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Worldview-2 High Resolution Remote Sensing Image Processing for the Monitoring of Coastal Areas

Worldview-2 High Resolution Remote Sensing Image Processing
for the Monitoring of Coastal Areas
Francisco Eugenio1, Javier Martin1, Javier Marcello1and Juan A. Bermejo2
1Instituto Oceanografía y Cambio Global, Universidad de Las Palmas de G.C.,
Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
2Fundación Observatorio Ambiental Granadilla, Edificio Puerto-Ciudad 1B,
38001 Santa Cruz de Tenerife, Spain
The spectral information provided by the multispectral
Worldview-2 satellite increases the amount of spectral data
available, thereby improving the quality of coastal
environmental products. The atmospheric correction has
proven to be a very important step in the processing of
Worldview-2 high resolution images. On the other hand,
specular reflection of solar radiation on non-flat water
surfaces is a serious confounding factor for bathymetry and
benthic remote sensing classification in shallow-water
environments. This paper describes an optimal atmospheric
correction model, as well as an improved algorithm for sun-
glint removal based on combined physical and image
processing techniques. This way, the atmospheric
reflectance can be estimated and the effects from the
apparent reflectance leaving from the water surface and the
seafloor can be eliminated. Finally, using the corrected
multispectral data, we have implemented an efficient
physics-based method to obtain the remote bathymetry and a
supervised classification for benthic mapping.
Index Terms High resolution multispectral imagery,
atmospheric model, sun-glint, bathymetry, benthic mapping.
Remote spectral imaging of coastal areas can provide
valuable information for characterizing and monitoring
coastal waters. The use of multispectral imagery from
satellite sensors such as Thematic Mapper, MODIS
(Moderate Resolution Imaging Spectroradiometer),
SeaWiFS (Sea-viewing Wide Field-of-view Sensor), and
others has been established for many applications, including
the estimation of chlorophyll concentrations, suspended
matter and roughly water depth. With the advent of very
high resolution multispectral imaging sensors such as the
WordView 2 (WV2), there is the potential to retrieve much
more information. Applications include water quality
monitoring, benthic habitat mapping and remote bathymetry
in coastal areas. However, achieving these goals requires
overcoming a number of challenges.
Water-leaving radiance is very difficult to determine
accurately, as it is often small compared to reflected
radiance from sources such as atmospheric and water
surface scattering, and it is subject to uncertainties in the
sensor’s radiometric calibration.
Thus, the atmospheric correction has proven to be a
crucial aspect in the processing of high resolution images
that can affect subsequent steps in remote sensing
applications of satellite data. On the other hand, specular
reflection of solar radiation on non-flat water surfaces is a
serious confounding factor for bathymetry and, specially,
for benthic remote sensing mapping in shallow-water
This paper describes an optimal atmospheric correction
model and an improved algorithm for sun-glint removal
based on physical and image processing techniques. Those
methods have been applied to the multispectral WorldView-
2 (WV2 hereafter) channels to estimate atmospheric
reflectance and to remove the effects from the apparent
reflectance leaving from the water surface and the seafloor,
respectively. Finally, we have implemented an efficient
physics-based method to obtain the bathymetry of shallow
coastal waters and a minimum distance supervised
classification for benthic mapping, respectively.
In order to make reliable estimates of water quality
parameters, bathymetry and benthic mapping in the coastal
areas, accurate retrievals of water leaving reflectances are
required. In this context, the present operational atmospheric
correction algorithms work reasonably well over clear ocean
areas (‘Case 1’ waters), but gives inaccurate results over
brighter coastal waters (‘Case 2 waters). So, we have
implemented a multi-channel atmospheric correction
algorithm, specifically, the 6S (Second Simulation of a
Satellite Signal in the Solar Spectrum) atmospheric
correction method adapted to high resolution WorldView-2
multispectral satellite imagery.
2.1. WorldView-2 imagery
The WorldView-2 high-resolution commercial imaging
satellite was launched on October 8, 2009. The satellite is in
a nearly circular, sun-synchronous orbit with a period of
100.2 minutes at an altitude of approximately 770 km.
