Assessing the Accuracy of Water Temperature Determination and Monitoring of Inland Surface ...

203 Pages·2014·11.27 MB·English

MSc Water Management
Thesis

20 January 2014
Delft, The Netherlands

Assessing the Accuracy of Water Temperature Determination and
Monitoring of Inland Surface Waters Using Landsat 7 ETM+
Thermal Infrared Images

A Case Study on the Rhine River, North Sea Canal, and Hollands Diep

Delft University of Technology
Faculty of Civil Engineering and Geosciences
Department of Water Management

And

HKV
LIJN IN WATER

Tom de Boer | 4171306
[email protected]

Supervising Professor
Prof. dr. ir. N.C. van de Giesen

Supervisors / Graduation Committee
Prof. dr. M. Menenti
Dr. ir. S.C. Steele-Dunne
Dr. ir. E.L.A. Wolters
Drs. P.C.M. Jeurissen

Abstract

Keywords: remote sensing, Landsat 7, ETM+, surface water temperature, thermal infrared (TIR)

This research evaluates the potential of thermal infrared (TIR) remote sensing to determine and
continuously monitor the horizontal water temperature distribution of inland surface water bodies.
Usually, monitoring temporal and spatial variability of surface water temperature takes place by
measurement networks of in-situ gauges, but these networks are often limited by sparse sampling in
both time and especially space.

For these and other reasons (e.g. relatively cheap, easy, and fast) the use of remote sensing in water
management studies and practices has increased. By remote sensing in the TIR spectrum the TIR
radiation leaving from the top surface water layer (< 0.1 mm) is measured, which can be used to
determine the radiant water temperature distribution in the horizontal plane. This horizontal radiant
water temperature distribution can be used as supplement to in-situ kinetic temperature
measurements. It is however necessary to first evaluate the accuracy and precision of the remotely
sensed water temperatures. Therefore, the goal of this study is to determine how and with what
accuracy and precision TIR radiant water temperature measurements (T ) can be used as an
remote
approximation for the horizontal distribution of the kinetic water temperature, based on comparisons
of T with in-situ kinetic water temperature measurements (T ).
remote in-situ

The criteria used in this study to determine the accuracy and precision of T is by means of
remote
comparison with T , which are usually taken at a certain depth in the water column. T
in-situ remote
represents a pixel value, which is thus compared with a point measurement of T . The bias (T
in-situ remote
– T ) statistics are indicative for the obtained accuracy and precision.
in-situ

In this study Landsat 7 ETM+ images measured in the thermal infrared spectrum (λ = 8-14 μm) are
used for water temperature determination of T (by means of the inverse of Planck’s Law),
remote
together with Landsat 7 ETM+ images measured in the visual and near infrared spectrum for water
detection of inland surface water bodies. The effects of emissivity; atmospheric absorption, emission
and scattering; and surface effects and thermal stratification are evaluated and, if possible, corrected
for. Uncertainty contained by the Landsat 7 ETM+ instrument has been taken into account by means
of applying a 95% confidence interval over the obtained surface water body. Reduction of thermal
pollution by land-originating TIR radiation of radiant water temperatures is well established by such a
95 % confidence interval water body.

The correction for atmospheric circumstances took place by means of the web-based Atmospheric
Correction Parameter Calculator, ACPC (see http://atmcorr.gsfc.nasa.gov/), which makes use of the
MODTRAN radiative transfer model. Obtained results have been verified by means of a newly
derived atmospheric correction algorithm for Landsat 7 ETM+ TIR images, developed with use of the
MODIS In-Scene Split Window Method. For clear-sky images, on which this study focuses, the
uncertainty contained by the atmospheric correction is up to ±0.8 °C inland, which can increase up to
±1.5 °C near the coast. Coastal uncertainty of the atmospheric correction is larger because of larger
coastal atmospheric gradients (mainly of water vapour), which is difficult to correct for.

