validation of rs approaches to model surface ... · the study reveals that there is a satisfactory...
Post on 11-Oct-2020
2 Views
Preview:
TRANSCRIPT
Validation of RS Approaches to Model Surface
Characteristics in Hydrology:
A Case Study in Guareña Aquifer, Salamanca, Spain
Michael Gidey Gebreyesus
February, 2009
Validation of RS Approaches to Model Surface
Characteristics in Hydrology:
A Case Study in Guareña Aquifer, Salamanca, Spain
By
Michael Gidey Gebreyesus
Thesis submitted to the International Institute for Geo-information Science and Earth Observation in
partial fulfillment of the requirements for the degree of Master of Science in Geo-information Science
and Earth Observation, Specialization: Integrated Watershed Modeling and Management
Thesis Assessment Board
Dr. Ir. C.M.M. Mannaerts Chairman (ITC Enschede)
Dr. Ir. M. J. Booij External Examiner (University of Twente)
Msc. Ir. G.N. Parodi First Supervisor (ITC Enschede)
Dr. Ir. M.W. Lubczynski Second Supervisor (ITC Enschede)
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer
This document describes work undertaken as part of a program of study at the International
Institute for Geo-information Science and Earth Observation. All views and opinions expressed
therein remain the sole responsibility of the author, and do not necessarily represent those of
the institute.
Dedicated to my dearest parents and my sisters and brothers
Who always care about me!
i
Abstract
Validation of RS methods capable of estimating variables like soil moisture and fluxes such as latent
and sensible heat is vital for the use of RS models in ungaged and remote areas. In this study the
validation of the surface energy balance system (SEBS) was carried out by estimating soil moisture
and actual evapotranspiration for the Guareña catchment in Spain and comparing them against ground
measurements. Thirteen atmospherically corrected MODIS images were processed and compared
from ground information collected on 23 soil moisture loggers and 5 meteorological stations. The
proportional relation of the relative soil moisture with the relative evapotranspiration was used for the
estimation of soil moisture from RS as SEBS was primarily developed for the estimation of surface
turbulent fluxes. A downscaling procedure to improve the comparison between point and RS
information was accomplished using the temporal stability approach.
The study reveals that there is a satisfactory correlation (r2=0.65) between the average field scale soil
moisture estimates of the RS method SEBS and the ground measurements, allowing this methodology
for modeling initialization. There is no correlation however between the pixel wise RS estimates and
the measured soil moisture on the point scale level after the pixel level estimates were downscaled to
the point scale ground measurements (0<r2<0.2).The AET estimates were compared with the
complementary (advection–aridity) method. Results indicate good correlation between the two
methods with coefficients of determination, r2, greater than 0.86 for all the pixels compared. The
single crop coefficient was also computed based on the estimates of the evapotranspiration from the
RS and the values are found to be in good agreement with the values in the FAO guide lines.
Estimates of AET were higher by 3% to 40 % when NDVI was used as a surrogate for the land cover
in estimating momentum roughness heights, which suggests against the use of non-interactive NDVI
based methods for Zom retrievals. The conclusion from this study is that SEBS can provide satisfactory
estimates of soil moisture at the field scale and can give reliable estimates of AET.
ii
Acknowledgements
First I thank the Almighty God for giving me the grace and the patience to complete my education.
I would like to thank the World Bank and the Government of Japan for giving me this opportunity
through the Joint Japan/World Bank Graduate Scholarship Program.
I thank very much my first supervisor MSc. Ir. Gabriel Parodi for his suggestions, guidance and help
throughout the research period. I thank my second supervisor Dr. Lubczynski for his advice and
introducing the study area. I also thank Professor Bob Su for his suggestion on the preliminary results
and MSc. Lichun Wang for modifying the SEBS program in ILWIS.
