Advantage of hyperspectral over multispectral remote sensing for aquatic environments
Argument: Not all bands (wavelengths) in hyperspectral data are useful. This has been clearly
shown by Lee and Carder (2002) through sensitivity analysis using in situ hyperspectral data.
They concluded that as compared to hyperspectral data, multi-spectral data (15-20 bands) are
equally effective in deriving most aquatic properties (absorption by phytoplankton, CDOM,
detritus, and particulate backscattering, and shallow-water bathymetry and bottom albedo) under
most circumstances. However, there are at least three facts that support hyperspectral sensors.
1. Band requirement for deriving aquatic properties
The sensitivity analysis by Lee and Carder (2002) used continuous bands from 400 to 800 nm.
This configuration is not available in any existing multi-band sensors. On the other hand, current
multi-band sensors (MODIS, MERIS, SeaWiFS, OCM, and others) are not optimized to capture
all spectral shape characteristics (as defined by all local minima and maxima as well as local
inflections in the spectra) from a variety of aquatic environments (open ocean, turbid water, coral
reef, river plume, etc.). Lee et al. (2007) used derivative analysis to recommend 17 discrete
spectral bands from 380 to 760 nm (Fig. 1, Table 1). Note that this recommendation has not
considered the atmospheric transmission “windows”, and therefore should be tuned accordingly.
It is clear that this recommended list of bands is not available in any existing or planned
multi-band sensors. In terms of engineering and cost, it is more costive and challenging to
have these discrete bands than to have a hyperspectral sensor from which these bands can
be extracted.
Fig. 1 Proposed bands (orange) to capture all information from a variety of aquatic reflectance
spectra. The result is based on 1st and 2nd derivative analysis of in situ hyperspectral data. These
bands will capture all spectral shape information from local minima and maxima as well as local
inflection. Figure adapted from Lee et al. (2007). The exact band selection is listed in Table 1.
1
Table 1. Proposed bands based on derivative analysis. See Fig. 1 for explanation. Table from Lee
et al. (2007).
1st derivative 2nd derivative
2
2. Band requirement for benthic classification and species (functional group) recognition
The above analysis did not consider distinguishing the various species (functional groups) in the
benthos or in the water column.
Hochberg et al. (2003) used hyperspectral data collected from a variety of shallow bottoms to
determine which bands are the most effective to recognize (classify) the various bottom features.
Fig. 2 shows that the required bands are different for different bottom types, and lack of spectral
information made Landsat-ETM+ incapable of differentiating corals from algae (Fig. 3).
LDF 2
Fig. 2. Occurrence frequency of 2nd derivative local maxima for the 12 bottom types (Hochberg
et al., 2003). Note that all bottom types exhibit features near 600 and 650 nm, and >90% of
brown hermatypic corals and soft/gorgonian corals have a feature near 570 nm while all algal
classes lack this feature. These wavelengths are missing in the recommended list in Fig. 1
and Table 2 which focused on other aquatic information.
LDF 1
LDF 1
Fig. 3. Linear Discrimination Function (LDF) scores for AVIRIS (hyperspectral) and LandsatETM+ (multispectral) in terms of distinguishing bottom types between coral (red), algae (green),
and sand (blue). Clearly, Landsat-ETM+ cannot distinguish coral from algae. Figure from
Hochberg and Atkinson (2003).
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Discrimination of phytoplankton functional groups in water column
2
-1
Specific Absorption Coefficient (m mg )
Because of the different spectral absorption shapes from the various phytoplankton pigments
(Fig. 4, Bidigare et al., 1989), hyperspectral absorption spectra of phytoplankton measured in the
laboratory have shown some promise in distinguishing the subtle spectral signatures from
different phytoplankton groups (Bidigare et al. 1989; Hoepffner and Sathyendranath 1993; Millie
et al. 1997), particularly from the derivative spectra. For example, using 4th derivative of the
absorption spectra Millie et al. (1997) found distinguishable spectral signatures of the toxic
Karenia brevis. Although there are a handful of studies to use multi-spectra data to recognize
major phytoplankton groups from space (e.g., Alvain et al., 2005), the same application using
hyperspectral data is scarce, possibly due to the lack of hyperspectral remote sensing data from
satellites. One such example is shown in Craig et al. (2006), where in situ hyperspectral
reflectance was used to derive hyperspectral absorption by phytoplankton, and then derive the 4th
derivative of the absorption spectra. The 4th derivative was then compared with a reference K.
brevis spectrum to derive a similarity index (SI). Fig. 5 shows that this remotely sensed SI is
highly correlated with K. brevis concentration. Similar (i.e., derivative) approach is impossible
with multi-spectral data.
0.08
Chl a
Chl b
Chl c
Photosynthetic carotenoid
Photoprotective carotenoid
Phycoerythrin #1
Phycoerythrin #2
0.06
0.04
0.02
0.00
400
500
600
700
Wavelength (nm)
Figure 4. Weight-specific absorption coefficients (m2 mg-1) derived for the major pigment types
found in marine phytoplankton (from Bidigare et al. 1989).
3. Research on the unknowns
During the past two decades we have gained substantial knowledge on the importance of each
individual band in the multi-spectral data in studying the aquatic environment. However, there
are always unknowns when a multi-spectral sensor is designed. For example, MODIS lacks of a
band between 700 and 710-nm that captures the fluorescence signal from intense phytoplankton
blooms, while MERIS does not have a band at 640 nm which is useful to derive shallow-water
bathymetry and total absorption in the red. Even if we can now select the individual bands to our
best knowledge, as our research progresses we may find additional bands useful, by when it is
too late. For example, while the MODIS band at 678-nm is useful to detect chlorophyll
fluorescence and therefore provides a unique value to distinguish phytoplankton blooms in
CDOM-rich waters (Hu et al., 2005), in sediment-rich waters its value is significantly degraded
because the high backscattering leads to a false fluorescence signal. In this type of waters,
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perhaps the best way to estimate biomass (chlorophyll concentration) is through derivative
analysis (e.g., Goodin et al., 1993), which is possible only with hyperspectral remote sensing.
Another example is the choice of the atmospheric correction bands. Only after the launch of
SeaWiFS, was it found that the use of the 670-nm as a replacement of the 765-nm band could
avoid noise caused by digitization and thin cirrus clouds (Hu et al., 2000, Fig. 6), and only after
the launch of MODIS was it found that the use of the short-wave infra-red (SWIR) bands could
effectively remove the atmospheric effect even over the most turbid waters (Wang and Shi,
2005).
In conclusion, because of the different band requirements for different applications, the best
approach, as long as engineering and cost permit, is to have a hyperspectral sensor.
Fig. 5. K. brevis (Florida’s red tide) remote
sensing using hyperspectral data from top left
clockwise: Remote sensing reflectance (Rrs)
spectra from in situ measurements;
Phytoplankton absorption spectra (a)
derived from Rrs; Similarity Index (SI)
derived from the 4th derivative of a, as
compared with in situ K. brevis
concentrations (cells ml-1).
5
Rrs (555 nm, sr-1) and _865
0.01
Rrs (555 nm)
_865/10
0.001
0
50
100
150
200
250
300
350
Day of the year (1997 - 1998)
Fig. 6. Results of different atmospheric corrections using SeaWiFS data. The retrieved Rrs(555)
for the oligotrophic gyre should approach the theoretical “true” value (the solid black line). The
empty circles are from the 765- and 865-nm atmospheric correction, while the stars are from the
670- and 865-nm atmospheric correction. The latter can effectively remove most of the errors
due to digitization noise and thin cirrus clouds (Hu et al., 2000).
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