WorldView-2 acquires 11-bit data in nine spectral bands
covering panchromatic, coastal, blue, green, yellow, red, red
edge, NIR1, and NIR2. The spectral response of each band
is shown in Figure 1 [1].
This work relied on Ortho Ready Standard Worldview-
2 images. Images were monthly taken from August 2011. At
nadir, the collected nominal ground sample distance is 0.46
m (panchromatic) and 1.84 m (multispectral), however,
commercially available products are resampled to 0.5 m and
2.0 m (outside U.S.). The nominal swath width is 16.4 km.
The study area is in the south part of Tenerife Island
(Canary Islands), as shown in Figure 2. Granadilla area has
a water quality monitoring network in place for two years.
To evaluate the results generated by the atmospheric model,
we used ground-based spectral data collected by the
spectroradiometer Vis/NIR ASD FieldSpec 3 nearly
coincident with WorldView-2 satellite over flight (see
Figure 3 (a)).
2.2 Atmospheric correction model for high
resolution WorldView-2 multispectral imagery
The atmospheric correction algorithms to process remotely
sensed data from low resolution sensors (p.e. MODIS,
SeaWiFS, MERIS) were primarily designed for retrieving
water-leaving radiances in the visible spectral region over
deep ocean areas, where the water-leaving radiances are
close to zero. For turbid coastal environments and optically
shallow waters, water-leaving radiances may be
significantly greater than zero because of backscattering by
suspended materials in the water and bottom reflectance.
Hence, applications of the Case 1 algorithm to satellite
imagery acquired over turbid coastal waters often result in
negative water-leaving radiances over extended areas.
Therefore, improved atmospheric correction algorithms
must be developed for the remote sensing of Case 2 waters.
Figure 1. WV2 relative spectral radiance response (nm).
In this context, we decided to implement the 6S
(Second Simulation of a Satellite Signal in the Solar
Spectrum) atmospheric correction method adapted to high
resolution WorldView-2 multispectral imagery of
Granadilla area. 6S is an advanced radiative transfer code
designed to simulate the reflection of solar radiation by a
coupled atmosphere-surface system for a wide range of
atmospheric, spectral and geometrical conditions [2]. It
belongs to the group of procedures called atmospheric
correction for the process of removing the effects of the
atmosphere on the reflectance values of images taken by
satellite sensors. The code operates on the basis of an SOS
(successive orders of scattering) method and accounts for
the polarization of radiation in the atmosphere through the
calculation of the Q and U components of the Stokes vector.
This model predicts the reflectance ρ of objects at the
top of atmosphere (TOA) using information about the
surface reflectance and atmospheric conditions. The TOA
reflectance ( can be estimated using the following
 
  (1)
The minimum data set needed to run the 6S model is
the meteorological visibility, type of sensor, sun zenith and
azimuth angles, date and time of image acquisition, and
latitude-longitude of scene center. In this study we have
proceeded to correct the eight-band multispectral and
panchromatic band of WV2 by means of the 6S model,
defining the geometry of the satellite observation and
viewing angle. The true reflectance value ρλ is obtained
from the model output by the following expression,
     (2)
where ρλ is the corrected reflectance, is the sensed
radiance, xa, xb, and xc are the coefficients obtained from
the model
Figure 2. WorldView-2 image of the Granadilla area
(Canary Islands, Spain) acquired on February 18, 2012 and
overlaid in goggle map©.
Finally, in order to check the proper functioning of the
selected 6S atmospheric correction algorithm, ground-based
reflectance measurements were performed on a variety of
locations, with similar weather and lighting conditions.
Figure 3 (a) shows the WV2 image of Granadilla area where
in-situ radiometric test points were obtained. The results
achieved by 6S atmospheric correction techniques on WV2
image compared with ground-based reflectance
measurements, are presented in Figure 3 (b). As it can be
observed, the results show a great correlation between the
reflectivity values obtained by in-situ measurements and the
corresponding obtained by the eight multispectral satellite
channels through the 6S atmospheric model.
Figure 3. (a) Location of the in-situ test points on
WorldView-2 imagery of Granadilla area (February 2012)
and, (b) ground-based reflectance measurements (top) and
corresponding WorldView-2 multispectral 6S atmospheric
corrected reflectance (bottom).