I am very grateful to Eng. Guido Baroncini for his help and guidance during and after the field work. I
thank very much Dr. Jose Martinez-Fernandez of the University of Salamanca for making available
the soil moisture and other data and for allowing me to use the office and laboratory during my stay in
Spain for the field work. I thank the staff at CIALE and the ITC staff and group members of the field
work Frances, Ryes, Ricardo and Ruwan. I thank also our course director Ir. A.M Lieshout and Dr. A.
Gieske for their advice and support during my stay at ITC.
I am also very grateful to my wife Merry who encourages me for further education and for giving me
the moral support and love. My special and sincere thanks go to my sister Genet, my brother in law
Joseph and my niece Heaven who comforted me during my weekends and breaks. I thank my parents
and siblings for giving me all the support and care I needed.
Last but not least I express my sincere thanks to all my Eritrean friends and my class mates from
Africa, Asia and Latin America. My special thanks go to Janaka Perera for his assistance in modifying
the HBV simulation.
iii
Table of contents
1. General introduction ....................................................................................................................... 1 1.1. Rationale ................................................................................................................................ 1 1.2. Problem Statement................................................................................................................. 1 1.3. Research Objective ................................................................................................................ 2
1.3.1. General Objective.............................................................................................................. 2 1.3.2. Specific Objective ............................................................................................................. 2
1.4. Research Questions................................................................................................................ 2 1.5. Methodology.......................................................................................................................... 3
1.5.1. Data availability ................................................................................................................ 3 1.5.2. Pre Field Work .................................................................................................................. 3 1.5.3. Field Work......................................................................................................................... 3 1.5.4. Post Field Work................................................................................................................. 3
1.6. Thesis Outline........................................................................................................................ 5 2. Theoretical background and description of the study area ............................................................. 6
2.1. Literature Review .................................................................................................................. 6 2.1.1. Surface energy balance Models and RS............................................................................ 6 2.1.2. Evapotranspiration and RS................................................................................................ 6
2.1.2.1. General...................................................................................................................... 6 2.1.2.2. Estimation of evapotranspiration ............................................................................. 7
2.1.3. Soil moisture...................................................................................................................... 8 2.1.3.1. Measurement............................................................................................................. 8 2.1.3.2. Soil moisture and evapotranspiration ....................................................................... 8
2.1.4. Point measurement, RS and Scaling.................................................................................. 9 2.1.5. Hydrological models ....................................................................................................... 10
2.2. Description of the study area ............................................................................................... 11 2.2.1. Location........................................................................................................................... 11 2.2.2. Climate ............................................................................................................................ 11
2.2.2.1. Precipitation............................................................................................................ 11 2.2.2.2. Temperature............................................................................................................ 11 2.2.2.3. Reference evapotranspiration ................................................................................. 12
2.2.3. Land cover and land use.................................................................................................. 13 2.2.4. Soils ................................................................................................................................. 13
2.3. Data collection and availability of ground data................................................................... 14 2.3.1. Instrumentation................................................................................................................ 14 2.3.2. Meteorological data......................................................................................................... 14 2.3.3. Data on physical properties of soil.................................................................................. 15 2.3.4. Soil moisture data............................................................................................................ 16 2.3.5. Discharge data ................................................................................................................. 18
3. Pre processing of images............................................................................................................... 19 3.1. Introduction to MODIS images ........................................................................................... 19 3.2. Importing MODIS level 1B data into ILWIS ...................................................................... 19
iv
3.3. Pre processing for SEBS...................................................................................................... 20 3.3.1. Radiance and reflectance at TOA.................................................................................... 20 3.3.2. Brightness temperature computation............................................................................... 20 3.3.3. Atmospheric correction ................................................................................................... 21 3.3.4. Application of SMAC in ILWIS ..................................................................................... 21
4. Analysis of point soil measurement values and up/down scaling................................................. 25 4.1. Introduction.......................................................................................................................... 25 4.2. Temporal stability analysis .................................................................................................. 25 4.3. Results of temporal stability analysis .................................................................................. 26 4.4. Up/down scaling .................................................................................................................. 29 4.5. Potential soil wetness........................................................................................................... 29
