Abstract:
Identifying pixels on the water surface natural water is an important step before applying a specific
algorithm to calculate water environment parameters from satellite imagery. A mistake can occur when
detecting clouds on turbid water (due to glare) and detecting their shadows on any surface water. Some
algorithms exist but their performance is unsatisfactory, especially on turbid waters where cloudless pixels
are sometimes classified as clouds or earth, resulting in data loss. This is especially important for the
satellite image sensing have high resolution, such as the observations made by the sensor ETM + on
Landsat-7, OLI on Landsat-8 or MSI on Sentinel- 2. In this study, we develop a two-step algorithm to
extract water pixels (called LS7WiPE) for the Landsat-7 / ETM + sensor based on our experience from the
WiPE algorithm for OLI and MSI sensors (Dat et al., 2019).
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ISSN 2354-0575
Journal of Science and Technology74 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
AUTOMATIC WATER SURFACE EXTRACTION ALGORITHM FOR
LANDSAT7-ETM+ SATELLITE IMAGE OVER COASTAL
AND INLAND WATERS
Dinh Ngoc Dat1,*, Pham Van Bach Ngoc1, Huynh Xuan Quang1,
Mai Thi Hong Nguyen1, Doan Minh Chung1, Bui Trung Thanh2
1 Institute of Space Technology, Vietnam Academy of Science and Technology, Cau Giay, Hanoi
2 Hung Yen University of Technology and Education, Dan Tien, Khoai Chau, Hung Yen
* Corresponding author: dndat@sti.vast.vn
Received: 15/05/2020
Revised: 20/08/2020
Accepted for publication: 25/09/2020
Abstract:
Identifying pixels on the water surface natural water is an important step before applying a specific
algorithm to calculate water environment parameters from satellite imagery. A mistake can occur when
detecting clouds on turbid water (due to glare) and detecting their shadows on any surface water. Some
algorithms exist but their performance is unsatisfactory, especially on turbid waters where cloudless pixels
are sometimes classified as clouds or earth, resulting in data loss. This is especially important for the
satellite image sensing have high resolution, such as the observations made by the sensor ETM + on
Landsat-7, OLI on Landsat-8 or MSI on Sentinel- 2. In this study, we develop a two-step algorithm to
extract water pixels (called LS7WiPE) for the Landsat-7 / ETM + sensor based on our experience from the
WiPE algorithm for OLI and MSI sensors (Dat et al., 2019).
Keywords: WiPE algorithm, Landsat-7/ETM+ satellite imagery, phân tích hình dạng quang phổ.
1. Introduction
Landsat 7 was launched from Vandenberg
airbase in California on April 15, 1999 with a Delta
II missile. The satellite carries a sensor called ETM
+ (sensor for specialized map quality improvement).
Like the OLI adjacent sensor, ETM + (Table 1) has
3 spectral channels in the visible region, 2 spectral
channels in the near infrared spectrum, while the
TIRS collects signals in two thermal infrared bands.
Recent studies have demonstrated the potential
of Landsat 5 / TM, ETM +, OLI for ocean color-
related applications where its average spatial
resolution (30m) provides detailed information
(Pahlevan and Schott, 2013; Vanhellemont and
Ruddick, 2014). Atmospheric correction algorithms
have been developed to retrieve the remote sensing
reflectance (Rrs( λ), where λ is the wavelength from
the measured atmospheric peak radiation (TOA)
signal by TM, ETM + or OLI (Franz et al, 2015;
Pahlevan et al, 2017).
Although there are differences in the sensitivity
of the radiation measurements other sensors to the
Landsat satellite stream, the evaluation of water
parameters, such as suspended particulate matter
concentration (SPM), disc depth Secchi, and
colored dissolved organic matter (CDOM), from
(Rrs(λ) are possible, mainly from empirical and
regional approaches, with relatively good accuracy
for with coastal waters (Vanhellemont and Ruddick,
2014; Concha and Schott, 2016; Kutser et al., 2016;
Lymburner et al., 2016; Lee et al., 2016; Urbanski
et al., 2016; Slonecker et al. , 2016; Li et al., 2017).