The emissivity of water approaches that of a black body (ε = 1), but is usually 1 to 2 % lower. This
causes a reduction of up to 1 °C of the established radiant water temperature, but introduces an
uncertainty of up to ± 0.5 °C.

The surface effects and thermal stratification are influenced by many factors and processes which are
difficult to address. The combined result of the surface effets and thermal stratification lead to an
uncertainty in winter and summer of ±1.6 and ±3.2 °C, respectively.

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Overall, this study concludes the best procedure to approximate the horizontal kinetic water
temperature distribution of inland surface waters with Landsat 7 ETM+ TIR images makes use of a
95% Confidence Interval Water Mask, and an Emissivity and Atmospheric Correction.

The accuracy and precision levels of the horizontal water temperature distribution display an average
bias of 1.5 °C with σ = 1.5 °C and SE = 0.1 °C. T tends to nearly always over-predict T . Using
remote in-situ
the 95% Confidence Water Mask to avoid thermal pollution of water pixels by land (sub-pixel
heterogeneity), rivers with a width less than 120 m cannot be well resolved from the ETM+ images
anymore. Furthermore, no physical relation could be derived between T and T . The
remote in-situ
numerous and complex processes that together affect the measurement of T and its agreement
remote
with T , combined with the issue of the scale difference between T and T , make it difficult
in-situ remote in-situ
to derive a (physical) relation or formula that connects T to T . The seasonal influence,
remote in-situ
expressed by a difference between winter and summer, could be captured by means of the statistical
analysis. During winter, T over-predicts T on average by 0.8 ° with a bias spread of σ = 0.8 °C
remote in-situ
and SE = 0.2 °C. During summer, T over-predicts T on average by 1.8 ° with a bias spread of σ
remote in-situ
= 1.6 °C and SE = 0.2 °C.

The bias statistics of the obtained horizontal T distribution and the statistical seasonal relation
remote
between T and T can be used as an approximation for the horizontal kinetic water
remote in-situ
temperature distribution. Based on the results in this study and the difficulty to establish a more
direct relation between T and T , the proposed systematic correction becomes: in winter
remote in-situ
T - 0.8 and in summer T – 1.8. The bias spread statistics (σ and SE) form a first and
remote remote
reasonable quantification for the precision and uncertainty contained by the obtained
approximation.

It is recommended to attempt to reduce the uncertainties contained by approximations obtained in
this study by further research. Research towards a better Atmospheric Correction, which especially
accounts for local surface conditions and the spatial variations in atmospheric circumstances, could
mean a major improvement for the remotely sensed water temperature approximations.

Other research to improve the approximations of horizontal kinetic water temperature distributions
by T is to assess the thermal stratification and the surface effects. To better understand the
remote
thermal water body processes and the skin effect, improving our insight in the relation between
kinetic water temperature in the water column, kinetic water temperature at the surface (water
depth 0 m) and the TIR radiant surface water temperature measured locally would help. This
improved insight would also help to reduce the uncertainty of remote sensing measurements.

It is also recommended to investigate and improve the operational abilities of remotely sensed water
temperatures. A main practical constraint is the time interval by which images of the same location
are generated by the Landsat 7 satellite: 16 days. Therefore, to operationalize space-borne remote
sensing of water temperatures for daily water management practises it is highly recommended to
include more satellites with a TIR spectral channel. Another practical recommendation is to optimize
the time of ETM+ image acquisition and processing. With NASA currently requiring 1-3 days to
process the images to L1T process level, it must be carefully decided for what purposes the water
temperature information can be used.

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Preface

The ability to monitor the earth surface from space by means of remote sensing has provided great
opportunities in the field of water management. However, the use of remote sensing in this field still
has not fully been explored. In recent years the number of scientific studies conducted towards the
use of remote sensing for water management purposes has increased, but the translation to
applications remains limited. A lack of awareness of the actual possibilities of remote sensing might
be the underlying reason for this limited applied use of remote sensing.

