气候变化领域集成服务门户(CCPortal): Benthic classification and IOP retrievals in shallow water environments using MERIS imagery

CCPortal

DOI	10.1016/j.rse.2020.112015
	Benthic classification and IOP retrievals in shallow water environments using MERIS imagery
	Garcia R.A.; Lee Z.; Barnes B.B.; Hu C.; Dierssen H.M.; Hochberg E.J.
发表日期	2020
ISSN	00344257
卷号	249
英文摘要	Deriving inherent optical properties (IOPs) from multispectral imagery of shallow water environments using physics-based inversion models require prior knowledge of the spectral reflectance of the bottom substrate. The use of an incorrect bottom reflectance adversely affects the IOPs and, in part, the depth derived from inversion models. To date, an operational approach that determines the bottom reflectance from multispectral imagery is lacking; development in this area is especially paramount for locations that exhibit temporal variability in the spatial distributions of submerged aquatic vegetation and benthic microalgae. In this work, we develop a multispectral implementation of the HOPE-LUT algorithm (Hyperspectral Optimization Processing Exemplar with benthic Look Up Table), and apply the approach to MERIS imagery of the Great Bahama Bank (GBB). Overall benthic classification accuracy of this approach was 80.0%, revealing the areal coverage of benthic flora can range from 1052.3 km2 to 6169.3 km2 between years in the Exumas, GBB. Comparison of HOPE-LUT IOP retrievals to common inversion model implementations (particularly HOPE, with its default sand endmember) shows that using an incorrect bottom reflectance can lead to over-estimations in aphy(443) (absorption coefficient of phytoplankton at 443 nm), of up to 95%, under-estimations of adg(443) (absorption coefficient of detritus and gelbstoff) up to 50%, and over-estimations of depth up to 20%. In addition, the HOPE-LUT parameterizations generate IOPs within the range of those measured in situ. We demonstrate that, at the scale of a MERIS pixel, the dominant substrates of seagrass, unattached bottom macroalgae and benthic microalgae are spectrally unresolvable at the depths that these classes occur in the GBB. Lastly, we evaluate the performance of commonly used atmospheric corrections algorithms for bathymetry estimation and benthic classification accuracy. The combined benthic classification and inversion scheme presented here is autonomous, i.e., it does not require scene-specific thresholds or modifications. Thus, it should be portable to Sentinel 3 OLCI and potentially MODIS Aqua imagery to obtain a continuous time series of changes in IOPs and benthic cover for the shallow waters over the Great Bahama Bank. © 2020 Elsevier Inc.
英文关键词	Atmospheric correction; Bathymetry; Benthic classification; Great Bahama Bank; Inherent optical properties; MERIS
语种	英语
scopus关键词	Algae; Continuous time systems; Image classification; Microorganisms; Optical properties; Remote sensing; Substrates; Table lookup; Absorption co-efficient; Atmospheric corrections; Bathymetry estimations; Classification accuracy; Inherent optical properties (IOPs); Multi-spectral imagery; Shallow water environment; Submerged aquatic vegetations; Reflection; accuracy assessment; algorithm; bathymetry; benthos; image classification; MERIS; microalga; MODIS; optical property; satellite imagery; Sentinel; shallow water; spatial distribution; spectral reflectance; temporal variation; Atlantic Ocean; Great Bahama Bank
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/179168
作者单位	School For the Environment, University of Massachusetts Boston, Boston, MA 02125, United States; School of Molecular and Life Sciences, Curtin University, Bentley, WA 6845, Australia; College of Marine Science, University of South Florida, St Petersburg, FL, United States; Department of Marine Sciences and Geography, University of Connecticut, Groton, CT, United States; Bermuda Institute of Ocean Sciences, St.George's GE 01, Bermuda
推荐引用方式 GB/T 7714	Garcia R.A.,Lee Z.,Barnes B.B.,et al. Benthic classification and IOP retrievals in shallow water environments using MERIS imagery[J],2020,249.
APA	Garcia R.A.,Lee Z.,Barnes B.B.,Hu C.,Dierssen H.M.,&Hochberg E.J..(2020).Benthic classification and IOP retrievals in shallow water environments using MERIS imagery.Remote Sensing of Environment,249.
MLA	Garcia R.A.,et al."Benthic classification and IOP retrievals in shallow water environments using MERIS imagery".Remote Sensing of Environment 249(2020).