Using data collected by the Hyperspectral Imager for the Coastal Ocean
(HICO) on the International Space Station between 2010–2014,
hyperspectral reflectance spectra of various floating matters in global oceans and
lakes are derived for the spectral range of 400–800 nm. Specifically, the
entire HICO archive of 9411 scenes is first visually inspected to identify
suspicious image slicks. Then, a nearest-neighbor atmospheric correction
is used to derive surface reflectance of slick pixels. Finally, a spectral
unmixing scheme is used to derive the reflectance spectra of floating
matters. Analysis of the spectral shapes of these various floating matters
(macroalgae, microalgae, organic particles, whitecaps) through the use of a
spectral angle mapper (SAM) index indicates that they can mostly be
distinguished from each other without the need for ancillary information.
Such reflectance spectra from the consistent 90 m resolution HICO
observations are expected to provide spectral endmembers to differentiate
and quantify the various floating matters from existing multi-band satellite
sensors and future hyperspectral satellite missions such as NASA's Plankton,
Aerosol, Cloud, ocean Ecosystem (PACE) mission; Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) mission; and Surface Biology and
Geology (SBG) mission. All spectral data are available at
Since the debut of the first proof-of-concept Coastal Zone Color Scanner
(CZCS, 1978–1986), satellite ocean color missions have evolved from the
original goal of mapping phytoplankton biomass and primary production to
many other applications. Because of improved spectral resolution and
instrument sensitivity, mapping various types of floating matters has also become
possible (IOCCG, 2014). These floating matters range from living to
non-living, including
Currently, mapping floating matters using optical remote sensing requires
the detection of a spatial anomaly using the near-infrared (NIR) bands and
then discrimination of the anomaly by comparing its spectral characteristics
with known spectra of floating matters (Qi et al., 2020) or by using
ancillary information (e.g., in certain regions a spatial anomaly can only
be caused by a certain type of floating algae). Spectral discrimination
requires the knowledge of spectral signatures of various floating matters.
However, despite scattered laboratory or field measurements of certain types of floating matters, hyperspectral data of these floating matters are mostly
unavailable. Although medium-resolution (300 m) sensors such as the Ocean
and Land Colour Imager (OLCI) have been used to show spectral variations in
floating matters (Qi et al., 2020), the data are not hyperspectral;
therefore certain spectral features may have been missed. For example,
various pigments (e.g., chlorophyll
Data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on
the International Space Station (ISS) may serve this purpose. HICO has 128
bands covering a spectral range of 353–1080 nm. From its entire mission
of 2010–2014, a total of
The primary objective of this paper is to derive HICO-based hyperspectral reflectance of various floating matters. This requires customized atmospheric correction and pixel unmixing to account for the small proportion of floating matters within an image pixel. From such derived spectra, a secondary objective is to analyze whether they can be differentiated spectrally. Similarly to the compiled hyperspectral dataset for inherent and apparent optical properties to support future hyperspectral missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission (Casey et al., 2020), such a dataset for floating matters is expected to help develop or improve algorithms for the PACE mission as well as for the hyperspectral Surface Biology and Geology (SBG) mission currently being planned by NASA (Cawse-Nicholson et al., 2021).
HICO Level-1B (calibrated radiance) data were obtained from the NASA Goddard
Space Flight Center (
Step 1 is to generate quick-look red–green–blue (RGB) and false-color RGB
(FRGB) images with Rayleigh-corrected reflectance (
Step 2 is to determine image slicks through visual inspection of both RGB and FRGB images. Figure 1a shows an FRGB image captured in the central western Atlantic, where an elongated greenish slick is identified.
Demonstration of how surface reflectance of floating matter
(
Step 3 is to derive surface reflectance (
The final step, Step 4, is to perform spectral unmixing of
Once
The approach above was applied to the visually identified image slicks to
derive
Surface reflectance (
Surface reflectance (
Surface reflectance (
Surface reflectance (
Of all spectra presented in Figs. 2–4, one common feature for all
floating macroalgae and microalgae (except red
There are several assumptions used in the nearest-neighbor atmospheric
correction and spectral unmixing (Eq. 4). Violations of these assumptions
will cause errors in the derived
Another uncertainty source can come from the assumption of linear mixing
between floating matters and water (Eq. 3). For macroalgae, linear
mixing up to the reflectance saturation level has been shown in laboratory
experiments (Hu et al., 2017; Wang et al., 2018). As long as the
macroalgae stay on the very surface of the water (as opposed to being submerged
under the surface), this assumption should be valid not just for macroalgae
but for all floating matters. For the same reason, if certain portions of
kelp are submerged in water, large uncertainties may result from the linear
unmixing scheme. Under high-wind conditions, the strong mixing may result in
submerged algae (especially for microalgae), thus violating the linear
mixing rule. However, the cases presented in Figs. 2–5 were selected very
carefully to avoid high wind speed (
Additional uncertainties may come from the HICO radiometric calibration,
which affects
Indeed, with all these possible sources of uncertainty, such HICO-derived
Spectral discrimination can be performed through either visual inspection or
the use of a certain type of similarity index (e.g., SAM, Eq. 6). Here,
results of the SAM analysis are presented in Table 1, followed by
descriptions of visual inspection to interpret the spectral similarity or
difference. Because nearly all floating algae show typical red-edge
reflectance, discrimination of different algae types is focused on
wavelengths
Spectral angle mapper values (degrees) between different floating
matters for the spectral range of 450–670 nm, derived from the
HICO-derived and field-measured spectra shown in Figs. 2–4. An SAM of
0
Table 1 shows the SAM results for three types of macroalgae (
For each type of floating matter, HICO-derived
The results from the SAM table can also be explained through visual inspection and interpretation of the spectral shapes, as discussed below.
