better performance than the blue-to-green band ratio algorithm, and XGBoost was the most robust among the tested three machine learning methods. However, the blue-to-green band ratio algorithm still worked well for clear open ocean waters with low POC, and ANN was more effective for optically complex waters with extremely high POC. This study provided globally applicable methods for satellite retrieval of POC concentrations, which should be helpful for studying POC dynamics in global oceans as well as in productive marginal seas. © 2021 Elsevier Inc.
英文关键词	Climate change; Machine learning; Marine carbon; Ocean colour remote sensing
语种	英语
scopus关键词	Adaptive boosting; Climate change; Climate models; Complex networks; Neural networks; Oceanography; Organic carbon; Satellites; Support vector machines; Machine learning methods; Marine carbon cycle; Optically complex waters; Particulate organic carbon; Recursive feature elimination; Satellite retrieval; Sensitive features; Three machine learning methods; Learning systems; algorithm; artificial neural network; biological pump; carbon cycle; complexity; concentration (composition); global ocean; particulate organic carbon; performance assessment; satellite data; support vector machine
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/178935
作者单位	MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China; College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China; School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; Department of Geography, Hong Kong Baptist University, Hong Kong
推荐引用方式 GB/T 7714	Liu H.,Li Q.,Bai Y.,et al. Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods[J],2021,256.
APA	Liu H..,Li Q..,Bai Y..,Yang C..,Wang J..,...&Wu G..(2021).Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods.Remote Sensing of Environment,256.
MLA	Liu H.,et al."Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods".Remote Sensing of Environment 256(2021).