5. The Surface Energy Balance System-SEBS.................................................................................. 31 5.1. Introduction.......................................................................................................................... 31 5.2. Estimation of parameters ..................................................................................................... 31
5.2.1. Surface and bio physical parameters ............................................................................... 31 5.2.2. Weather and other parameters ......................................................................................... 35
5.3. Energy balance components ................................................................................................ 36 6. Hydrological modelling (HBV)..................................................................................................... 41
6.1. Introduction.......................................................................................................................... 41 6.2. Digital elevation model........................................................................................................ 41 6.3. Catchment behavior and assessment of discharge data ....................................................... 43 6.4. Input data ............................................................................................................................. 44 6.5. Model parameters ................................................................................................................ 45 6.6. Model calibration................................................................................................................. 45 6.7. Results and discussion ......................................................................................................... 46
7. Analysis of results and discussion................................................................................................. 48 7.1. Comparison of soil moisture................................................................................................ 48
7.1.1. Selected soil moisture stations ........................................................................................ 48 7.1.2. Field scale average soil moisture..................................................................................... 49
7.2. Comparison of AET............................................................................................................. 50 7.2.1. Comparison with the Complementary approach ............................................................. 50 7.2.2. Single crop Coefficient (Kc) ........................................................................................... 52
7.3. Effect of input data .............................................................................................................. 54 7.4. Limitations ........................................................................................................................... 55
8. Conclusions and recommendations ............................................................................................... 57 8.1. Conclusions.......................................................................................................................... 57 8.2. Recommendations................................................................................................................ 58 References ......................................................................................................................................... 59
Appendices ............................................................................................................................................ 61
v
List of figures
Figure 1-1 Schematic diagram of the methodology. ........................................................................................... 4 Figure 2-1 Study area.......................................................................................................................................... 11 Figure 2-2 Monthly average precipitation......................................................................................................... 12 Figure 2-3 Monthly average temperature. ........................................................................................................ 12 Figure 2-4 Monthly average reference evapotranspiration (based on Penman-Monteith equation). .......... 12 Figure 2-5 Land cover map................................................................................................................................. 13 Figure 2-6 Lithology of Guareña........................................................................................................................ 14 Figure 2-7 Guareña-REMEDHUS instrumentation......................................................................................... 15 Figure 2-8 Soil samples collected during field work. ........................................................................................ 16 Figure 2-9 Soil moisture probes.......................................................................................................................... 16 Figure 2-10 Soil moisture data from some Hydra probes (as seen, few stations need data filtering)........... 17 Figure 2-11 Soil moisture data TDR probes...................................................................................................... 17 Figure 2-12 Average soil moisture versus average precipitation..................................................................... 18 Figure 2-13 Guareña discharge 2002. ................................................................................................................ 18 Figure 3-1 Estimation of AOT at 550 nm. ......................................................................................................... 23 Figure 3-2 AOT and water vapor data for September 07, 2007...................................................................... 23 Figure 3-3 Comparison of TOA reflectance with ground reflectance for Sept 7,2007. ................................. 24 Figure 3-4 Comparison of TOA broad band albedo with ground albedo for a single pixel.......................... 24 Figure 4-1 Mean and standard deviation of relative difference for hourly moisture data. .......................... 26 Figure 4-2 Mean and standard deviation of relative difference-daily moisture data. ................................... 27 Figure 4-3 Mean and standard deviation of relative difference for one year data-hourly data. .................. 28 Figure 4-4 soil moisture of stations M9 and F6 against the whole REMEDHUS site.................................... 29 Figure 4-5 Alternative top soil potential wetness maps for REMEDHUS network....................................... 30 Figure 5-1 SEBS processes.................................................................................................................................. 32 Figure 5-2 Land cover and momentum roughness height (Zom) map for Guareña and environs. ................ 33 Figure 6-1 Schematic representation of HBV model (SMHI).......................................................................... 41 Figure 6-2 Guareña catchment with SRTM extracted and digitized rivers(digitized rivers source Eng.