This research evaluates the potential of thermal infrared remote sensing, in particular by the Landsat
7 ETM+ instrument with thermal infrared spectral channel, to determine and continuously monitor
the horizontal water temperature distribution of inland surface water bodies. The awareness that
water temperature is a critical water quality parameters has led to regulations and legal obligations
to monitor and manage its value. Remote sensing can supplement in-situ measurement network in
the monitoring and management of this water temperature.

This thesis report forms the conclusion of the MSc Civil Engineering, track Water Management, at the
University of Technology Delft. Starting this research in June 2013 I can state now, seven months
later, it has been more than just a learning experience. I have realised the last five years of study
have been preparing me for the conduction of this thesis and life after. Overcoming problems, always
remain positive and using my obtained analytical skills were the key ingredients to complete this
thesis.

However, I could not have completed my thesis without the help of my supervisors, colleagues,
friends and family. In particular I would like to express my gratitude to Erwin Wolters and Susan
Steele-Dunne, who were always available to provide me with advise when I needed it most.
Furthermore, Prof. Nick van de Giesen, Prof. Massimo Menenti and Petra Jeurissen have been very
helpful supervisors, who have made sure my thesis contains both a good scientific level and an
applied water management focus.

I would like to thank my colleagues at HKV , because they were always available for help and
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advise. I would like to thank my friends for the discussions we have had about my thesis topic and
the useful advise they gave me to improve my thesis. Last but not least, I would like to thank my
family, and in particular my parents, for always being there for me when I need them to.

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Acknowledgements

I would like to express my gratitude to a number of people and organizations who have provided me
with the required data to conduct my research. From Rijkswaterstaat, the Netherlands, I would like
to thank the total team of the Helpdesk Water, Peter Beuse, Dju Bijstra and Petra Jeurissen. I would
also like to thank the Bundesanstalt für Gewässerkunde, Germany, for providing me with all the
necessary data of the German Rhine River.

For the opportunity and funding of this research, in particular the fieldwork, I would like to thank
HKV . For fieldwork assistance I would like to thank Fons Bennink.
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Contents

Abstract .....................................................................................................................................................................i
Preface ......................................................................................................................................................................i
Acknowledgements ................................................................................................................................................. iv
Contents ................................................................................................................................................................... v
List of Figures ........................................................................................................................................................ viii
List of Tables........................................................................................................................................................... xii
List of Abbreviations ............................................................................................................................................. xiii

1 Introduction .................................................................................................................................................... 1
1.1 Research Context ................................................................................................................................... 1
1.2 Research Goal and Question ................................................................................................................. 3
1.2.1 Research Goal ............................................................................................................................... 3
1.2.2 Research Questions ...................................................................................................................... 4
1.3 Scope of Study ....................................................................................................................................... 4
1.4 Outline of the Report ............................................................................................................................. 4

2 TIR Remote Sensing in Surface Water Temperature Monitoring ................................................................... 6
2.1 Remote Sensing of Surface Water Temperature in General ................................................................. 6
2.2 Remote Sensing of Surface Water Temperature in Riverine Landscapes ............................................. 6
2.3 Methodologies to Remotely Determine Surface Water Temperatures ................................................ 7
2.3.1 The Electromagnetic Spectrum ..................................................................................................... 7
2.3.2 Image Processing – DN to Radiant Surface Water Temperature .................................................. 8
2.3.3 Image Processing - Water Body Detection and Delineation ....................................................... 12
2.3.4 Determination Horizontal Surface Water Temperature Distribution ......................................... 12
2.4 Advantages and Disadvantages of Remote Sensing Methods ............................................................. 13
2.5 Airborne vs. Space-borne Remote Sensing.......................................................................................... 14
2.6 Quality of Water Temperatures from Remote Sensing Methods........................................................ 15
2.7 Limitations in Water Temperature Determination by Remote Sensing .............................................. 16
2.8 Opportunities and Challenges in Water Temperature Remote Sensing ............................................. 16

