Comparison between simulated Landsat-7, Landsat8, Sentinel-2 and Sentinel-3 satellite data for
detecting inland water quality variables
Marit van Oostende
Student ID: 10187448
University of Amsterdam
Supervisor: Emiel van Loon
Date: 25/06/2015
The water quality of fresh water bodies is important for human health, biodiversity and
aquatic ecosystem health. New earth observation satellites currently and to be deployed in the
near future have the potential to improve remote sensing for inland waters and will enable
continued time series on the OACs; Chlorophyll-α, Coloured dissolved organic matter and Nonalgal particulate matter. Higher spectral resolution and careful placement of spectral bands has
been shown to improve water quality retrieval whether the sensors used were designed for
terrestrial or ocean applications. However, a sensor specifically designed for the monitoring of
inland water quality may not be cost effective. By evaluating the water quality retrieval
accuracy that can be achieved from reflectance spectra obtained from common satellite sensors,
this study aims to identify a cost-effective compromise by identifying the most suitable sensor
for this purpose. The output of this study is a comparison between simulated terrestrial sensors
Landsat-7, Landsat-8 and Sentinel-2 and the coastal –ocean sensor Sentinel-3 for retrieving
OAC data. We applied this comparison to five different lakes along a temperate to tropical
gradient. The spectral inversion method (algorithm) used was the adaptive linear matrix
inversions of forward simulations of spectra performed in EcoLight, a radiative transfer
numerical model imbedded in IDL. Incorporating signal to noise factors of each sensor
Sentinel-3 is the best suited to retrieve OAC data. When lakes are considered too small for the
Sentinel-3 sensor pixel size, Sentinel-2, with its smaller pixel size, may be the most useful for this
purpose.
Table of contents
1. Introduction ......................................................................................................................................... 4
2. Theoretical background ...................................................................................................................... 7
3. Methods ............................................................................................................................................ 14
4. Results ............................................................................................................................................... 16
5. Discussion ......................................................................................................................................... 22
6. Conclusions ....................................................................................................................................... 23
Acknowledgements ............................................................................................................................... 23
Citations ................................................................................................................................................ 24
2
List of definitions
Abbreviations
aLMI
CHL
CDOM
CPC
CPE
IOP
LDCM
NAP
OAC
SIOP
TSM
Adaptive linear matrix inversion
Chlorophyll-α
Coloured dissolved organic matter
Cyano-phycocyanin
Cyano-phycoerythrin
Inherent optical property
Landsat data continuity mission
Non-algal particulates
Optically active constituents
Specific inherent optical propertie
Total suspended matter
Parameters
a
a*NAP (440)
a*phy
aCDOM/a(CDOM)
aNAP/a(NAP)
aphy/a(CHL)
a(w)
b
bbNAP
bbphy
bb*NAP(550)
bb*phy(550)
bbphy/b(CHL)
bbNAP/b(NAP)
bbw/b(w)
c
CCHL
CCDOM
CNAP
Ed
Lsky
Lu
rrs
s
SCDOM
SNAP
YNAP
Yphy
Total absorption coefficient
Specific absorption of NAP at the 440 nm
CHL specific absorption spectrum
Absorption coefficient of CDOM
Absorption coefficient of NAP
Absorption coefficient of CHL
Absorption coefficient of pure water
Total backscattering coefficient
Backscattering coefficient of NAP
Backscattering coefficient of CHL
Specific backscattering of NAP at 550nm
Specific backscattering of CHL at 550nm
Backscattering coefficient of CHL
Backscattering coefficient of NAP
Backscattering coefficient of pure water
Attenuation coefficient
Concentration of CHL
Concentration of CDOM
Concentration of NAP
Downwelling irradiance
Downwelling radiance from the sky
Total upwelling radiance
Remote sensing reflectance
Direction
Spectral slope constant for CDOM absorption coefficient
Spectral slope constant for NAP absorption coefficient
Power law exponent for NAP backscattering coefficient
Power law exponent for NAP backscattering coefficient
3
m-1
m2g-1
m2mg-1
m-1
m-1
m-1
m-1
m-1
m-1
m-1
m2g-1
m2mg-1
m-1
m-1
m-1
m-1
W m-1nm-1
W m-1sr-1nm-1
W m-1sr-1nm-1
sr-1
nm-1
nm-1
-
1. Introduction
The quality of inland water bodies is important for consumption, agriculture, fishing, recreation and
ecosystems. It is affected by a number of factors including urbanisation, population growth, land use
change, deforestation, farming, overexploitation and contamination from industries. Therefore water
quality monitoring is essential to observe the condition and discover trends in the water body
constituents. Inland waters are defined in this thesis as inland surface freshwaters. Water quality refers
to the physical, chemical and biological content of the water and may vary. It does not describe an
absolute but rather a condition relative to the use or purpose of the water. The most important optical
water quality variables of inland water are the optically active constituents (OACs); chlorophyll-α
(CHL), coloured dissolved organic matter (CDOM), non-algal particulates (NAP) and cyanophycocyanin (CPC) (Guerschman, et al., 2015). Inland waters are also referred to as Case 2 waters.
