气候变化领域集成服务门户(CCPortal): Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans

CCPortal

DOI	10.1016/j.rse.2020.112022
	Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans
	Li H.; He X.; Bai Y.; Shanmugam P.; Park Y.-J.; Liu J.; Zhu Q.; Gong F.; Wang D.; Huang H.
发表日期	2020
ISSN	00344257
卷号	249
英文摘要	With a revisit time of 1 h, spatial resolution of 500 m, and high radiometric sensitivity, the Geostationary Ocean Color Imager (GOCI) is widely used to monitor diurnal dynamics of oceanic phenomena. However, atmospheric correction (AC) of GOCI data with high solar zenith angle (>70°) is still a challenge for traditional algorithms. Here, we propose a novel neural network (NN) AC algorithm for GOCI data under high solar zenith angles. Unlike traditional NN AC algorithms trained by radiative transfer-simulated dataset, our new AC algorithm was trained by a large number of matchups between GOCI-observed Rayleigh-corrected radiance in the morning and evening and GOCI-retrieved high-quality noontime remote-sensing reflectance (Rrs). When validated using hourly GOCI data, the new NN AC algorithm yielded diurnally stable Rrs in open ocean waters from the morning to evening. Furthermore, when validated by in-situ data from three Aerosol Robotic Network-Ocean Color (AERONET-OC) stations (Socheongcho, Gageocho and Ieodo), the GOCI-retrieved Rrs at visible bands obtained using the new AC algorithm agreed well with the in-situ values, even under high solar zenith angles. Practical application of the new algorithm was further examined using diurnal GOCI observation data acquired in clear open ocean waters. Results showed that the new algorithm successfully retrieved Rrs for the morning and evening GOCI data. Moreover, the amount of Rrs data retrieved by the new algorithm was much higher than that retrieved by the standard AC algorithm in SeaDAS. Our proposed NN AC algorithm can not only be applied to process GOCI data acquired in the morning and evening, but also has the potential to be applied to process polar-orbiting satellite ocean color data at high-latitude ocean that also include satellite observation with high solar zenith angles. © 2020 Elsevier Inc.
英文关键词	Atmospheric correction; Geostationary satellite; High solar zenith angle; Neural network; Ocean color remote sensing
语种	英语
scopus关键词	Color; Geostationary satellites; Large dataset; Open Data; Orbits; Remote sensing; Aerosol robotic networks; Atmospheric corrections; Novel neural network; Polar-orbiting satellites; Radiometric sensitivity; Remote-sensing reflectance; Satellite observations; Solar zenith angle; Oceanography; AERONET; algorithm; atmospheric correction; GOCI; ocean color; open ocean; solar radiation; spatial resolution; spectral reflectance; zenith angle
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/179173
作者单位	Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China; Ocean College, Zhejiang University, Zhoushan, China; Department of Ocean Engineering, IIT Madras, Chennai, India; Korea Ocean Satellite Center, Korea Institute of Ocean Science&Technology, Busan, South Korea; Key Laboratory of Spectral Imaging Technology of CAS, Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi'an, China
推荐引用方式 GB/T 7714	Li H.,He X.,Bai Y.,et al. Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans[J],2020,249.
APA	Li H..,He X..,Bai Y..,Shanmugam P..,Park Y.-J..,...&Huang H..(2020).Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans.Remote Sensing of Environment,249.
MLA	Li H.,et al."Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans".Remote Sensing of Environment 249(2020).