Specular reflection of solar radiation on non-flat water
surfaces is a serious confounding factor for turbidity remote
sensing in shallow-water environments. Therefore, the
remote bathymetry and the mapping of benthic features can
be seriously impeded by the state of the water.
To overcome this challenge, experts could refer to
previous methods and models designed to take advantage of
the glint to compute surface characteristics (e.g., wave
height) or to remove glint contamination prior to estimating
water column constituents and optical properties (e.g.,
mapping shallow-water benthos). However, these methods
have been conceived for the open ocean, not for nearshore
shallow environments. Because of nearshore topography,
the assumption of monodirectionality of waves is generally
not valid. Moreover, open ocean algorithms are designed for
low-resolution data (1 km), where glint effects occur at a
scale much smaller than pixel dimensions. In this paper, we
propose a method based on combined physical principles
and image processing techniques for removal of sea surface
effects from high-resolution imagery in coastal
Glint Removal: Following the procedure suggested by
Hedley et al. (2005), one or more regions of the image are
selected where a range of sun-glint is evident, but where the
underlying spectral brightness would be expected to be
consistent (i.e., areas of deep water) [4]. For each visible
band all the selected pixels are included in a linear
regression of NIR brightness against the visible band
brightness. If the slope of this line for band i is bi, then the
reflectance (R) of all the pixels in the image can be
deglinted in band i by the application of the following
     (3)
The deglinting procedure was carried out with
atmospherically corrected WorldView2 multispectral
imagery, and only on images that had glint pixels that would
hinder the visibility of bottom features.
Unfortunately, the previous deglinting process, using
the expression (3), affects to the spectral content of the
image altering intensity and colors. To overcome this
inconvenience, the Histogram Matching technique is applied
to statistically equalize images after deglinting from the
original water reflectivity for each channel.
Given that not all the sensor bands capture precisely the
energy at the same time, a further improvement in the glint
removal algorithm has been performed consisting on the use
of an sliding window centered at singular points (foam of
the waves) where template matching techniques have been
applied over a reduced search area in order to eliminate the
small spatial misalignments between the bands. After this
improvement, the subtraction between the bands to be
corrected and the near infrared band can be reliably
performed. This new image processing technique removes
most of the noise after the deglinting process. Finally,
another improvement included is based on the elimination of
pixels achieving reflectance values above a threshold
adjusted for coastal waters. That way, the foam of the waves
or "whitecaps" can be removed and such pixels filled by
The results of a full deglinted image are shown in
Figure 4. This example presents a glinty Worldview-2
image from the Granadilla area, with a poor signal to noise
ratio in shallow waters, as shown in Figure 4 (a). Before
incorporating the glint removal procedure, glint was a major
problem when trying to classify the bottom. After the glint
was removed, the bottom features became pronounced and
classification algorithms could be applied successfully
(Figure 4 (b)).
Figure 4. Results obtained after deglinting process: (a)
original WorldView-2 image of the Granadilla area and, (b)
image after deglinting using equation (3).
After atmospheric and glinting corrections of WorldView-2
multispectral imagery of Granadilla area, remote bathymetry
and benthic mapping of shallow-water environments can be
obtained with high resolution and precision. For bathymetry,
an efficient multichannel physics-based algorithm has been
implemented, capable of solving the radiative transfer
physical model equation of seawater.
Using the radiative model to compute bathymetry has
yielded good results as it considers the physical phenomena
of water absorption and the relationship between the albedo
of the seafloor and the reflectivity of the shallow waters.
Thus, the radiative modeling allows us to calculate the
albedo of the seafloor [5]. This achievement is of
fundamental importance for the classification of benthic
species. The model is given by equation,
        (4)
where R(0-,λ) is the reflectivity of the water inner surface.
R (0-,λ) is the reflectivity of the deep water inner surface.
Rb (0-,λ) is the seafloor albedo or reflectivity. Kd is the
diffuse attenuation coefficient and Z is the depth. The results
for Granadilla region are displayed in Figure 5.