Case 1 waters are optically relatively simple waters, where algae and its breakdown products are the
OACs, often summarised in the chlorophyll concentration. Case 1 waters are usually oceans. Case 2
waters are more complex, more OACs are relevant than just chlorophyll and they influence each other
(Dekker, et al., 2003).
There are three ways in which water quality are usually measured: laboratory analysis, in situ remote
sensing1 and earth observation2. Earth observation satellites can provide water quality data on a daily
basis on a large scale, which is not possible with field-based approaches (laboratory analysis and in
situ remote sensing) only. Earth observation provides an objective, wide viewing, high frequency and
continuous measurement tool. Field- and earth observation measurements can be used to complement
and validate each other (Guerschman, et al., 2015).
Earth observation satellites measure spectra from space at different wavelengths (spectral bands).
These spectra can be used for determining OACs. There are four approaches by which spectral
reflectance measurements can be used to estimate concentrations of OACs. First there is the empirical
method. Herein statistical relationships are sought between measured spectral values and measured
water parameters. This is the least scientific method, as a causal relationship does not necessarily exist
between the parameters used. Second is the semi-empirical method; the spectral characteristics of the
compounds sought are more or less accurately known. This spectrometric knowledge can be included
in the statistical analysis. Reasonable algorithms can be found by common sense and improved by
experience. Algorithms that use single bands, band ratios, band arithmetic or multiple bands as
independent variables in different regression analyses is a widely used example of the empirical
approach. This method suffers from the fact that extrapolation beyond the range of constituents
observed may produce erroneous results. Thirdly, there is the analytical method, a difficult method
wherein reflectance spectra are simulated using radiative transfer theory and the results cannot be
easily inverted. At last, the semi-analytical approach. This approach is more complex then empirical
approaches and requires measurements and knowledge of the local inherent optical properties (IOPs).
It is more accessible than purely analytical methods and uses algebraic solutions of the reflectance
approximation to derive OACs (Dekker A. G., 1993) (Matthews, 2011).
This thesis will make use of a semi-analytical method to estimate OAC concentrations derived from
modelled satellite reflection spectra that were simulated using the EcoLight model. This method is
chosen because it is useful for understanding causal relationships between the remote-sensing
Measuring the earth’s surface without coming into contact with it through sensing and recording reflected or
emitted energy (Dekker & Hestir, 2012).
2
Remote sensing measurements made from aircraft or satellites (Dekker & Hestir, 2012).
1
4
reflectance, the IOPs and the OACs and are not as complex and time consuming as purely analytical
methods. In this semi-analytical based computer model, OACs can be estimated with an initial input
of concentration-specific IOP (SIOP) datasets, concentrations and remote sensing reflectance (Rrs),
measured in this case with the TriOS Ramses fieldspectroradiometer. IOP data is measured in this
study with the BB9 and ac-s instruments that measure backscattering at nine wavelengths and beam
absorption and attenuation in hyperspectral wavelengths.
Current remote sensing of inland waters is limited by the fact that high spectral resolution imagery has
a low spatial resolution and vice versa (Julian, Davies-Colley, Gallegos, & Tran, 2013). New earth
observation satellites will be deployed in the near future or have recently been deployed, and have the
potential to improve remote sensing for inland freshwaters and have a long lasting impact (Dekker &
Hestir, 2012). These satellites include the Landsat Data Continuity Mission (LDCM) or Landsat-8,
and the Sentinel-2 and Sentinel-3 mission which can be of great value as they give free data access,
and can be used for continuous inland water quality monitoring (Palmer, Kutser, & Hunter, 2015).
The launch of Landsat-8 in February 2013 ensures the continuous stream of satellite data which is
essential for monitoring. It has been stated that Landsat-8 data will be comparable to other Landsat
records in terms of spatial resolution, swath width, global geographic coverage and spectral coverage
on the land cover (Irons, Dwyer, & Barsi, 2012). This article by Irons et al., was written before the
launch of Landsat-8 and therefore a comparison between Landsat-7 and Landsat-8 data is still
required. There is only one study which compares Landsat-7 and Landsat-8 data. This study compares
spectral bands with sample points and vegetation indices. However, they do emphasise that more
comparison analysis between Landsat-8 and other sensors should be carried out (Li, Jiang, & Feng,
2014). It has been proven that data from Landsat 1 to 7 can be used interchangeably to measure and
monitor the same landscape phenomena (Vogelmann, Helder, Morfitt, Choate, Merchant, & Bulley,
2001). The Landsat satellite series is the longest running earth observation mission, operating since
1972.
The ESA’s Sentinel missions, like the LDCM, will provide high resolution optical imagery and
continuity of earth observation data collection. As this mission is relatively new, no comparison
studies have been conducted between these two or with the LDCM. Further details of the satellite
sensors are discussed in the Theoretical background. Hence the question is: Is it possible, in relation
to water quality, to compare Landsat-8 and Sentinel data performance and consequently to be able to
relate them to older legacy or archival datasets (Landsat 5 & 7) allowing trend analysis across various
sensors?