Guido Baroncini, University of Salamanca). ............................................................................................ 42 Figure 6-3 Coverage of the precipitation stations in the catchment................................................................ 42 Figure 6-4 Catchment response for rainfall events in Guareña (discharge is in hectolitres/sec).................. 43 Figure 6-5 Yearly and monthly rainfall and discharge comparison with the discharge to rainfall ratios
shown in the boxes. ..................................................................................................................................... 43 Figure 6-6 Monthly rainfall and discharge comparison................................................................................... 44 Figure 6-7 Reclassified land cover and elevation maps of Guareña as required in HBV.............................. 44 Figure 6-8 Observed and simulated hydrograph for the period June 2003 to September 2004. .................. 47 Figure 7-1 Comparison of ground measured and SEBS derived soil moisture (2007-2008). ........................ 49 Figure 7-2 Comparison of ground measured and SEBS derived average soil moisture (2007-2008)........... 50 Figure 7-3 Soil moisture for selected days in the REMEDHUS network. ...................................................... 51 Figure 7-4 Comparison of actual and reference evapotranspiration. ............................................................. 52 Figure 7-5 Comparison of AET between SEBS and the complementary approach. ..................................... 53 Figure 7-6 Actual evaporation for day 318 of 2007 (Nov 14)........................................................................... 54 Figure 7-7 Actual evaporation for day 122 of 2008 (May 1)............................................................................ 55 Figure 7-8 Sensitivity of evapotanspiration to wind speed. ............................................................................. 56
vi
List of tables
Table 2-1 Location and resolution of meteorological stations. ........................................................................15 Table 3-1 Spectral characteristics of MODIS used in this research................................................................19 Table 3-2 MODIS level 1B products selected for further processing in SEBS...............................................20 Table 3-3 Summary of AOT, Water vapor and Ozone data. ...........................................................................23 Table 4-1 Mean relative difference and standard deviation for hourly moisture data. ................................26 Table 4-2 Mean relative difference and standard deviation for averaged daily data....................................27 Table 4-3 Mean and standard deviation of relative difference for one year data-hourly data (year starts at
the end of dry period in September)..........................................................................................................27 Table 5-1 Instantaneous weather parameters at satellite over pass time........................................................36 Table 6-1 Some characteristics of the Guareña basin as computed in ILWIS HYDRO processing.............42 Table 6-2 Model parameters for the first run in HBV .....................................................................................46 Table 6-3 Model parameters for the period June 2003 to September 2004....................................................47 Table 7-1 Comparison of Kc values. ..................................................................................................................53
Appendix Table 1 MODIS images reflectance/radiance scales and offsets.....................................................61 Appendix table 2 Physical properties of the top 5cm soil from the REMEDHUS network ( Data collected
from University of Salamanca) ..................................................................................................................64 Appendix table 3 Results of dry sieve analysis. .................................................................................................67 Appendix table 4 Formula for the complementary (advection-aridity) approach of estimating AET
(Brutsaert, 2005) .........................................................................................................................................68 Appendix table 5 Formula for changing wind speed from measured height ..................................................68 Appendix table 6 Determination of Porosity for top layer soils (5cm) at the soil moisture stations (for
location see Figure 2-7) of the REMEDHUS network. ............................................................................69 Appendix table 7 Formula and constants for calculation for hourly reference evapotranspiration. ...........70 Appendix table 8 Sample hourly meteodata and calculation of reference evapotranspiration (Villamor
station day 1-day 4).....................................................................................................................................71 Appendix table 9 Soil moisture data from the REMEDHUS network averaged from hourly data for the
years 2007 to 2008(blank means no data available). ................................................................................74
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
1
1. General introduction
1.1. Rationale
Hydrological and meteorological records for most remote areas in developing countries are
unavailable and those available are sometimes erroneous, unreliable and have considerable gaps. Data
acquisition through remote sensing (RS) therefore could be an alternative and reliable resource to
alleviate this kind of problem. Moreover for modeling land-atmospheric hydrologic interactions, there
is the desire that RS provide both model parameters and meteorological data like surface air
temperature, humidity, precipitation and radiation which would permit model simulations based solely
on RS data (Schultz and Engman, 2000).