(a) (b)
Figure 5. (a) WorldView-2 atmospheric and sun-glint
corrected imagery, (b) seafloor albedo and, (c) map of
estimated depth (bathymetry) for the Granadilla area.
For the mapping of benthic features, a supervised
classification of benthic indexes has been carried out. The
training classes were clearly defined (see bottom of Fig. 5)
and a detailed separability assessment was conducted using
the Jeffries-Matusita and the Transformed Divergence
metrics. In our context the supervised classification methods
used was the minimum distance. This technique uses the
mean vectors for each class and calculates the Euclidean
distance from each unknown pixel to the mean vector for
each class [6]. The pixels are classified to the nearest class,
     (5)
where D is the Euclidean distance; i the ith class; x is the n-
dimensional data (where n is the number of bands) and mi is
the mean vector of a class. Figure 6 provides the results of
the benthic classification. Results were validated and are
consistent with available bionomic profiles.
Figure 6. Classification map of shallow-water benthos of
the Granadilla area, obtained by minimun distance
supervised classification.
Coastlines, shoals and reefs are some of the most dynamic
and constantly changing regions of the globe. Monitoring
and measuring these changes is critical to marine navigation
and an important tool in understanding our environment.
This work has demonstrated the application of very
high resolution multispectral imagery to remote bathymetry
and benthic mapping in the shallow-water environments.
The results include depth maps and bottom visualizations.
As part of this effort, atmospheric correction in the littoral
zone was advanced through new capabilities added to the 6S
atmospheric correction method. For evaluating atmospheric
correction we compared the 6S model with coincident
ground-based reflectance measurements in the area under
study areas obtaining a very good correlation between the
reflectivity values obtained by in-situ measurements and the
corresponding acquired by atmospheric processing of the
eight multispectral satellite channels.
Specular reflection of solar radiation on non-flat water
surfaces is a serious factor that impedes the proper
estimation of water quality parameters, as well as the
bathymetry and the mapping of benthic features. Therefore,
an improved and robust methodology to remove glint
contamination has been included. This procedure exploits
physical information but it is also relies on image processing
algorithms to achieve the maximum performance.
After atmospheric and glinting corrections of
WorldView-2 multispectral imagery, bathymetry and
benthic mapping of shallow-water environments can be
obtained with high resolution and precision. For bathymetry,
an efficient multichannel physics-based algorithm has been
implemented while for the mapping of benthic features, a
supervised classification of benthic indexes has been carried
out. Results have been validated with in-situ data providing
an excellent accuracy.
This work has been supported by the Observatorio
Ambiental Granadilla (OAG) Contract ULPGC-OAG-FULP
240/142/3. Ground-based measurements were supported by
the project MICINN CGL2010-22189-C02 (GOTA-ULL).
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[3] Hedley, J.D., Harborne, A.R. and Mumby, P.J., “Simple
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bentos,” International Journal of Remote Sensing, Vol. 26,
Nº 10, pp. 2107-2112, May 2005.
[4] Maritorena S., Morel A. and B. Gentilly B., " Diffuse
reflectance of oceanic shallow waters: influence of water
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[5] Lyzenga, D.R.; Malinas, N.P.; Tanis, F.J.; ,
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[6] Robert, A. Schowengerdt., Remote Sensing. Models and
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... Integrated spatial and spectral processing techniques were identified as an alternative method for mapping benthos types, extent, and density from WV2 satellite images of the Canary Islands coastal area [12]. A combination of water column correction, seafloor-type normalized indexes, and supervised classification techniques provided the benthic habitat map with high spatial detail and accuracy. ...
... There are various types of benthic-habitat-mapping techniques based on satellite imagery and remote sensing data, which have been proved to be effective [27], [28]; however, results of high-resolution imagery-based benthos classification are affected by water column effects. In the water column, the dynamic inherent optical properties of light absorption and scattering change the spectral response of seafloor features over space and time, complicating the benthic mapping process [10]- [12]. Absorption and scattering of light in the water column are caused not only by suspended materials (i.e., during the works of the port of Granadilla); the water itself exponentially decays light intensity with increased water depth (light attenuation). ...