The satellite sensors operate on diverse spectral, spatial and temporal resolutions (Roy, et al., 2014)
(Berger, Moreno, Johannessen, Pieternel, & Hanssen, 2012). This can be problematic when different
band placements and sensitivities for certain wavelengths may give different results on OAC
concentrations for the same water body. The different spectral resolutions could have serious
implications for relating new satellite data to older data of the Landsat series. Successful application
of any multi-spectral satellite sensor ultimately depends on the ability of that sensor to adequately
describe the shape of the reflectance spectrum and hence relate shape to water quality concentrations.
Improving the placement and width of spectral bands leads to stronger correlations with OACs and
increasing the number of such bands allows for a greater range of OACs to be retrieved (Dekker,
1993). The spectral resolution of different satellite sensors will therefore determine the ability to
discriminate a range of OACs. Sensors with limited spectral resolution will be restricted in their
5
ability to discriminate different OACs and will derive concentrations with less accuracy (Dekker,
1993). Higher spectral resolution and careful placement of spectral bands has been shown to improve
water quality retrieval (Aurin & Dierssen, 2012). By evaluating the retrieval accuracy that can be
achieved from reflectance spectra obtained from these common satellite sensors, this study aims to
identify the most suitable sensor for determining water quality in inland waters, and also determine
whether data from these satellites can be used interchangeably and therefore trends can be spotted.
Aim and output
The aim of the present study is to identify the most suitable satellite sensor for effective optically
active constituent retrieval by investigating the accuracy of OAC measurements of five different
inland freshwaters along a longitudinal gradient in Eastern Australia collected by existing satellites
Landsat-7, Landsat-8, Sentinel-2 and the future Sentinel-3. In other words, how accurate is the
simulated satellite OAC concentrations in relation to the in situ measured OAC concentrations?
The output of this study is a comparison between simulated Landsat-7, Landsat-8, Sentinel-2 and
Sentinel-3 satellite OAC data and laboratory measured OAC data, conducted with adaptive linear
matrix inversions applied to generated spectra made in EcoLight
These satellites were selected because the data is free and relevant for measuring inland water quality.
Also these satellites have a relatively small pixel size, a reasonable revisit cycle and are currently
operating or will be operating in the near future (Dekker & Hestir, 2012). Remote sensors like
MODIS, MERIS, VIIRS, IKONOS, Quickbird, SPOT-5, GeoEYE, RapidEye and Worldview-2 will
not be included as they do not meet all mentioned criteria (Table 1).
Satellite
MODIS
MERIS
VIIRS
IKONOS, Quickbird,
SPOT-5, GeoEye
RapidEye
Worldview-2
Sentinel-2
Sentinel-3
Landsat-7
Landsat-8
Spatial
resolution
(m)
250-1000
300
750
2-4
6.5
2
20-60
300
30
30
Revisit cycle
Free of
charge (Y/N)
Daily
2-3 days
750
On-demand/260 days
Daily
On-demand
5
2
16
16
Y
Y
Y
N
Operating now or
in the near future
(Y/N)
Y
N
Y
Y
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Table 1: Satellite sensors and some properties important for monitoring of OACs. Bold characters are the properties
where satellites do not meet the criteria set by this study. Data modified from Dekker and Hestir (2012)
6
2. Theoretical background
Water constituents
Optical water quality variables which can be estimated with remote sensing are;
1.
2.
3.
4.
5.
6.
7.
chlorophyll pigments (CHL),
phycocyanin (CPC, CPE),
total suspended matter (TSM),
coloured dissolved organic matter (CDOM),
vertical light attenuation (Kd), turbidity,
bathymetry and
emergent and submerged aquatic vegetation.
The temperature of the water surface skin layer can be estimated with thermal infrared remote sensing
but is not further discussed here as this requires earth observation sensors with thermal bands which
usually have lower spatial resolution and are not present on each of these sensors.. The first four
variables (1 to 4) are the most important water quality variables. CHL is an indicator of phytoplankton
biomass and nutrient status and is important for assessing the quality of drinking water and the light
environment; CDOM is the optically measurable component of dissolved organic matter in the water
and important for the light environment; TSM is important for assessing the concentration of
particulates suspended in the water column and the light environment in water and CPC and CPE are
indicators of cyanobacterial biomass, common in harmful and toxic algal blooms. These constituents
all affect the water reflectance spectrum in different ways; an example is shown in Figure 1. Because
the CPC and its reflectance absorption minimum falls outside of the spectral bands of Landsat and
Sentinel-2 this shall not be retrieved in this study.
In the visible and near infrared region (~400-900 nm) the influence of OACs interacts to modify the
shape and amount of the spectrally reflected signal. In wavelengths longer than 900 nm water itself is
such a strong absorber that very little radiation is reflected from the water bodies. It is dependent on
the type of satellite sensor system and its spectral response band placement what the quality of the
measurement is per constituent (Dekker & Hestir, 2012; Guerschman, et al., 2015).
Figure 1: A typical reflectance spectrum from a eutrophic inland water body and the regions in
which the different OACs influence the shape of that spectrum (Guerschman, et al., 2015).
7
Remote sensing
Earth observation
The satellites used in this study and some of their properties are shown in Table 2.