Table 1. List of spectrum channels and corresponding
resolutions of satellite Landsat-7/ ETM+
Many algorithms have been developed to
detect and extract surface water from remote
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Khoa học & Công nghệ - Số 27/Tháng 9 - 2020 Journal of Science and Technology 75
sensing technology in about two decades. For
example, the Normalized Differential Vegetation
Index (NDVI), developed in 1973 by Rouss et al., Is
based on the red and infrared bands, which are used
to distinguish the soil humus layer from clear water
to chisel (Vermote and Saleous, 2007; Jawak et al.,
2015). To determine the water pixels, the normalized
difference water index (NDWI), which is based
on the difference between reflectance in the green
band, is heavily influenced by the water surface and
the Near-Infrared reflectance Spectrometer (NIR) ,
was introduced by McFeeters (1996). However, this
commonly used metric was unable to effectively
suppress the signals from soil and water appearing
in the image. Therefore, combine this with GIS
(McFeeters, 2013) or the Modified Normalized
Difference Water Index (MNDWI) by replacing the
average infrared range with the near-infrared band
(Xue). , 2006) to improve water surface detection
in environments with complex information (images
with different coatings layer). Due to the relatively
good performance of the algorithm, as well as the
full automation, the Fmask algorithm (The Function
of the Mask) is often used to mask clouds, shadows
and snow in Landsat images (Zhu and Woodcock,
2012). This algorithm is based on predefined
thresholds of TOA reflectance values at specific
ranges, calculates cloud probabilities, analyzes
temperature, luminance, normalized Difference
Vegetation Index (NDVI), and normalized
differential snow index (NDSI), for a variety of
objects (earth, water, shadow clouds, snow and
clouds). Despite recent improvements by Fmask
(Zhu et al., 2015), based on improvements on the
Landsat 8 satellite and using a dynamic threshold
instead of a fixed threshold for detecting clouds on
water, some identifies Water pixel false positives are
still limited especially in turbid waters (confused/
not recognized). Due to the high contribution of
the Rayleigh component to the TOA signal, its
contribution should therefore be eliminated in
order to better characterize the spectral shape of
the reflectance per object (Nordkvist et al., 2009).
For these reasons, the current algorithm, called
WiPE using atmospheric reflectance spectra peak
have been corrected Rayleigh (ρrc(λ)) as input
parameters. WIPE based on the combination
of the common criteria are applied to (ρrc(λ)) to
differentiate primarily pixels with pixel ground
water, clouds and vegetation, and based on the
application of analytical Hue -Saturation-Value or
Brightness, HSV to optimize the detection of thin
cloud and shadow pixels in the waters.
2. Research data
Figure 1. Algorithm development collection image
(blue) and algorithm test image (red)
The Landsat-7 downloaded from USGS
(https://landsat.usgs.gov/) based the different
geographical locations including atmospheric
conditions and environmental contrast (Figure
1). The entire data set has been divided into two
different subgroups. The first group, made up of
196,738 different pixels (with 164,681 water pixels)
extracted from 32 different images, was used to
develop the algorithm. The second group consists
of 12 different images for independent testing and
algorithm validation. The wide spectral resolution
range of water is reflected in the large variation of
the Rayleigh corrected reflection spectrum, ρrc(λ),
extracted on water pixels (Figure 2).
Figure 2. Reflect spectrum of water pixels in
algorithm development database
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Journal of Science and Technology76 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
2.1. Eliminating the Influence of Rayleigh
The TOA reflectance value, ρ
TOA
(λ), is
developed as follows (Gordon, 1997):
(1)
which ρR(λ) is the reflectance due to multiple
scattering of a pure atmospheric air molecules,
ρa(λ) is the reflectivity of the many scattering by
aerosols in a pure atmosphere, and ρRa(λ) stands
for the term interacting in real atmospheres that
contain both molecules and aerosol. T(λ) and t(λ)
are the direct transmission and diffusion of the
atmosphere, respectively. ρ
g
(λ), ρ
wc
(λ) and ρ
w
(λ)
are the reflectivity coefficients due to sunlight,
white light, and optically important components in
water, respectively. The reflectivity of sunglint and
whitecaps are related to wind speed and geometry
(Wang and Bailey, 2001).