3 Research Methodology and Material ............................................................................................................ 18
3.1 Case Study Areas ................................................................................................................................. 18
3.1.1 Rhine River .................................................................................................................................. 18
3.1.2 North Sea Canal .......................................................................................................................... 20
3.1.3 Hollands Diep .............................................................................................................................. 21
3.2 Research Methodology ........................................................................................................................ 23
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3.2.1 Water Detection and Delineation ............................................................................................... 24
3.2.2 Best Estimate Horizontal T .................................................................................................. 30
remote
3.2.3 Bias Analysis (T – T ) ....................................................................................................... 36
remote in-situ
3.2.4 Approximation of Horizontal Kinetic Water Temperature Distribution by T ...................... 37
remote
3.3 Material and Data ................................................................................................................................ 39
3.3.1 Data ............................................................................................................................................. 39
3.3.2 Fieldwork .................................................................................................................................... 41
3.3.3 Software ...................................................................................................................................... 43

4 Results .......................................................................................................................................................... 44
4.1 Water Detection and Delineation ........................................................................................................ 44
4.1.1 Cloud and Cloud Shadow – Detection and Removal ................................................................... 44
4.1.2 Water Detection and Delineation ............................................................................................... 48
4.2 Best Estimate Horizontal T ........................................................................................................... 51
remote
4.2.1 Comparing T to T ........................................................................................................... 51
remote in-situ
4.2.2 MODIS Split Window Atmospheric Correction Results ................................................................ 57
4.2.3 Comparing T to T : Case Study Water Bodies ................................................................ 63
remote in-situ
4.3 Bias Analysis (T – T )................................................................................................................ 69
remote in-situ
4.3.1 Fieldwork Results ........................................................................................................................ 69
4.3.2 External Factors Influencing Bias ................................................................................................ 72
4.3.3 Internal Factors Influencing Error ............................................................................................... 86
4.4 Approximation of Horizontal Kinetic Water Temperature Distribution by T ............................... 91
remote
4.4.1 Approximation of Horizontal Kinetic Water Temperature Distribution by T ...................... 91
remote
4.4.2 Addressing Horizontal Water Temperature Information Needs: Examples ............................... 94

5 Discussion ..................................................................................................................................................... 99
5.1 Methodology ....................................................................................................................................... 99
5.2 Best Estimate Horizontal T Distribution ..................................................................................... 100
remote
5.3 Bias Analysis: Relating T to T ............................................................................................... 101
remote in-situ
5.4 Approximation of Horizontal Kinetic Water Temperature Distribution by T ............................. 102
remote

6 Conclusion and Recommendations ............................................................................................................ 103
6.1 Conclusion ......................................................................................................................................... 103
6.2 Recommendations ............................................................................................................................. 104
6.2.1 Recommendations for further Scientific Research ................................................................... 104
6.2.2 Recommendations to Operationalize Space-Borne Remote Sensing of Water Temperature .. 105

7 References .................................................................................................................................................. 106
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Appendix A: Visualization Landsat 7 Path-Row Combinations............................................................................ 113
Appendix B: ACCA Algorithm .............................................................................................................................. 119
B.1 ACCA Pass 1 – Spectral Cloud Identification ............................................................................................. 119
B.2 Cloud Signature Development .................................................................................................................. 122
B.3 ACCA Pass 2 .............................................................................................................................................. 122
B.4 Aggregation and Filling ............................................................................................................................. 122
B.5 Cloud Shadow Detection .......................................................................................................................... 122
Appendix C: Atmospheric Correction Tool .......................................................................................................... 123
C.1 Functionality ACT ...................................................................................................................................... 123
C.2 Performance ACT ...................................................................................................................................... 123
C.3 ACT Used Model Input .............................................................................................................................. 124
Appendix D: Fieldwork Impression ..................................................................................................................... 125
Appendix E: Sample Areas for Judgment Water Detection ................................................................................ 126
Appendix F: Data Details ..................................................................................................................................... 128
F.1 Remote Sensing Data ................................................................................................................................ 128
F.2 In-situ Data ................................................................................................................................................ 131
Appendix G: Result Details .................................................................................................................................. 133
G.1 Comparing T to T : Additional Statistics ...................................................................................... 133
remote in-situ
G.2 Comparing T to T : Case Study Sites Additional Statistics ........................................................... 134
remote in-situ
G.2.1 Hollands Diep ...................................................................................................................................................... 134
G.2.2 North Sea Canal ................................................................................................................................................... 136
G.2.3 Rhine Holland ...................................................................................................................................................... 138
G.2.4 Rhine Germany .................................................................................................................................................... 140
G.3 Analysis Radiant Water Temperatures ..................................................................................................... 142
G.3.1 Fieldwork Results ................................................................................................................................................ 142
G.3.2 Analysis Radiant Water Temperatures – External Factors .................................................................................. 143
G.3.3 Analysis Radiant Water Temperatures – Internal Factors ................................................................................... 178

