TY - JOUR
T1 - Benthic Classification and IOP Retrievals in Shallow Water Environments using MERIS Imagery
AU - Garcia, Rodrigo A.
AU - Lee, Zhongping
AU - Barnes, Brian B.
AU - Hu, Chuanmin
AU - Dierssen, Heidi M.
AU - Hochberg, Eric J.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - MERIS
KW - Great Bahama Bank
KW - Inherent optical properties
KW - Atmospheric correction
KW - Bathymetry
KW - Benthic classification
UR - https://digitalcommons.usf.edu/msc_facpub/2045
UR - https://doi.org/10.1016/j.rse.2020.112015
U2 - 10.1016/j.rse.2020.112015
DO - 10.1016/j.rse.2020.112015
M3 - Article
VL - 249
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -