Benthic Classification and IOP Retrievals in Shallow Water Environments using MERIS Imagery

Authors

Rodrigo A. Garcia, University of Massachusetts
Zhongping Lee, University of Massachusetts
Brian B. Barnes, University of South FloridaFollow
Chuanmin Hu, University of South FloridaFollow
Heidi M. Dierssen, University of Connecticut
Eric J. Hochberg, Bermuda Institute of Ocean Sciences

Document Type

Article

Publication Date

11-2020

Keywords

MERIS, Great Bahama Bank, Inherent optical properties, Atmospheric correction, Bathymetry, Benthic classification

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.rse.2020.112015

Abstract

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 a_phy(443) (absorption coefficient of phytoplankton at 443 nm), of up to 95%, under-estimations of a_dg(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.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Remote Sensing of Environment, v. 249, article 112015

Scholar Commons Citation

Garcia, Rodrigo A.; Lee, Zhongping; Barnes, Brian B.; Hu, Chuanmin; Dierssen, Heidi M.; and Hochberg, Eric J., "Benthic Classification and IOP Retrievals in Shallow Water Environments using MERIS Imagery" (2020). Marine Science Faculty Publications. 2045.
https://digitalcommons.usf.edu/msc_facpub/2045