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List of Figures

Figure 1-1 Cooling Water Discharge Plume at Shell Moerdijk, Hollands Diep at 23 April 2010. Coordinates Are Presented in Northing and
Easting .............................................................................................................................................................................................. 3
Figure 1-2 Horizontal Radiant Surface Water Temperature Distribution of Rhine River Estuary at 27 May 2005 ............................................. 3
Figure 2-1 Electromagnetic Spectrum (Turner, et al., 2003) .............................................................................................................................. 8
Figure 2-2 Image Processing Approach (Handcock, et al., 2012)........................................................................................................................ 8
Figure 2-3 TIR Reflection and Emission Sources (Handcock, et al., 2012) .......................................................................................................... 9
Figure 2-4 Factors Controlling Radiant Temperature (Prakash, 2000) ............................................................................................................. 10
Figure 2-5 Inclination Principle of Satellites (Integrated Publishing, 2003) ...................................................................................................... 15
Figure 3-1 Rhine River Catchment (Lammersen, Engel, Van de Langemheen, & Buiteveld, 2002) .................................................................. 18
Figure 3-2 In-Situ Temperature Measurement Network Rhine ........................................................................................................................ 19
Figure 3-3 North Sea Canal Overview .............................................................................................................................................................. 20
Figure 3-4 In-Situ Temperature Measurement Network North Sea Canal ....................................................................................................... 21
Figure 3-5 Hollands Diep .................................................................................................................................................................................. 22
Figure 3-6 In-Situ Temperature Measurement Network Hollands Diep ........................................................................................................... 22
Figure 3-7 Research Methodology, Including Report Outline .......................................................................................................................... 23
Figure 3-8 Visualization Landsat 7 Path-Row combination 199-23 .................................................................................................................. 25
Figure 3-9 Water Body Delineation by means of Winterbed Shapefile ............................................................................................................ 27
Figure 3-10 Landsat 7 ETM+ Imaging with and without SLC (USGS, 2013b) ..................................................................................................... 27
Figure 3-11 Water Body Erosion Based on the Application of the Confidence Interval ................................................................................... 28
Figure 3-12 2D Gaussian Distribution (Chernenko, 2012) ................................................................................................................................ 29
Figure 3-13 Explored Procedures to Determine Best Estimate Spatial Radiant Surface Water Temperature .................................................. 30
Figure 3-14 Image Processing Procedure without Atmospheric Correction ..................................................................................................... 31
Figure 3-15 Emissivity Values Water (Handcock, et al., 2012) ......................................................................................................................... 32
Figure 3-16 Image Processing Procedure with Atmospheric Correction .......................................................................................................... 32
Figure 3-17 Visualisation of the Mathematical Smoothing .............................................................................................................................. 35
Figure 3-18 Possible Locations Measurement Stations .................................................................................................................................... 35
Figure 3-19 Procedure for Extracting Temperatures from Measurement Stations Located outside the Water Body ...................................... 36