RS methods are capable of estimating state variables such as soil moisture and surface fluxes such as
latent heat, sensible heat and soil heat fluxes. However validation of RS derived data through a
comprehensive study using ground measurement network and well established hydrological modeling
is required to use RS in ungaged and remote catchments.
1.2. Problem Statement
The systematic use of RS derived data in hydrology has tremendously increased in the last two
decades and some of the hydrologic processes of the land surface that has attracted researchers of RS
are soil moisture, evapotranspiration and other surface turbulent fluxes. Soil moisture is an important
component in the water cycle in local and regional scale. It controls the surface energy balance and
partitioning between infiltration and surface runoff (Bastiaanssen and Iwmi, 1998). However there are
few ground stations or networks that observe and measure this state variable. Thus the role of RS in
obtaining both spatial and temporal moisture data is vital. Evapotranspiration, which is closely related
with soil moisture, is also important component of the water cycle and its estimation and studying its
temporal and spatial variation is essential.
Different algorithms and methods have been developed to extract soil moisture, evapotranspiration
and other hydrologic parameters from RS data. The validation of the accuracy of these data in
different climatic conditions and geographic regions is indispensable, since there is always a
dependency on local conditions that prevents generalization. Part of the Guareña river catchment in
the Duero Basin, Spain has been monitored by a program named Network of Soil Moisture
Measurement Stations of the University of Salamanca (REMEDHUS) from June 1999 to the present.
The network consists of a series of 23 soil moisture stations distributed over the central Duero basin,
which mostly covers the Guareña sub basin.
This highly instrumented basin therefore is ideal to attempt the validation of soil moisture data
extracted from the RS algorithm Surface Energy Balance System-SEBS(Su, 2002b). The method has
the potential to be used in other areas with similar geographical and climatic conditions. This
algorithm was primarily developed to estimate surface turbulent fluxes. The relation of the latent heat
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
2
flux (evaporation) with soil moisture is exploited for the validation. This study aims at achieving this
goal through both direct and indirect comparison of the remote sensed data with the ground measured
data and through analysis of hydrological model simulated–observed matching. The hydrological
model proposed for this purpose is the HBV (Hydrologysca Byråns Vattenbalansavdelning). Soil
moisture is an out put variable from this model and the matching of the observed discharge with the
simulated discharge could be used to evaluate the estimation of the soil moisture from the RS on basin
scale level.
1.3. Research Objective
1.3.1. General Objective
The main objective of this study was the validation of a RS technique through the comparison of soil
moisture estimates against ground truth data and the comparison of RS estimated actual
evapotranspiration (AET) with local estimation methods. The soil moisture was obtained indirectly
from the SEBS RS model primarily used in extracting surface energy fluxes. To this end the following
specific objectives were formulated.
1.3.2. Specific Objective
• Estimation of latent, sensible and soil heat fluxes, relative evaporation, evaporative fraction
and daily AET from the RS model SEBS.
• Relate the estimated relative evaporation with relative soil moisture, compute soil moisture
and then compare and validate the computed soil moisture estimate against ground truth data.
• Produce a similar comparison of the AET estimated from the RS method with other methods.
• Assessment of the strength and drawbacks of the algorithm with regards to input data.
• Develop a procedure for the selection of data out of the available for calibration and validation
of hydrological model and then compare soil moisture outputs from the calibrated hydrological
model against the soil moisture output from the RS approach and the ground measurement.
1.4. Research Questions
• Are the RS derived soil moisture data comparable with the ground measured ones? And can
they be used to evaluate the RS model?
• How to compare spatially distributed RS soil moisture results with point measurements and
estimates from ground instruments? Which scaling up or scaling down methods are adequate?
• Are the spatially averaged soil moisture data from the RS comparable with the field average
soil moisture from the ground instruments?