Satellite
Satellite sensor systems
Spatial resolution (m)
No of Spectral Bands
Landsat-7
ETM+
30
8
Landsat-8
OLI/TIRS
30
11
Sentinel-2
MSI
10, 20, 60
12
Sentinel-3
OLCI
300
21
Revisit cycle (days)
Swath width (km)
Launch date
Years in orbit/Minimum
design life (yr)
16
185
April 1999
15/5
16
185
February 2013
2/5
5
290
June 2015
0/7
2
1270
Late 2015
0/7
Table 2: Properties of the satellite sensor systems: ETM+, OLI/TIRS, MSI, OLCI
The above information was compiled from the official USGS, ESA and NASA web pages (https://earth.esa.int/web/guest/missions/esafuture-missions/sentinel-3,https://earth.esa.int/web/guest/missions/esa-future-missions/sentinel-2 http://landsat.usgs.gov/landsat8.php,
http://geo.arc.nasa.gov/sge/landsat/l7.html).
The spatial resolution is the pixel size, or the smallest surface area on de earth surface that can be
measured. Sentinel-2 has the smallest overall spatial resolution at 10 m or 100 m2 and sentinel-3 the
largest at 300m or 90000 m2. The satellite sensors record the amount of reflected light in each spectral
band for each pixel.
Spectral resolution is determined by the number, the width and placing of the spectral bands. All earth
observation sensor systems have spectral bands which are receptive to certain electromagnetic
wavelengths. For these sensors they range between 400-12,000 nm. Only the spectral bands which
range to 900 nm are useful for detecting OACs as they penetrate the water column and therefore only
those are displayed in Table 3 and Figure 2 (Guerschman, et al., 2015). The panchromatic bands of
the Landsats are also left out, as they have no added value to this study while those bands cannot
detect OACs. Figure 2 shows how different the placements of the spectral bands are, comparing the
different satellites.
8
Satellite
Band
Satellite
sensor
systems
Landsat-7
ETM+
Wavelength
(nm)
Landsat-8
OLI/TIRS
Wavelength
(nm)
1
2
3
4
5
6
7
8
483
565
660
852
443
483
563
655
865
9
10
11
12
13
14
15
16
17
18
19
Sentinel-2
MSI
Wavelength
(nm) / Spectral
Resolution (m)
443 / 60
490 /10
560 /10
665 /10
705 /20
740 /20
783 /20
842 /10
a:865 /20
Sentinel-3
OLCI
Wavelength
(nm)
400
413
443
490
510
560
620
665
674
681
709
754
761
764
768
779
865
885
900
Table 3: centre wavelengths (nm) of the different spectral bands. The spatial resolution of Sentinel-2 is also shown, as
this varies per band.
The above information was compiled from the official USGS and ESA web pages
(http://landsat.usgs.gov/band_designations_landsat_satellites.php, https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3olci/resolutions/radiometric, https://sentinel.esa.int/web/sentinel/sentinel-2-msi-wiki/-/wiki/Sentinel%20Two/Performance).
Figure 2: Schematic overview of the spectral band placement of Sentinel-2, Sentinel-3, Landsat-8 and Landsat-7.
Near infrared bands are displayed in grey.
9
In situ remote sensing
TriOS Ramses
The TriOS Ramses is a widely used in situ hyper spectral radiometer (350nm to 950 nm). It derives
the down welling radiance from the sky Lsky; total upwelling radiance Lu and down welling irradiance
Ed . It has three radiometers; one held above water (Lsky) and two underwater sensors, which measure
radiance and irradiance (Lu, Ed) in the water (Hommersom, et al., 2012). With these three parameters
measured, remote sensing reflectance can be derived through (Dekker, et al., 2003):
𝑟𝑟𝑠(𝑠) =
𝐿𝑢 (𝑠)
𝐸𝑑
(eq. 1)
where s is the direction of the reflection.
The most important properties of the TriOS Ramses instrument are shown in Table 4.
BB9 and ac-s
The BB9 measures the optical backscattering b in the water. The ac-s measures optical absorption a
and beam attenuation coefficient. With The BB9 and ac-s together, all inherent optical properties can
be determined per site.
Instrument
Deployment
Measures
Method
Wavelength
(nm)
320-950
TriOS
Ramses
BB9
In water
Lu, Ed, Lsky
In water
b
ac-s
In water
c, a
Three
Radiometers
Backscattering
412-715
Sensor
Spectrophotometer 400-730
Accuracy
0.3 nm
0.01 m-1
Table 4: Some properties of the instruments used in the field. Data was retrieved from the official instrument sites;
www.trios.de and wetlabs.com.
10
Radiometry
The colour of water is a complex optical feature, influenced by scattering and absorption processes as
well as emission by the water column and of the reflectance by the substrate (Figure 3) (Dekker, et al.,
2003).
Figure 3: Schematic diagram of the various processes that contribute to the signal as measured by a remote sensor
(Dekker, et al., 2003).
Light incident upon water with all its components may be transmitted, scattered or absorbed. When
the spectral absorption and scattering properties are determined of a water column, it is possible to
calculate specific per unit spectral absorption and scattering, thus estimating the concentration of all
constituents.
