气候变化领域集成服务门户(CCPortal): Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach

CCPortal

DOI	10.1016/j.rse.2019.111604
	Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach
	Pahlevan N.; Smith B.; Schalles J.; Binding C.; Cao Z.; Ma R.; Alikas K.; Kangro K.; Gurlin D.; Hà N.; Matsushita B.; Moses W.; Greb S.; Lehmann M.K.; Ondrusek M.; Oppelt N.; Stumpf R.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m3 and a mean Chla of 21.7 mg/m3, suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40–60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n < 800) for Sentinel-2A/B and -3A, we show that the MDN model provides most accurate products from recorded images processed via three different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER, and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products. This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by improving the global representativeness of the training data as well as simultaneous retrievals of multiple optically active components of the water column. © 2019 The Authors
英文关键词	Algorithm development; Chlorophyll-a; Earth observation; Inland and coastal waters; Machine learning; Sentinel missions; Water quality
语种	英语
scopus关键词	Aquatic ecosystems; Chlorophyll; Data handling; Errors; Learning algorithms; Learning systems; Remote sensing; Uncertainty analysis; Water quality; Algorithm development; Chlorophyll a; Coastal waters; Earth observations; Sentinel missions; Machine learning; atmospheric correction; chlorophyll a; coastal water; correction; data set; EOS; machine learning; numerical model; performance assessment; reflectance; remote sensing; SeaWiFS; Sentinel; uncertainty analysis; water quality
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/179410
作者单位	NASA Goddard Space Flight Center, Greenbelt, MD, United States; Science Systems and Applications, Inc. (SSAI), Lanham, MD, United States; Creighton University, Department of Biology, Omaha, NE, United States; Environment and Climate Change Canada, Burlington, ON, Canada; Nanjing Institute of Geography and Limnology, Nanjing, China; University of Tartu, Tartu, Estonia; Wisconsin Department of Natural Resources, Madison, WI, United States; VNU University of Science, Vietnam National University, Hanoi, Viet Nam; University of Tsukuba, Ibaraki, Japan; Naval Research Laboratory, Washington, DC, United States; University of Wisconsin-Madison, Space Science and Engineering, Madison, WI, United States; Xerra Earth Observation Institute and the University of Waikato, Hamilton, New Zealand; NOAA Center for Satellite Applications and Research, College Park, MD, United States; University of Kiel, Department of Geography, Kiel, Germany; NOAA National Center for Coastal Science Studies, Silver Spring, MD, United States
推荐引用方式 GB/T 7714	Pahlevan N.,Smith B.,Schalles J.,et al. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach[J],2020,240.
APA	Pahlevan N..,Smith B..,Schalles J..,Binding C..,Cao Z..,...&Stumpf R..(2020).Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach.Remote Sensing of Environment,240.
MLA	Pahlevan N.,et al."Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach".Remote Sensing of Environment 240(2020).