气候变化领域集成服务门户(CCPortal): Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data

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DOI	10.1016/j.rse.2020.112035
	Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data
	Wei J.; Wang M.; Lee Z.; Briceño H.O.; Yu X.; Jiang L.; Garcia R.; Wang J.; Luis K.
发表日期	2020
ISSN	00344257
卷号	250
英文摘要	Polar-orbiting ocean color satellites such as Landsat-8, Suomi National Polar-orbiting Partnership (SNPP), and Sentinel-3 offer valuable image data for the derivation of water bathymetry in optically shallow environments. Because of the multi-spectral limitation, however, it is challenging to derive bathymetry over global shallow waters without reliable mechanistic algorithms. In this contribution, we present and test a physics-based algorithm for improved retrieval of bathymetry with multi-spectral sensors. The algorithm leverages the temporal variation of water-column optical properties in two satellite measurements. By incorporating two remote sensing reflectance spectra in an optimization procedure, it enhances the spectral constraining condition for the optimization, thus leading to improved retrieval accuracy. This scheme is evaluated using synthetic multi-spectral data. It is shown that the new approach can provide accurate estimation of water depths over 0–30 m range with three types of benthic substrates (corals, seagrass, and sand) and for a wide range of water column optical properties. Based on the degree of improvement, Landsat-8 appears to be benefited the most, followed by SNPP, and then Sentinel-3. The application of the new approach is demonstrated with satellite images over shallow waters (0–30 m) dominated with coral reefs, seagrass, and sand, respectively. This proof-of-concept study confirms the promise of multi-spectral satellite sensors for accurate water depth retrieval by accounting for the temporal characteristics in multiple measurements, suggesting a path forward for the derivation of bathymetry from the existing satellites over global shallow waters. © 2020 Elsevier Inc.
英文关键词	Bathymetry; Landsat-8; Remote sensing reflectance; Sentinel-3; Shallow water; SNPP; Spectral optimization; Temporal variation
语种	英语
scopus关键词	Bathymetry; Optical properties; Orbits; Plants (botany); Remote sensing; Satellites; Multiple measurements; Multispectral sensors; Ocean-color satellites; Optimization procedures; Remote-sensing reflectance; Satellite measurements; Shallow water bathymetry; Temporal characteristics; Hydrographic surveys; algorithm; bathymetric survey; Landsat; ocean color; satellite altimetry; satellite imagery; Sentinel; shallow water; spectral analysis; spectral reflectance; Suomi NPP; temporal variation; water column; Anthozoa
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/179155
作者单位	NOAA Center for Satellite Applications and Research, College Park, MD 20740, United States; Global Science & Technology, Inc., Greenbelt, MD 20770, United States; University of Massachusetts Boston, School for the Environment, Boston, MA 02125, United States; Florida International University, Southeast Environmental Research Center, Miami, FL 33199, United States; Xiamen University, State Key Laboratory of Marine Environmental Science, Xiamen, Fujian 361005, China; Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO 80523, United States; Curtin University, School of Molecular and Life Sciences, Bentley, WA 6845, Australia
推荐引用方式 GB/T 7714	Wei J.,Wang M.,Lee Z.,et al. Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data[J],2020,250.
APA	Wei J..,Wang M..,Lee Z..,Briceño H.O..,Yu X..,...&Luis K..(2020).Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data.Remote Sensing of Environment,250.
MLA	Wei J.,et al."Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data".Remote Sensing of Environment 250(2020).