Full-text available
Coastlines, shoals, and reefs are some of the most dynamic and constantly changing regions of the globe. The emergence of high-resolution satellites with new spectral channels, such as the WorldView-2, increases the amount of data available, thereby improving the determination of coastal management parameters. Water-leaving radiance is very difficult to determine accurately, since it is often small compared to the reflected radiance from other sources such as atmospheric and water surface scattering. Hence, the atmospheric correction has proven to be a very important step in the processing of high-resolution images for coastal applications. On the other hand, specular reflection of solar radiation on nonflat water surfaces is a serious confounding factor for bathymetry and for obtaining the seafloor albedo with high precision in shallow-water environments. This paper describes, at first, an optimal atmospheric correction model, as well as an improved algorithm for sunglint removal based on combined physical and image processing techniques. Then, using the corrected multispectral data, an efficient multichannel physics-based algorithm has been implemented, which is capable of solving through optimization the radiative transfer model of seawater for bathymetry retrieval, unmixing the water intrinsic optical properties, depth, and seafloor albedo contributions. Finally, for the mapping of benthic features, a supervised classification methodology has been implemented, combining seafloor-type normalized indexes and support vector machine techniques. Results of atmospheric correction, remote bathymetry, and benthic habitat mapping of shallow-water environments have been validated with in situ data and available bionomic profiles providing excellent accuracy.
... Coastal water quality in the form of water turbidity or Total Suspended Sediment (TSS) concentration has been widely studied across diverse geographical locations[11][12][13][14][15][16][17][18][19][20]by using a suite of remote sensing sensors such as, Landsat[21][22][23][24][25][26][27][28][29][30], MEdium Resolution Imaging Spectrometer (MERIS)[7,[31][32][33], MODerate resolution Imaging Spectroradiometer (MODIS)[16,17,20,29,[34][35][36][37][38][39][40][41][42][43][44], and Sea-viewing Wide Field-of-view Sensor (SeaWiFS)[13,[45][46][47][48][49]. In addition to these most commonly used and " free to ground " sensors, commercial high spatial resolution sensors such as Syst?m Pour l'Observation de la Terra (SPOT)[22,50,51], IKONOS[14]and WorldView-2 (WV2)[52]are also employed to map the TSS. The high spatial resolution commercial satellite sensors such as IKONOS, WV2, and GeoEye-1 can provide data at spatial resolutions of approximately 0.5 m?4.0 m with temporal resolutions of ~1?8 days[53]. ...
Full-text available
The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor's radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit.
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The evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation ( R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.
Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic (PAN) and multi-spectral (MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening (PS), principal component analysis (PCA), Gram-Schmidt (GS), and wavelet fusion (WF), are utilized to fuse MS and PAN images of GF-1. The results of subjective and objective evaluation methods application indicate that GS performs the best, followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.
Full-text available
Specular reflection of solar radiation on non-flat water surfaces is a serious confounding factor for benthic remote sensing in shallow-water environments. This problem was recently overcome by Hochberg et al., who provided an effective method for the removal of 'sun glint' from remotely sensed images by utilization of the brightness in a near-infrared (NIR) band. Application of the technique was shown to give an increase in the accuracy of benthic habitat classification. However, as presented, the method is sensitive to outlier pixels, requires a time-consuming masking of land and cloud, and is not formulated in a manner leading to ease of implementation. We present a revised version of the method, which is more robust, does not require masking and can be implemented very simply. The practical approach described here will hopefully expedite the routine adoption of this effective and simple technique throughout the aquatic remote sensing community.
Full-text available
A vector version of the 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) radiative transfer code (6SV1), which enables accounting for radiation polarization, has been developed and validated against a Monte Carlo code, Coulson's tabulated values, and MOBY (Marine Optical Buoy System) water-leaving reflectance measurements. The developed code was also tested against the scalar codes SHARM, DISORT, and MODTRAN to evaluate its performance in scalar mode and the influence of polarization. The obtained results have shown a good agreement of 0.7% in comparison with the Monte Carlo code, 0.2% for Coulson's tabulated values, and 0.001-0.002 for the 400-550 nm region for the MOBY reflectances. Ignoring the effects of polarization led to large errors in calculated top-of-atmosphere reflectances: more than 10% for a molecular atmosphere and up to 5% for an aerosol atmosphere. This new version of 6S is intended to replace the previous scalar version used for calculation of lookup tables in the MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric correction algorithm.