The IOPs only depend upon the medium. There are two main inherent optical processes, absorption
(a) and scattering (b), with the sum described as the beam attenuation coefficient. An example of an
IOP is the beam attenuation coefficient (c) and it represents the total loss of the light due to absorption
and scattering combined (Dekker A. G., 1993):
𝑐 =𝑎+𝑏
(eq. 2)
The absorption coefficient of the medium as a whole, at a given wavelength, is equal to the sum of the
individual absorption coefficients of the components present. Therefore:
𝑎(𝑡𝑜𝑡𝑎𝑙) = 𝑎(𝑤) + 𝑎(𝑁𝐴𝑃) + 𝑎(𝐶𝐻𝐿) + 𝑎(𝐶𝐷𝑂𝑀)
(eq. 3)
Where a(w) is the absorption of pure water, a(NAP) by non-algal particulate, a(CHL) by chlorophyll
and a(CDOM) by coloured dissolved organic matter.
Backscattering is the process by which photons change direction through interactions with matter and
causes radiant energy to leave the water. It is caused by water b(w), by chlorophyll b(CHL) and nonalgal particulate b(NAP) (Kirk, 1983):
𝑏 = 𝑏(𝑤) + 𝑏(𝐶𝐻𝐿) + 𝑏(𝑁𝐴𝑃)
(eq. 4)
11
Once the spectral absorption and scattering properties of the water samples have been determined it is
possible to calculate concentrations and estimate reflectance and vice versa. This can be done in the
program EcoLight accordingly (Dekker, et al., 2003):
𝑏
𝑟𝑟𝑠 = 𝑓(𝑎+𝑏)
(eq. 5)
aLMI analysis
The parameterisation of the IOP spectral shapes requires a detailed description of the often complex
optical variability within freshwater systems. Adaptive models that use variable sets of specific IOP
(SIOP) parameters can be used, which overcome problems of fixed SIOP sets. Brando et al. (2012)
introduced an adaptive implementation of the LMI (aLMI), proposed by (Hoge & Lyon, 1996) , that
incorporates a regional description of naturally occurring SIOPs derived from in situ measurements
and samples collected simultaneously. This approach reduces computational complexity and increases
parameter retrieval accuracy by limiting the model retrievals to the natural variability expected within
the study area (Brando, Dekker, Park, & Schroeder, 2012).
Figure 4: Outline of the inversion steps in aLMI for retrieval of concentrations and IOPs (Brando, Dekker, Park, &
Schroeder, 2012).
Figure 4 shows the schematic operation of the aLMI analysis. Every step, applicable to this particular
research, is explained as follows:
1. The first step is to put in each lake surface remote sensing reflectance per satellite.
2. The next inputs are all the SIOP sets. All SIOP sets are tested by aLMI and the set with the
best optical closure is used for that particular reflection.
12
3. The aLMI model first calculates the concentrations out of the input reflectance and one of the
SIOP sets.
4. Then it calculates the modelled reflectance out of the measured concentrations with a matrix
inversion. When there are more bands in the reflectance spectrum than concentrations (>3),
the matrix is over determined and it uses singular value decomposition to conduct the
inversion.
5. The last step of the loop is to see whether the used SIOP set is valid, so if the model optically
closes. This is a vital component for testing the validity of the model. It compares the input
reflectance to the modelled reflectance with the relative root mean square error.
6. The SIOP set with the best optical closure (the lowest ∆k) is used for determining
concentrations of OACs and the IOPs. Threshold conditions may be set to determine whether
the spectral closure between the modeled and measured spectrum is sufficient to provide
valid water quality information or not to avoid retrieving OACs that are too far from reality.
13
3. Methods
Site description
Six reservoirs have been sampled across Eastern Australia, spanning climatic regions from alpine to
tropical latitudinal ranges. The reservoirs are: a deep alpine hydroelectric lake (Blowering Reservoir
"BLO"); a temperate major headwater storage (Lake Hume, "HUM"); a semi-arid, small, shallow
vegetated weir subject to cyanobacterial blooms (Lake Cargelligo "CAR"); a temperate shallow
artificial lake in the centre of Canberra (Lake Burley Griffin, "LBG"); and a reservoir with a large
watershed that spans tropical rainforest and savannah in the north and dry tropical and semi arid
savannah in the south (Burdekin Falls Dam, "BDF") (Hestir, Brando, Campbell, Dekker, & Malthus,
2015). These particular lakes have been chosen because of their diversity and recent databases
(February/March 2013) with necessary water quality data are already available (provided by the
Commonwealth Scientific and Industrial Research Organisation, CSIRO). Some properties of these
lakes are shown in Table 5.
Site
name
Reservoir
name
Description
Lat (S)
Lon(E)
BDF
147.045
35.466
148.261
91
44
CAR
Cargelligo
33.285
146.402
5
15
HUM
Hume
36.119
147.039
40
202
LBG
BurleyGriffin
Storage
capacity ML
x10^6
1.86
1.63
0.0004
3.04
0.003
Dry tropics. Irrigation, urban
supply.
