气候变化领域集成服务门户(CCPortal): OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors

CCPortal

DOI	10.1016/j.rse.2020.112236
	OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors
	Fan Y.; Li W.; Chen N.; Ahn J.-H.; Park Y.-J.; Kratzer S.; Schroeder T.; Ishizaka J.; Chang R.; Stamnes K.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	We introduce a new platform, Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART), for analysis of data obtained by satellite ocean color sensors. OC-SMART is a multi-sensor data analysis platform which supports heritage, current, and possible future multi-spectral and hyper-spectral sensors from US, EU, Korea, Japan, and China, including SeaWiFS, Aqua/MODIS, SNPP/VIIRS, ISS/HICO, Landsat8/OLI, DSCOVR/EPIC, Sentinel-2/MSI, Sentinel-3/OLCI, COMS/GOCI, GCOM-C/SGLI and FengYun-3D/MERSI2. The products provided by OC-SMART include spectral normalized remote sensing reflectances (Rrs values), chlorophyll_a (CHL) concentrations, and spectral in-water inherent optical properties (IOPs) including absorption coefficients due to phytoplankton (aph), absorption coefficients due to detritus and Gelbstoff (adg) and backscattering coefficients due to particulates (bbp). Spectral aerosol optical depths (AODs) and cloud mask results are also provided by OC-SMART. The goal of OC-SMART is to improve the quality of global ocean color products retrieved from satellite sensors, especially under complex environmental conditions, such as coastal/inland turbid water areas and heavy aerosol loadings. Therefore, the atmospheric correction (AC) and ocean IOP algorithms in OC-SMART are driven by extensive radiative transfer (RT) simulations in conjunction with powerful machine learning techniques.To simulate top of the atmosphere (TOA) radiances, we solve the radiative transfer equation pertinent for the coupled atmosphere-ocean system. For each sensor, we have created about 13 million RT simulations and comprehensive training datasets to support the development of the machine learning AC and in-water IOP algorithms. The results, as demonstrated in this paper, are very promising. Not only does OC-SMART improve the quality of the retrieved water products, it also resolves the negative water-leaving radiance problem that has plagued heritage AC algorithms. The comprehensive training datasets created using multiple atmosphere, aerosol, and ocean IOP models ensure global and generic applicability of OC-SMART. The use of machine learning algorithms makes OC-SMART roughly 10 times faster than NASA's SeaDAS platform. OC-SMART also includes an advanced cloud screening algorithm and is resilient to the contamination by weak to moderate sunglint and cloud edges. It is therefore capable of recovering large amounts of data that are discarded by other algorithms (such as those implemented in NASA's SeaDAS package), especially in coastal areas. OC-SMART is currently available as a standalone Python package or as a plugin that can be installed in ESA's Sentinel Application Platform (SNAP). © 2020 Elsevier Inc.
英文关键词	Atmospheric correction; Machine learning; OC-SMART; Ocean color; Ocean IOPs; Radiative transfer; Remote sensing
语种	英语
scopus关键词	Aerosols; Backscattering; Color; Data handling; Information analysis; Machine learning; NASA; Oceanography; Radiative transfer; Remote sensing; Satellites; Turing machines; Water absorption; Absorption co-efficient; Atmospheric corrections; Backscattering coefficients; Environmental conditions; Inherent optical properties (IOPs); Radiative transfer equations; Remote-sensing reflectance; Satellite ocean color sensors; Learning algorithms; absorption; aerosol; algorithm; Aqua (satellite); atmospheric correction; backscatter; Landsat; machine learning; MODIS; ocean color; satellite data; satellite sensor; SeaWiFS; Sentinel; VIIRS; China; Japan; Korea; United States
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/179003
作者单位	Light & Life Laboratory, Department of Physics, Stevens Institute of Technology, Hoboken, NJ 07307, United States; Korea Institute of Ocean Science and Technology, Korea Ocean Satellite Center, Busan, 49111, South Korea; Department of Ecology, Environment and Plant Sciences (DEEP), Stockholm University, Stockholm, 106 91, Sweden; CSIRO Ocean & Atmosphere, Brisbane, QLD 4001, Australia; Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan; Union County Magnet High School, Scotch Plains, NJ 07076, United States
推荐引用方式 GB/T 7714	Fan Y.,Li W.,Chen N.,et al. OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors[J],2021,253.
APA	Fan Y..,Li W..,Chen N..,Ahn J.-H..,Park Y.-J..,...&Stamnes K..(2021).OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors.Remote Sensing of Environment,253.
MLA	Fan Y.,et al."OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors".Remote Sensing of Environment 253(2021).