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 Non-algal 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
2. Theoretical background
3. Methods
4. Results
5. Discussion
6. Conclusions
Acknowledgements
Citations
List of definitions
aLMI / Adaptive linear matrix inversion
CHL / Chlorophyll-α
CDOM / Coloured dissolved organic matter
CPC / Cyano-phycocyanin
CPE / Cyano-phycoerythrin
IOP / Inherent optical property
LDCM / Landsat data continuity mission
NAP / Non-algal particulates
OAC / Optically active constituents
SIOP / Specific inherent optical propertie
TSM / Total suspended matter
Parameters
a / Total absorption coefficient / m-1
a*NAP (440) / Specific absorption of NAP at the 440 nm / m2g-1
a*phy / CHL specific absorption spectrum / m2mg-1
aCDOM/a(CDOM) / Absorption coefficient of CDOM / m-1
aNAP/a(NAP) / Absorption coefficient of NAP / m-1
aphy/a(CHL) / Absorption coefficient of CHL / m-1
a(w) / Absorption coefficient of pure water / m-1
b / Total backscattering coefficient / m-1
bbNAP / Backscattering coefficient of NAP / m-1
bbphy / Backscattering coefficient of CHL / m-1
bb*NAP(550) / Specific backscattering of NAP at 550nm / m2g-1
bb*phy(550) / Specific backscattering of CHL at 550nm / m2mg-1
bbphy/b(CHL) / Backscattering coefficient of CHL / m-1
bbNAP/b(NAP) / Backscattering coefficient of NAP / m-1
bbw/b(w) / Backscattering coefficient of pure water / m-1
c / Attenuation coefficient / m-1
CCHL / Concentration of CHL / -
CCDOM / Concentration of CDOM / -
CNAP / Concentration of NAP / -
Ed / Downwelling irradiance / W m-1nm-1
Lsky / Downwelling radiance from the sky / W m-1sr-1nm-1
Lu / Total upwelling radiance / W m-1sr-1nm-1
rrs / Remote sensing reflectance / sr-1
s / Direction / -
SCDOM / Spectral slope constant for CDOM absorption coefficient / nm-1
SNAP / Spectral slope constant for NAP absorption coefficient / nm-1
YNAP / Power law exponent for NAP backscattering coefficient / -
Yphy / Power law exponent for NAP backscattering coefficient / -
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 cyano-phycocyanin (CPC) (Guerschman, et al., 2015). Inland waters are also referred to as Case 2 waters. Case 1 waters are opticallyrelatively 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 relevantthan 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 sensing[1] and earth observation[2].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 morecomplex 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 spectrathat were simulated using the EcoLight model. This method is chosen because it is useful for understanding causal relationships between the remote-sensing 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 Ramsesfieldspectroradiometer.IOP data is measured in this study with the BB9 and ac-s instrumentsthat 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 satelliteswill bedeployed 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 whichcan be of great valueasthey 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 Landsatrecords 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 studywhich 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 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 gradientin 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 situmeasured OAC concentrations?
The output of this study is a comparison between simulated Landsat-7, Landsat-8, Sentinel-2 and Sentinel-3satellite OACdata and laboratory measured OAC data, conducted withadaptive linear matrix inversionsapplied 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 likeMODIS, 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 / Spatial resolution (m) / Revisit cycle / Free of charge (Y/N) / Operating now or in the near future (Y/N)MODIS / 250-1000 / Daily / Y / Y
MERIS / 300 / 2-3 days / Y / N
VIIRS / 750 / 750 / Y / Y
IKONOS, Quickbird, SPOT-5, GeoEye / 2-4 / On-demand/2-60 days / N / Y
RapidEye / 6.5 / Daily / N / Y
Worldview-2 / 2 / On-demand / N / Y
Sentinel-2 / 20-60 / 5 / Y / Y
Sentinel-3 / 300 / 2 / Y / Y
Landsat-7 / 30 / 16 / Y / Y
Landsat-8 / 30 / 16 / Y / Y
2. Theoretical background
Water constituents
Optical water quality variables which can be estimated with remote sensing are;
- 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 particulatessuspended 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).
Remote sensing
Earth observation
The satellites used in this study and some of their properties are shown in Table 2.
Satellite sensor systems / ETM+ / OLI/TIRS / MSI / OLCI
Spatial resolution (m) / 30 / 30 / 10, 20, 60 / 300
No of Spectral Bands / 8 / 11 / 12 / 21
Revisit cycle (days) / 16 / 16 / 5 / 2
Swath width (km) / 185 / 185 / 290 / 1270
Launch date / April 1999 / February 2013 / June 2015 / Late 2015
Years in orbit/Minimum design life (yr) / 15/5 / 2/5 / 0/7 / 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 (,,).
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.
Satellite / Landsat-7 / Landsat-8 / Sentinel-2 / Sentinel-3Band / Satellite sensor systems / ETM+
Wavelength (nm) / OLI/TIRS
Wavelength (nm) / MSI
Wavelength (nm) / Spectral Resolution (m) / OLCI
Wavelength (nm)
1 / 483 / 443 / 443 / 60 / 400
2 / 565 / 483 / 490 /10 / 413
3 / 660 / 563 / 560 /10 / 443
4 / 852 / 655 / 665 /10 / 490
5 / 865 / 705 /20 / 510
6 / 740 /20 / 560
7 / 783 /20 / 620
8 / 842 /10
a:865 /20 / 665
9 / 674
10 / 681
11 / 709
12 / 754
13 / 761
14 / 764
15 / 768
16 / 779
17 / 865
18 / 885
19 / 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 (
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):
(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.