Cool temperate. Hydro-power,
irrigation and recreation.
Semi-arid grassland. Diversion
weir/inflow wetland.
Warm temperate. Hydro-power,
irrigation, river regulation and
recreation.
Dry-continental. Recreation and
urban supply.
Primary inflow rivers
20.627
BLO
Burdekin
Falls
Blowering
Max
Depth
(m)
40
35.174
149.065
18
7
Number
of sites
CHL
μg/La
NAP
a440/ma
CDOM
mg/La
Burdekin River, Belyando River
Tumut River
Lachlan River
Murrey River
Molonglo River
8
10
7
6
5
9.81
1.32
31.91
5.03
12.48
9.93
0.59
26.80
1.84
7.75
1.59
0.28
1.16
0.43
2.01
Site
name
BDF
BLO
CAR
HUM
LBG
Surface
area
(km2)
220
Table 5: Description of sample sites, modified from (Hestir, Brando, Campbell, Dekker, & Malthus, 2015).
a=Average of measured data
Materials
The materials required for this research are:
1.
2.
3.
4.
5.
6.
7.
The TriOS Ramses, for in situ remote sensing reflectance data.
The ac-s and BB9, for measuring IOPs.
EcoLight, for data processing and simulating satellite data.
ENVI, an IDL program to create spectral response bands.
EcoLight, an IDL program to generate a suite of spectra across different concentration ranges.
aLMI , an IDL program to determine OAC concentrations through spectra.
A small boat, to deploy the sensors.
14
8.
9.
10.
11.
12.
Cooled shock bottles (one per site), for CDOM filtration.
Black polyethylene jerricans, 20L (one per site), for CHL and TSM filtration.
Filtration requirements, to filtrate the water samples after every day of field work.
GPS, to record site locations.
Camera.
Field data collection and data analysis
Field data collection and filtration
On all five lakes five to ten sites were selected that are spatially distributed across the lake. All sites
were visited within February and March (2013), the Austral summer. This is the temperate dry season
and tropical wet season. Three samples for concentration determination were taken per site, the
average of these samples is used as the measured concentration of the particular site. On every site the
following activities were executed:






TriOS Ramses measurement
BB9 and ac-s measurement
GPS coordinates recording
Water sampling
Taking photos of environmental conditions (sun glint, foam at surface, cloud cover etc.)
Record water conditions/weather conditions/particulars
After every day in the field, all water samples need to be filtered to ensure that samples, holding
organic matter, do not deteriorate. The protocol for filtration and analysis was handled according to
(Clementson, Parslow, Turnbull, McKenzie, & Rathbone, 2001). The laboratory analysis is carried
out by CSIRO Oceans & Atmopshere Laboratory in Hobart.
Data analysis and simulations
The following steps are taken in this research, the technical details are shown in Chapter 2:
1. The first step is to simulate satellite sensor reflectance. The remote sensing reflectance,
measured with the TriOS Ramses, is convolved with the program ENVI into the satellite
bands (L7, L8, S2, S3) and into a generic 10nm band reflectance. The last reflectance
spectrum has a spectral band every 10nm and will be used as the 'Measured Reflectance' in
EcoLight as a reference.
2. IOPs measured with the ac-s and BB9 are calculated into SIOP parameter sets. All the
parameter sets are input for the aLMI analysis. The program will select the best fitting SIOP
set with the lowest relative RMSE, and thus the best optical closure.
3. The last input for the aLMI analysis are the measured CHL, CDOM and NAP concentrations.
These concentrations are varied by -10%, 0% and +10% to diminish measurement errors, and
give 27 simulated spectra for each site.
4. When the input is completed the aLMI analysis can be carried out. The output of this analysis
is an Excel file per satellite with the columns: Site station; Measured CHL; Measured CDOM;
Measured NAP; Modelled CHL; Modelled CDOM; Modelled NAP; SIOP set nr; Optical
Closure.
5. The last step is to upload all data and perform further analysis and visualisation in Matlab. It
should be noted that the columns with the measured concentrations of OACs are the
concentrations used in the analysis and not the simulated concentrations of the 10nm
spectrum. Results of this analysis are shown in Chapter 4.
15
4. Results
This chapter discusses the results with the assumption that concentrations of compounds are measured
correctly in the field and that the aLMI analysis reflects the output of OAC concentrations correctly.
First, some descriptive statistics are shown, then the results of the root mean square error between
measured and satellite are discussed and lastly the importance of the relation between CDOM and
CHL is explained.
Descriptive statistics
Basic statistical analyses has been performed on the concentration data of CHL, CDOM and NAP of
all lakes combined. The total number of samples is 972 per satellite sensor, after the variation of
concentrations of OACs. Descriptive statistics are shown in the following Tables:
CHL Measured
11,71
Mean
11,72
Standard deviation
2,27
25th Percentile
18,27
75th Percentile
49,70
Range
50,52
Maximum
0,82
Minimum
7,94
Median
L7
14,45
23,54
2,38
15,92
177,03
177,03
0,00
6,31
L8
5,34
13,83
0,56
3,77
107,53
107,53
0,00
1,54
S2
14,27
17,00
4,53
12,24
66,18
67,07
0,89
7,37
S3
13,64
15,94
3,47
15,73
61,14
61,58
0,44
6,88
Table 6: Descriptive statistics of measured and simulated CHL concentrations.