• Are the RS evapotranspiration estimates comparable with other methods?
• Regarding specific topic in the SEBS validation: Are there considerable differences in the
outputs of the SEBS algorithm when the momentum roughness heights are empirically pre
determined and tagged to the attributes of land cover and when related to vegetation indices
(such as NDVI)?
• Can rainfall-runoff hydrological models be used in validating RS derived soil moisture data?
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
3
1.5. Methodology
Since many researchers have indicated that soil moisture and evapotranspiration are related, the latter
one was used to retrieve soil moisture from RS algorithm based on surface energy balance
approaches. Then the result is compared with ground truth data to validate or/and evaluate the
algorithm. In achieving this availability of the required data was checked and three consecutive phases
namely pre-field work, field work and post field work were adopted.
1.5.1. Data availability
Different types of data are required for validation and evaluation of RS techniques. For this research
the following datasets were required and collected from the study area, web sites, previous studies and
laboratory analysis.
• Meteorological data (temperature, wind speed, precipitation, radiation, relative humidity and
vapor pressure).
• Hydrologic data (discharge, soil moisture).
• Data for atmospheric correction(aerosol optical thickness, water vapor and ozone).
• Data on physical properties of soil such as soil texture, porosity, bulk density and field
capacity.
• Soil maps and land use land cover classification maps.
• Satellite images for digital elevation model (DEM) generation.
• Satellite images to retrieve the surface energy fluxes and then the soil moisture.
1.5.2. Pre Field Work
This phase covered the search and collection of MODIS (Moderate Resolution Imaging
Radiospectrometer) images, 90 m resolution Digital Elevation Model (DEM) from the Shuttle Radar
Topography Mission (SRTM) and delineation of the study area catchment using DEM hydro
processing built in ILWIS software. Review of literatures related with surface energy balance RS
approaches, evapotranspiration, soil moisture, up/down scaling methods and the HBV hydrological
model was conducted.
1.5.3. Field Work
In the second phase meteorological and hydrological data were collected. Voltmeter readings from
soil moisture probes were converted into volumetric soil moisture and land surface temperature
values. Soil samples were collected for laboratory analysis on the physical properties of the local soil
and then results were compared with previous studies in the basin and also with standard soil
characteristics software. Data for atmospheric correction and more MODIS images were downloaded
followed by preliminary data and image pre processing.
1.5.4. Post Field Work
This phase was the major part of the research and the data collected was organized, processed and
integrated into the models and algorithms for use in the planned validation and modeling process. Soft
wares such as MODIS Swath tools, ILWIS, ArcGis and EXCEL spread sheets were employed in the
process. Among the tasks analyzed and completed during this period were: Preprocessing of the
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
4
selected MODIS images such as changing raw data to radiance and reflectance, computing brightness
temperature, land surface albedo, emissivity and land surface temperature, atmospheric correction
using SMAC algorithm, estimation of relative evaporation and turbulent fluxes and then
determination of soil moisture. HBV Hydrological model running and calibration were also
conducted. Finally analysis and comparison of the results were made. The schematic flow diagram
representation of the methodology is shown in Figure 1-1 below.
Literature reviewAcquisition of
instruments
Acquisition of
images and maps
Field work
Hydro and
meteo Data
processing
Thesis output
Study area
delineation
Pre field work
Collect meteorological
and hydrological data
Collect soil maps
and samples and
conduct soil tests
Collect satellite
images and data for
atmospheric correction
Literature review
Analyze and compare RS
approaches, hydrological
modeling and ground
measurements
Post field work
Data and
Image pre
processing
Atmospheric
correction
SEBS Modeling
estimation of
surface fluxes,
actual and relative
evaporation
Soil moisture
estimation
Hydrological
model input
data
Model running
Model
calibration
Section 2.1
Sections
2.1,2.2,2.3,3.2,4.4
Sections 3.2, 4.2-4.5,
Chapters 5,6 and 7
Figure 1-1 Schematic diagram of the methodology.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
5
1.6. Thesis Outline
The thesis consists of 8 chapters.