Full-text available
A simple method for estimating water depths from multispectral imagery is described and is applied to several IKONOS data sets for which independent measurements of the water depth are available. The methodology is based on a physical model for the shallow-water reflectance, and is capable of correcting for at least some range of water-quality and bottom-reflectance variations. Corrections for sun-glint effects are applied prior to the application of the bathymetry algorithm. The accuracy of the depth algorithm is determined by comparison with ground-truth measurements, and comparisons between the observed and calculated radiances are presented for one case to illustrate how the algorithm corrects for water-attenuation and/or bottom-reflectance variations
A vector version of the 6S radiative transfer (RT) code (6SV1), primarily used for atmospheric correction of satellite data (MODIS, AVHRR, etc.), was publicly released in May 2005. In addition to accounting for radiation polarization, several significant updates were introduced into 6SV1. These updates included (1) a more accurate calculation of highly asymmetric aerosol scattering phase functions, (2) an arbitrary variation of a vertical aerosol profile, (3) the ability to control the accuracy of RT simulations by changing the number of calculation angles and layers, (4) the increase in the number of node wavelengths from 10 to 20, and (5) an accurate simulation of non-spherical aerosol particles by using pre-computed spheroid kernels. By now, 6SV1 has been elaborately validated for a range of atmospheric, spectral, and geometrical conditions and its accuracy has been carefully assessed through the comparison with such well-known benchmarks as a Monte Carlo approach and Coulson's tabulated values. The obtained results have shown a good agreement of 0.7% for the Monte Carlo approach and 0.2% for Coulson's values. In addition, 6SV1 is part of a joint code comparison project which is planned to be completed by the end of 2006. Within this project, the performance of 6SV1 is intensively tested against the performance of other commonly used vector RT codes, such as RT3 and Dave vector. This vector version of 6S is gradually replacing the previous scalar version for the calculation of look-up tables in the MODIS atmospheric correction algorithm. Collection 5 of MODIS surface reflectances will be produced using 6SV1.
We used simplifying assumptions to derive analytical formulae expressing the reflectance of shallow waters as a function of observation depth and of bottom depth and albedo. These formulae also involve two apparent optical properties of the water body: a mean diffuse attenuation coefficient and a hypothetical reflectance which would be observed if the bottom was infinitely deep. The validity of these approximate formulae was tested by comparing their outputs with accurate solutions of the radiative transfer obtained under the same boundary conditions by Monte Carlo simulations. These approximations were also checked by comparing the reflectance spectra for varying bottom depths and compositions determined in coastal lagoons with those predicted by the formulae. These predictions were based on separate determinations of the spectral albedos of typical materials covering the floor, such as coral sand and various green or brown algae. The simple analytical expressions are accurate enough for most practical applications and also allow quantitative discussion of the limitations of remote-sensing techniques for bottom recognition and bathymetry.
This book is a completely updated, greatly expanded version of the previously successful volume by the author. The Second Edition includes new results and data, and discusses a unified framework and rationale for designing and evaluating image processing algorithms. Written from the viewpoint that image processing supports remote sensing science, this book describes physical models for remote sensing phenomenology and sensors and how they contribute to models for remote-sensing data. The text then presents image processing techniques and interprets them in terms of these models. Spectral, spatial, and geometric models are used to introduce advanced image processing techniques such as hyperspectral image analysis, fusion of multisensor images, and digital elevationmodel extraction from stereo imagery. The material is suited for graduate level engineering, physical and natural science courses, or practicing remote sensing scientists. Each chapter is enhanced by student exercises designed to stimulate an understanding of the material. Over 300 figuresare produced specifically for this book, and numerous tables provide a rich bibliography of the research literature.
Radiometric Use of WorldView-2 Imagery. Technical N
  • Todd Updike
  • Chris Comp
Todd Updike, Chris Comp., "Radiometric Use of WorldView-2 Imagery. Technical N.," Digital Globe, 2010.