From Fig. 2, it is clear that although the three types of macroalgae all
share the same red-edge reflectance in the NIR, they have different spectral
shapes in the visible wavelengths. Unlike the
Similarly to the macroalgae, the microalgae scums also show elevated NIR
reflectance (Fig. 3), and their spectral shapes in the visible wavelengths makes it straightforward to distinguish between kinds (
Of all the microalgae scums of Fig. 3, the spectral shapes of red
The non-algae floating matters in Fig. 4 show spectral characteristics
different from both macroalgae and microalgae; for example they lack the
typical red-edge reflectance of vegetation and lack typical spectral
variations in the visible wavelengths due to pigment absorption. Within this
group, the organic matter of BSCs (Fig. 4a) and emulsified oil (Fig. 4b)
show some degrees of similarity as they also have monotonic reflectance
increases from a wavelength between 500–560 to at least 740 nm. The
difference between them is that BSC reflectance always starts to increase at
The inorganic “particles” (i.e., water bubbles, ice) also have distinctive spectral shapes. The examples in Fig. 4c indicate that submersed bubbles from ship wakes are similar in terms of spectral shapes, but all others are nearly identical in their lack of any narrow-band spectral features. Rather, foams, whitecaps, and ice all show flat reflectance spectral shapes between 400–800 nm that are consistent with in situ measurements of foams (Dierssen, 2019). The lack of narrow-band spectral features is similar to marine debris (Garaba and Dierssen, 2020). Such a similarity will make detection of marine debris very difficult, especially around ocean fronts because these are where surface materials tend to aggregate and foams also tend to form.
In addition to the spectra of Figs. 2–4 that can be well recognized, HICO
also showed reflectance spectra that are difficult to discriminate from
spectroscopy alone, as shown in Fig. 5. Without a known reflectance library,
one can only speculate what algae type could be responsible for the algae
scum spectra from some ancillary information in the literature. For example,
the often-reported blooms of
Because HICO is a pathfinder sensor that collected only a limited number of
scenes, not all reported floating matters have been captured. For example,
no HICO scene appears to have captured pumice rafts,
First, although all current multi-band sensors can detect floating matters through its elevated NIR reflectance (Qi et al., 2020), the Sentinel-3 Ocean and Land Colour Imager (OLCI) appears to be the best at differentiating spectral shapes in the visible wavelengths because of its 21 spectral bands between 400 and 1020 nm, especially because of its 620 nm band that can be used to differentiate whether an algae scum appears greenish or brownish, thus providing extra information to discriminate algae type in the absence of hyperspectral data.
Second, for the same reason, although only four bands (blue, green, red, NIR)
are available on the PlanetScope (Dove) constellation, the recent SuperDove
constellation is equipped with four additional bands with one centered at 610 nm and thus may significantly enhance the capacity of the current
high-resolution sensors (
Finally, the Ocean Color Instrument (OCI) on NASA's PACE mission, to be launched in 2023, will be the first of its kind to map global oceans with hyperspectral capacity (5 nm resolution between 340–890 nm, plus seven discrete bands from 940 to 2260 nm) with a nominal resolution of 1 km. Unlike HICO, OCI will cover global oceans and lakes every 1–2 d, thus providing unprecedented opportunities to detect, differentiate, and quantify various types of floating matters. The spectral reflectance data, derived from one sensor (HICO) with a stable calibration, may serve as a consistent dataset to help select the optimal bands for future applications once PACE data become available, for example, through the use of an SAM matrix as demonstrated in Table 1. Likewise, the SBG mission currently being planned by NASA is expected to have hyperspectral capacity between 380 and 2500 nm with a nominal resolution of 30 m (Cawse-Nicholson et al., 2021); such a mission will provide unprecedented opportunity to map various floating matters on a global scale, and the hyperspectral dataset developed here can help develop algorithms before its launch.
All HICO data used in this analysis are available at the NASA Ocean Biology
Distributed Active Archive Center (OB.DAAC,
Through customized atmospheric correction and spectral unmixing, hyperspectral reflectance spectra in the visible and NIR wavelengths of various floating matters have been derived from HICO measurements over global oceans and lakes.
The reflectance dataset shows distinguishable spectral shapes between floating algae (macroalgae and microalgae, such as
The contact author has declared that there are no competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
I thank NASA and the US Naval Research Laboratory for providing HICO data, thank
Lachlan McKinna for providing field-measured reflectance of
This research has been supported by the Earth Sciences Division of NASA (grant nos. 80NSSC21K0422, NNX17AF57G, 80NSSC20M0264, and 80LARC21DA002).
This paper was edited by François G. Schmitt and reviewed by Patrick Launeau and Qianguo Xing.