Chapter 2 deals with literature review on soil moisture, evapotranspiration, scaling and surface energy
balance methods. It also describes the study area with all relevant data on soil, precipitation,
evapotranspiration and land cover land use.
Chapter 3 gives introduction on MODIS images and explains the preprocessing steps required for the
images including atmospheric correction.
Chapter 4 analyses the point scale soil moisture values and explains how a soil moisture station is
selected as representative for the whole field and how scaling is performed.
Chapter 5 describes the SEBS algorithm and briefs the parameters used in the energy balance
methods.
Chapter 6 gives brief introduction about the HBV model and deals with the processes and outputs of
the hydrological modeling.
Chapter 7 discusses and compares the results obtained from the research of this thesis using maps
graphs and tables.
Chapter 8 gives conclusions and recommendations drawn based on this study.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
6
2. Theoretical background and description of the study area
2.1. Literature Review
2.1.1. Surface energy balance Models and RS
RS quantification is the process of inferring surface parameters in large and small spatial scales and
also in high and low temporal scales from measurements of the reflected and emitted electromagnetic
radiation of the Earth’s surface. In developing RS algorithms for estimation of atmospheric turbulent
fluxes two basic physical principles, the conservation of energy and turbulent transport must be
considered (Su, 2002a). Conservation of energy is the basis of the surface energy balance approaches
and the rationale behind is that evaporation is a change of state of water by demanding a supply of
energy for vaporization. The latter also called the aerodynamic approach, recognizes the importance
of wind in transporting vapor away from the evaporating surface. Since the energy available in the
energy balance approach needs to be distributed between sensible and latent heat fluxes which
includes the principle of turbulent transport, both principles should be treated in developing RS
algorithms of the surface fluxes.
The surface energy balance equation for an evaporating surface can be written in its simplest form as:
EHGRn λ++= 2-1
Where Rn is the net radiation, G is the soil heat flux; the energy utilized in heating the soil, H is the
energy conducted as sensible heat and λE is the latent heat flux; the energy utilized for evaporation
with all units in Wm-2.The net radiation is the sum of all incoming and outgoing radiation of both
short and long wavelengths.
In equation 2-1 only vertical fluxes are considered and the net rate at which energy is being
transferred horizontally, by advection is ignored. Other energy terms such as heat stored or released in
the plant, or the energy used in metabolic activities, are negligible as they account for a small fraction
of the net radiation (Allen and FAO, 1998).
2.1.2. Evapotranspiration and RS
2.1.2.1. General
In literature different types of evapotranspiration terms are found. The FAO Irrigation and Drainage
Paper no 56 describe in detail three types of crop evapotranspirations. Other terms such as potential
and AET are also common. Potential evapotranspiration (PET), is the maximum possible
evapotranspiration according to prevailing atmospheric conditions and vegetative properties. The
partially vegetated surface should be well supplied by water such that soil moisture forms no
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
7
limitation to stomatal aperture. The reference crop evapotranspiration defined in FAO paper no 56 is a
special case of PET with fixed properties and without variability while potentially evaporating or
transpiring surfaces have temporal and spatial variability.
2.1.2.2. Estimation of evapotranspiration
Dingman (2002) has classified different methods for estimating potential and AET based on data
requirement. They are grouped as temperature based, radiation based, combination and pan methods.
For the AET the widely used methods are the water balance approaches, the potential evaporation
approach and the energy balance approach. The soil moisture balance assuming no lateral flow in a
control volume is one of the methods under the water balance approach. Based on the water balance
approaches lysimeters are used to measure evapotranspiration by measuring the components of the
water balance and are important in evaluating other indirect methods of estimating ET. Regarding the
potential evaporation approach, its relation to the soil moisture is discussed in section 2.1.3.2.