The pixel value recorded by ETM + at the top
of the atmosphere is a digital number, from which
the TOA radiation, L
TOA
(λ), and reflectivity, ρ
TOA
(λ),
are calculated using equations (2) and equation (3),
respectively (Vanhellemont and Ruddick, 2014).
(2)
(3)
where L
TOA
(λ) is the spectral radiation (in Wm-2sr-
1μm-1), ML(λ) the radiation multiplier ratio (i.e. gain)
for each range, Qcal(λ) is the Level-1 pixel value in
numerical form and AL(λ) is the rate of the radiation
addition rate factor (ie deviation) for each band. d,
F0 is the Earth-Sun distance in astronomical units
and θ
0
is the average solar radiation of the band and
the angle of the sun’s peak.
The reflection coefficient due to Rayleigh
scattering in atmosphere, ρR (λ), can be defined
using the following equation (Gordon et al., 1988;
Wang and King, 1997):
(4)
where τR(λ) is the thickness of Rayleigh scattering,
pR(Θ) is a function of Rayleigh scattering phase at
a scattering angle, Θ, θv is the angle of the celestial
viewing angle and θ
0
the view of the sun. The optical
thickness at atmospheric pressure P is calculated as
follows:
(5)
where τ
R0
(λ) is the optical thickness at standard
atmospheric pressure P
0
is 1013.25 mbar. The
Rayleigh pR(Θ) scattering phase function is defined
as:
(6)
Terminology related to Θ- provides a
contribution to backscatter of photons from
atmosphere without interacting with the sea. The
terminology related to Θ+ stands for the photon
dispersed in the atmosphere towards the sea
surface. These terminologies are calculated from
solar sensor variable geometry as follows:
(7)
where Φ
0
and Φv are the sun and azimuth of the
sensor; r(θ) is the Fresnel reflection coefficient for
incident rays in the air at incident angle θ. A phase
function Rayleigh, PR(Θ), of the scattering angle, Θ,
and the Fresnel reflectance, r(θ), of the incident ray
in the air at the incident angle is ditermied by:
(8)
(9)
(10)
The Rayleigh corrected reflectance is defined
by Equation (11).
(11)
The Rayleigh scattering nomarlly contributes
about 80% to 90% of the TOA reflectivity in the
blue portion of spectrum for clear to turbid water
(Gordon et al., 1988; IOCCG, 2010).
2.2. The Reflection Spectrum Database of
Classified Objects
All TOA reflectance coefficients and Rayleigh-
corrected spectra are stored for each object including
clouds on land and water (2430 pixels), shadows on
land and water (2435 pixels), water (755344 pixels),
vegetation (2838 pixels), bare soil (333 pixels) and
buildings (340 pixels). All spectrum data of rrc(λ)
for selected objects which are non-water is given
in Figure 3.
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Khoa học & Công nghệ - Số 27/Tháng 9 - 2020 Journal of Science and Technology 77
Figure 3. Rayleigh calibrated spectral data; (a)
bare land, (b) buildings, (c) cloud, (d) vegetation
Benefit of using ρrc(λ) instead of ρTOA(λ) to
detect water pixels, which has typical reflection
spectra, is illustrated in Figure 4. From clear water
(Figure 4a) to very turbid water (Figure 4d), the
reflection spectral shape (as well as amplitude) of
ρrc(λ) has significantly different for various water
types.