Figure 3-20 Fieldwork Measurement Locations in the Nederrijn near the Island of Maurik ............................................................................ 42
Figure 3-21 Fieldwork Measurement Locations in the North Sea Canal near Buitenhuizen ............................................................................ 43
Figure 4-1 ACCA Algorithm Performance Illustration on Image of Path-Row 198-24 from 27 September 2009 .............................................. 44
Figure 4-2 ACCA Performance Illustration on Image Details of Path-Row 198-24 from 27 September 2009. The Black Circles Indicate
Examples of Overestimation of the Cloud Cover ........................................................................................................................ 45
Figure 4-3 ACCA Algorithm Performance Illustration on Image of Path-Row 198-24 from 2 June 2013 .......................................................... 45
Figure 4-4 ACCA Algorithm Performance Illustration on Image Details of Path-Row 198-24 from 27 September 2009. ................................. 46
Figure 4-5 Explanation Cloud Shadow Determination Based on Solar Angles: A = Solar Azimuth Angle, α = Solar Elevation Angle,
θz = Solar Zenith Angle (Stine & Geyer, 2013) ............................................................................................................................. 46
Figure 4-6 Cloud Shadow Detection. Consequences of Cloud Height Assumed Too High. Central Image: Yellow Circles Are Modelled
Cloud Shadows, Red Circles Are True Cloud Shadows. Lower Image Shows True Shadows Are Qualified as Water ................... 47
Figure 4-7 Cloud Shadow Detection. Consequences of Cloud Height Assumed Too Low. Central Image: Yellow Circles are Modelled
Cloud Shadows, Red Circles Are True Cloud Shadows. Lower Image Shows True Shadows Are Qualified as Water ................... 48
Figure 4-8 Example Water Delineation North Sea Canal in Image of Path-Row 198-24 of 27 May 2005 ......................................................... 50
Figure 4-9 PSF Integration ................................................................................................................................................................................ 51
Figure 4-10 Radiant vs. Kinetic Temperature Values – Results of All Twelve Model Procedures for All Images for all Water Bodies (and
All In-Situ Measurement Stations) Plotted Together. ................................................................................................................. 51
Figure 4-11 Radiant vs. Kinetic Temperature Values - Subplots of All Used Images, Obtained by All Twelve Model Procedures for All
Water Bodies (and All In-Situ Measurement Stations) ................................................................................................................ 52
Figure 4-12 Bias Whisker Boxplots for All Twelve Model Procedures for All Water Bodies (and All In-Situ Measurement Stations)............... 54
Figure 4-13 Frequency Distribution of Bias (T – T ) for All Twelve Model Procedures for all Water Bodies (and All In-Situ
remote in-situ
Measurement Stations) .............................................................................................................................................................. 55
Figure 4-14 Rhine River near Spellen, Germany. Image of Path-Row 197-24 from 3 June 2010. (a) Geographical Location, Google Maps.
(b) Water Detection and Delineation Results. (c) Rhine River Temperature, Normal Water Mask, Atmospherically
Uncorrected, Normal. (d) Rhine River Temperature, 95% Confidence Water Mask, Atmospherically Uncorrected Normal ...... 57
Figure 4-15 TOA Brightness Temperature versus Land Brightness Temperature: Linear Regression. Image Date: 27 May 2005 .................... 58
Figure 4-16 TOA Brightness Temperature versus Land Brightness Temperature: Linear Regression. Image Date: 13 April 2010 ................... 58
Figure 4-17 TOA Brightness Temperature versus Land Brightness Temperature: Linear Regression. Image Date: 20 September 2003 ......... 58
Figure 4-18 T Distributions Obtained Atmospherically Uncorrected, Atmospherically Corrected by RTM, and Atmospherically
remote
Corrected by MODIS In-Scene Split Window. Date: 27 May 2005 ............................................................................................... 59
Figure 4-19 T Distributions Obtained Atmospherically Uncorrected, Atmospherically Corrected by RTM, and Atmospherically
remote
Corrected by MODIS In-Scene Split Window. Date: 13 April 2010 .............................................................................................. 60

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