CDOM Measured
1,04
Mean
0,67
Standard deviation
0,42
25th Percentile
1,57
75th Percentile
2,78
Range
2,96
Maximum
0,18
Minimum
1,17
Median
L7
0,78
0,67
0,35
1,05
7,09
7,09
0,00
0,58
L8
0,66
0,43
0,37
0,81
2,24
2,26
0,02
0,57
S2
0,67
0,52
0,25
1,06
2,05
2,06
0,01
0,50
S3
0,83
0,57
0,35
1,28
2,25
2,39
0,14
0,59
Table 7: Descriptive statistics of measured and simulated CDOM concentrations.
NAP Measured
12,95
Mean
14,54
Standard deviation
1,71
25th Percentile
16,91
75th Percentile
51,12
Range
51,57
Maximum
0,46
Minimum
8,21
Median
L7
14,58
18,18
1,30
21,83
140,46
140,50
0,04
8,96
L8
10,02
9,29
1,49
16,31
40,10
40,52
0,42
7,33
S2
17,37
18,83
1,35
23,75
64,25
64,57
0,32
13,35
Table 8: Descriptive statistics of measured and simulated NAP concentrations.
16
S3
17,00
18,38
1,55
23,09
59,99
60,46
0,47
11,64
Satellite comparison
Figure 5: The root mean square error between the measured OAC concentrations in the field and the modelled OACs
per satellite.
Figure 5 shows clearly that, ascending from least suitable to most suitable satellite sensors and when
considering spectral band placement only, is; Landsat-7, Landsat-8, Sentinel-2, Sentinel-3. This is
expected as Landsat-7 only has three usable bands in the visible light spectrum and Landsat-8 only
has four. Sentinel-3 performs better than Sentinel-2 as its band placement is especially made for
detecting OACs, and also has more bands (respectively five and eleven). However, using more bands
does not automatically mean that concentrations will be measured more accurately. This is clearly
visible in the 10 nm spectrum. This spectrum has a 10nm wide band every 10 nm, but it measures
OACs less accurately than Sentinel-3 (Table 9).
RMSE CHL (ug/L)
10nm
L7
L8
S2
S3
7.8748
23.7391
16.1744
9.2823
6.7564
RMSE CDOM
(a440/m)
0.5373
0.6632
0.6286
0.5961
0.4919
RMSE NAP (mg/L)
10.7595
15.6603
9.2909
8.4340
7.4816
Table 9: output of the root mean square error between all measured and modelled OAC concentration data.
This phenomenon is explained by (Brando & Dekker, 2003). They emphasize the importance of band
placement when using matrix inversion methods, as it is very sensitive to noise. When using a spectral
band every 10 nm, a lot of bands with a high level of noise are present. The spectral bands of
Sentinel-3 are especially placed where signal levels are high for water quality variables (Figure 2). As
a result, Sentinel-3 detects less noise than the 10nm spectral band, and thus has a better accuracy in
measuring concentrations of OACs.
17
The next figures display the measured against modelled AOC concentrations and the 1:1 line. This
again shows that Sentinel-3 has the highest and Landsat-7 the least accuracy. Additionally, these
figures show that some lakes are measured more accurately than others. The next paragraph will
expand on these results.
Figure 6:The measured concentrations of OACs plotted against the simulated Landsat-7 concentrations of OACs.
Figure 7:The measured concentrations of OACs plotted against the simulated Landsat-8 concentrations of OACs.
18
Figure 8:The measured concentrations of OACs plotted against the simulated Sentinel-2 concentrations of OACs.
Figure 9:The measured concentrations of OACs plotted against the simulated Sentinel-3 concentrations of OACs.
19
Relation between CDOM and CHL
The lake characteristics are significant for interpretation of the data. In figures 7 to 9 it is shown that
for some lakes better results were achieved than for others. This is mainly due to the relation between
CHL and CDOM. These two compounds can be difficult to distinguish in a light spectrum. To
determine what lakes are causing spectral and covariance ambiguities, Figure 10 was made. This
figure is a visualisation of the Pearson correlation coefficient between CHL and CDOM.
Figure 10: The Pearson correlation between CHL and CDOM for the five different lakes. P-values are all below
0.005.
CDOM has two possible sources: it can be autochthonous (a breakdown product of CHL from algae);
or it is allochthonous CDOM. This can originate from: vegetation that falls into the water and breaks
down, agricultural run-off, soil leaching, etc. When CHL and CDOM are related, the main source for
CDOM is CHL, otherwise CDOM is allochthonous. When a lot of allochthonous CDOM is present,
then NRMSE of CDOM will be worse (Hestir, Brando, Campbell, Dekker, & Malthus, 2015).