Figure 4. Difference of reflectance spectrum ρrc(λ)
and ρTOA(λ) for different water types
3. Developing WiPE Algorithm for Landsat-7/
ETM+ Images
Typical spectrum examples of the ρrc(λ),
which allows understanding different spectral
criteria adopted in WiPE, are shown on five non-
water objects in Figure 3. With a relatively large
increasing ρrc(λ) from band 1 (blue) to band 5 (near
infrared), vacant land and buildings have significant
difference compared to other objects. Peaks in
spectral channels 5 (near infrared) and 7 (shortwave
infrared) are incorporated to a spectral shape which
allows distincting vacant land and buildings from
other objects. Similarlly, intensive increase of
ρrc(λ) is observed between the red and near infrared
channels for vegetation pixels; this is a remarkable
feature for their identification. Based on relatively
flat of spectral shape ρrc(λ) and a high signal level
in the blue-red part of spectrum, it allows relatively
well-defined clouds from other objects.
Figure 5. Diagram of the WiPE algorithm for
Landsat-7/ETM + sensor
Figure 6. Relationship between the ratio ρrc(4)/ρrc(2)
and the shortwave near infrared channel ρrc(7)
For a Landsat-7/ETM+ image, the Rayleigh
corrected reflection spectrum at shortwave infrared,
ρrc(7), and the ratio between the near infrared
channel and the channel green, ρrc(4)/ρrc(2), will
help to classify all non-water objects excepted
shadow objects. As above mentioned factors are
manually identified through graphical analysis
(Figure 6). The removed shadow objects are carried
over to the HSV transformation steps (Figure.
5). The S and V values of the Rayleigh corrected
spectrum are calculated as follows:
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Journal of Science and Technology78 Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
(12)
(13)
(14)
4. Applying WiPE Algorithm for Satellite
Imagery Landsat-7/ETM+
Results in Table 2 are acquired based on four
scenes (Figure 7) which are captured in downstream
of Mekong River at different and random times.
a) b)
c) d)
e) f)
g) h)
Figure 7. Results of LS7WiPE algorithm for
downstream Mekong River
Table 2. Average percentage difference between
automatic (LS7WiPE) and manual (QGIS) water
separation methods
Figures Image name LS7WiPE
(Pixel)
QGIS
(Pixel)
MAPD
(%)
7a LE07_L1TP_1240
53_20021207_201
70128_01_T1
28666001 28607521 0.2044
7c LE07_L1TP_1240
53_20030108_201
70126_01_T1
30359420 30351035 0.0276
7e LE07_L1TP_1240
53_20030313_201
70126_01_T1
21772494 21009847 3.6299
7g LE07_L1TP_1240
53_20030329_201
70125_01_T1
20948910 20945733 0.0151
5. Conclusions
A new algorithm (WiPE and LS7WiPE) has
been developed to evaluate water pixels for present
optical sensors. Unlike the existing algorithm,
WiPE based on on Rayleigh corrected reflectance,
(𝜆); therefore, it is more sensitive to the spectral
characteristics of various considered objects such
as clouds, shadow clouds, vegetation, bare land,
buildings, and water. WiPE generally shows very
good performance for detecting water pixels in
complex water environments.
Acknowledgments
This study is financially supported by the Code
Senior Researcher program under contract number
NVCC34.01/20-20.
ISSN 2354-0575
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THUẬT TOÁN TỰ ĐỘNG TÁCH MẶT NƯỚC CHO ẢNH VỆ TINH LANDSAT-7/ETM+
CHO VÙNG NƯỚC VEN BỜ VÀ TRONG ĐẤT LIỀN
Tóm tắt:
Việc xác định các pixel mặt nước trên các vùng nước tự nhiên là giai đoạn quan trọng trước khi áp
dụng các thuật toán dành riêng cho việc tính toán các thông số môi trường nước từ ảnh vệ tinh viễn thám.
Một số thuật toán tồn tại nhưng hiệu suất của chúng không đạt yêu cầu, đặc biệt là trên vùng nước đục,
nơi các pixel không có mây đôi khi được phân loại là mây hoặc đất, dẫn đến mất dữ liệu. Điều này đặc biệt
quan trọng đối với các ảnh vệ tinh viễn thám có độ phân giải không gian cao, chẳng hạn như các quan