Marine optical biogeochemistry: the chemistry of ocean color.

https://espace.library.uq.edu.au/view/UQ:330506/UQ330506_OA.pdf

A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS's Bathymetric Mapping Performance

[1] The present review describes progress in addressing and solving several fundamental and applied problems involving optical oceanography. These problems include: primary productivity, ecosystem dynamics, biogeochemical cycling, upper ocean heating, and the impacts of anthropogenic disturbances on ocean dynamics. Technological advances in optical sensors and ocean observing platforms are being used to increase the variety and quantity of optical observations and to greatly expand their sampling capabilities in time and space. Remote sensing of ocean color from aircraft- and satellite-borne instruments is vital to obtain regional- and global-scale optical data synoptically. In situ observations provide complementary subsurface data sets with high temporal and spatial resolution. In situ observations are also essential for calibration and validation of remotely sensed data as well as for algorithm development and data assimilation models. Important challenges remain to synthesize regional and global optical data sets obtained from optical sensors and oceanographic platforms and to utilize these data sets in predictive models of oceanic optical, physical, and biogeochemical dynamics.

Optical oceanography: Recent advances and future directions using global remote sensing and in situ observations

Using field measurements and quantitative modeling, we demonstrate that red coloration of the sea surface is not associated with any particular group of phytoplankton and is strongly dependent on the physiology of the human visual system. Red or brown surface waters can be produced by high concentrations of most types of algae, colored dissolved organic matter, or suspended sediment. Even though light reflected by red tides commonly peaks in the yellow spectral region (570–580 nm), human color perception requires consideration of the entire spectrum of light relative to receptors within the human eye. The color shift from green to red is not due to any special optical properties of the algae but results from an overlap in spectral response of the eye’s red and green cones (centered at 564 and 534 nm, respectively). The spectral peak in light reflected from dense algal blooms coincides with a critical hinge point in color vision (570–580 nm), where fine-scale shifts in the spectral shape of water-leaving radiance due to algal absorption and backscattering properties lead to pronounced variations in the observed color. Of the taxa considered, only Chlorophytes and Prochlorophytes lacked sufficient accessory pigments to produce a red tide. Chlorophyll fluorescence and enhanced near-infrared reflectance (the ‘‘red edge’’) contribute negligibly to the perceived color. Black water events are produced when water is highly absorbing but lacks backscattering constituents.

/pdf/red-and-black-tides-quantitative-analysis-of-water-leaving-3qc5fslwid.pdf

Red and black tides: Quantitative analysis of water-leaving radiance and perceived color for phytoplankton, colored dissolved organic matter, and suspended sediments

/pdf/the-new-age-of-hyperspectral-oceanography-378uezs5s9.pdf

The New Age of Hyperspectral Oceanography

We have developed a combined atmospheric-oceanographic spectral optimization solution decomposing measured
airborne radiance data from the passive spectrometer into environmental parameters of interest. In this model, we hold
depth measurements from the lidar as fixed constraints, thereby gaining a degree of freedom in the solution, and
extending the solution into deeper waters than achieved with passive data alone. In this paper, we illustrate results of the
data processing procedure and assess the accuracy of estimated IOPs (Inherent Optical Properties) parameters through
comparison to in-situ measurements.

A constrained optimization technique for estimating environmental parameters from CZMIL hyperspectral and lidar data

CZMIL will simultaneously acquire lidar and passive spectral data. These data will be fused to produce enhanced
seafloor reflectance images from each sensor, and combined at a higher level to achieve seafloor classification. In the
DPS software, the lidar data will first be processed to solve for depth, attenuation, and reflectance. The depth
measurements will then be used to constrain the spectral optimization of the passive spectral data, and the resulting water
column estimates will be used recursively to improve the estimates of seafloor reflectance from the lidar. Finally, the
resulting seafloor reflectance cube will be combined with texture metrics estimated from the seafloor topography to
produce classifications of the seafloor.

Active-passive data fusion algorithms for seafloor imaging and classification from CZMIL data

Integration of a bathymetric lidar and imaging spectrometer in CHARTS presented the challenge of developing new
algorithms and software for combining these two types of data. To support this development, we conducted several
field campaigns to collect airborne and in-situ data of the water column and seafloor. This work, sponsored by the
Office of Naval Research (ONR) led to development of the Rapid Environmental Assessment (REA) processor. REA
can be used to produce seafloor reflectance images from both sensors, and classification maps of the seafloor.
Ultimately, REA became the prototype software for CZMIL, and the CZMIL Data Processing System (DPS) has been
produced as a continuous refinement of REA. Here, we describe the datasets collected and illustrate results achieved
with the REA software.

Prelude to CZMIL: seafloor imaging and classification results achieved with CHARTS and the rapid environmental assessment (REA) processor

CZMIL is an integrated lidar-imagery sensor system and software suite designed for the highly automated generation of physical and environmental information products for mapping the coastal zone. This paper presents the results of CZMIL system validation in turbid water conditions on the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. The system performance test shows that CZMIL successfully achieved 7-8m depth in K d =0.46m -1 (K d is the diffuse attenuation coefficient) in Mississippi and up to 41m when K d =0.11m -1 in Florida. With a seven segment array for topographic mode and the shallow water zone, CZMIL generated high resolution products with a maximum pulse rate of 70 kHz, and with 10 kHz in the deep water zone. Diffuse attenuation coefficient, bottom reflectance and other environmental parameters for the whole multi km 2 area were estimated based on fusion of lidar and CASI-1500 hyperspectral camera data.

Coastal Zone Mapping and Imaging Lidar (CZMIL): first flights and system validation

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