Figure 10 clearly shows a low correlation between CHL and CDOM in BDF, CAR and BLO. This
makes sense as Burdekin Falls has two completely different main river inlets that mixes water up in
the lake and therefore the proportion of constituents gets mixed too. Lake Cargelligo has been
measured in the Austral end of summer, and this shallow lake is sensitive to cyanobacterial blooms,
which results in breakdown of cyanobacteria into CDOM. Since cyanobacterial compounds are not
taken in consideration in this research, CDOM appears to be allochthonous in this lake. Blowering
Dam also has no correlation between CDOM (p<0.5), however results show (Figure 11) that the
NRMSE of CDOM is excellent. In this case, the NRMSE of CHL is quite high. This alpine lake
endures a lot of rain during the month of February which results in seepage of water and consequently
outflow of CDOM. Lake Hume and Lake Burley Griffin on the other hand have highly correlated
CDOM and CHL, this means the main source of CDOM is the breakdown product of CHL in these
lakes.
20
Figure 11: The normalised root mean square error per satellite for the OACs and lakes.
The normalized root mean square error is conducted on the satellite data in order to compare CHL,
CDOM and NAP. NAP is most accurately measured in all lakes. Landsat-7 and Landsat-8 struggle
with differentiating OACs because they have broad and only few bands (three and four respectively).
Sentinel-3 performs best in differentiating all OACs because it has many carefully placed, narrow
spectral bands.
21
5. Discussion
There are some side notes to be considered when looking at this research. This research does not take
every factor of signal to noise in consideration, only spectral band placement and width. Signal to
noise is a function of band width, band location, pixel size and sensor sensitivity (Leijtens, PerezCalero, Dekker, & Peters, 2011).
𝑆𝑁𝑅 = 𝑓(𝐵𝑎𝑛𝑑 𝑤𝑖𝑑𝑡ℎ, 𝐵𝑎𝑛𝑑 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛, 𝑃𝑖𝑥𝑒𝑙 𝑠𝑖𝑧𝑒, 𝑆𝑒𝑛𝑠𝑜𝑟 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦)
(eq. 5)
When the pixel size is greater, the sensor has significantly more time per pixel to measure photons as
more photons can reach the sensor, due to the bigger area When looking at a sensor with the same
sensitivity and decreasing the pixel size, the amount of signal that this sensor receives is significantly
lower (Leijtens, Perez-Calero, Dekker, & Peters, 2011). Sentinel-3 has the biggest pixel size in this
research: 300mx300m. Sentinel-2 the smallest: 10mx10m. Satellites travel with a ground speed of
10km/sec above the earth surface, which translates to 0.03 seconds for 300 meters and 0.001 seconds
for 10 meter. If you would decrease the pixel size of Sentinel-3 to the pixel size of Sentinel-2, the
amount of signal collected would be 303 =27,000 times smaller. If the signal to noise ratio was, for
instance, 700 for Sentinel-3 it would then be 4.3, which would be unacceptable. Sentinel-2 has, of
course, a better signal to noise ratio due to a higher sensor sensitivity (Table 10).
Satellite
Landsat-7
Landsat-8
Sentinel-2
Sentinel-3
Mean signal to noise ratio
31
113
142
496
Table 10: Mean signal to noise ratios for the different satellites over the spectral bands in the visible light spectrum.
Data was collected from respectively www.nasa.com and www.esa.com.
However, with a bigger pixel size, it is harder (or impossible) to measure smaller inland lakes. The
edges of lakes in a pixel are not useable for remote sensing. Therefore a smaller pixel size would be
better for smaller lakes, but the signal to noise ratio will be lower.
22
6. Conclusions
This research compared the earth observation satellites Landsat-7, Landsat-8, Sentinel-2 and Sentinel3. There are, of course, other satellites that could have been compared, however these satellites all
have drawbacks on spatial resolution, revisit cycle and costs. Out of these selected four, simulated
Sentinel-3 data gave the best results for measuring CHL, CDOM and NAP in different types of lakes.
When considering signal to noise ratio as well, Sentinel-3 is still the better option. However, its pixel
size may be a drawback on smaller lakes. For smaller (or narrow) lakes, Sentinel-2 is the best option
to use, with its smallest pixel size out of all four.
Concentration data of all satellites may be used interchangeably while they measure the same
component. However, when doing this, it is important to know the differences between the satellites,
especially in time series. One should take in consideration that Landsat-7 and Landsat-8 are not very
accurate for this specific purpose. Landsat-8 performs significantly better than Landsat-7 because it
has more and slightly differently placed spectral bands. Broad band systems like these measure a lot
noise in comparison to narrow spectral band systems like the Sentinels. Although, the narrow bands
must be carefully placed for its purpose, otherwise it will still measure a high level of noise, which
can be seen in the 10nm spectrum.
In an optimal situation there would be a satellite with the appropriate band placement and width, pixel
size and sensitivity, especially made for OAC detection. As long as this kind of satellite does not
exist, Sentinel-3 (or Sentinel-2, depending on lake size) is the best sensor to use for research on
OACs.
Acknowledgements
First of all, I would like to thank the CSIRO for giving me the opportunity to experience working and
contributing on one of their researches and providing me with data. I also want to thank dr. Janet
Anstee, dr. Hannelie Botha and dr. Arnold Dekker for their advice, comments and time.
23
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