Another method in the potential evaporation approach, the complementary relation ship approach, as
discussed in Brutsaert (2005) has the following form:
poact ETPETET −= 2 2-2
Where ETact is AET, PETo is PET under equilibrium conditions and ETp is the PET.
The energy balance approach as explained in section 2.1.1 deals with conservation of energy. The
Penman-Montieth, the Bowen ratio and the Eddy correlation approaches are classified into this group.
The Penman-Monteith equation developed based on the Penman combination equation is given in
literature as:
)1(
)( )(
a
s
asa
pa
r
r
eer
C
n GRE
++∆
+−∆= −
γλ
ρ
2-3
Where Rn is the net radiation, G is the soil heat flux, (es-ea) is the vapor pressure deficit of the air, ρa
is the mean air density at constant pressure, Cp is the specific heat of the air, ∆ is the slope of the
saturation vapor pressure temperature relation ship, γ is the psychometric constant, rs is bulk surface
resistance and ra is the aerodynamic resistance. The FAO Penman-Monteith equation to estimate crop
evapotranspiration was derived based on this equation for a standard reference surface.
RS approaches
A number of algorithms that employ RS imageries have been developed to compute
evapotranspiration. As described by Immerzeel et al (2006) though it is difficult to classify the
different methods they arbitrarily have classified the approaches into four: the thermal infra-red
empirical methods, the feed back approach, the Land parameterization and the Energy balance and
similarity theory methods. In the latter method, the Monin-Obukhov similarity theory is used for the
computation of sensible heat flux and land surface energy balance for the latent heat flux. Some
algorithms from this method are Surface Energy Balance System(SEBS) by Su (2002b) (which is used
in this thesis and discussed in chapter 5), Surface Energy Balance Algorithm for Land (SEBAL) by
Bastiaanssen et al (1998), Simplified Surface Energy Balance Index(S-SEBI) by Roerink et al (2000),
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
8
and Disaggregated Atmosphere Land Exchange Inverse model (DisALEXI) by Norman et al. (2003).
Using these algorithms it is also possible to estimate actual evatranspiration for days without satellite
imagery (Immerzeel et al., 2006) in combination with daily meteorological data.
2.1.3. Soil moisture
2.1.3.1. Measurement
Near surface soil moisture influences the partitioning of precipitation into infiltration and runoff and
is important in evapotranspiration because it controls water availability to plants and thus affects the
partitioning of latent and sensible heat (Grayson and Western, 1998). There are, however, few
standard stations or networks that observe and measure this state variable apart from research sites.
Hence incorporating soil moisture in hydrological models of river basins has been as difficult as
essential.
Measurement of soil moisture content at a point in general can be categorized into two; direct and
indirect. The gravimetric method is a direct, absolute technique for estimating the water content of
soils. Volumetric soil moisture can be measured indirectly by a number of ways such as the time
domain reflectometry (TDR) method, the frequency domain measurement (FD) method (using
capacitance probes), electric resistance blocks and radiological methods.
The basic principle of the TDR and FD methods is that both measure the difference in capacity of the
soil to transmit high frequency electromagnetic waves or pulses which can be calibrated to soil
moisture content. They have advantage in giving continuous soil moisture reading if used with data
loggers. Measurement by electric resistance block is based on the principle that electrical resistance of
a porous block (e.g. gypsum) is proportional to its water content.
Nowadays RS also provides indirect quantifications of the top layer surface soil moisture with large
spatial and temporal coverage. This top layer soil moisture can be related to the profile soil moisture
content through modeling as the latter one is required in most applications (Antonio et al., 2005).
According to Chen et al (2008) soil moisture retrieval from RS is attempted from Optical (including
reflective near-infrared), thermal and microwave systems. Wang et al. (2007) have developed an
algorithm to retrieve soil moisture from the optical/infrared region while the microwave regions of
the electromagnetic spectrum have been used by Wagner et al. (1999b) and Wen et al. (2003). In this
research soil moisture was retrieved from the optical and the thermal regions of the electromagnetic
spectrum as they are used by the SEBS algorithm.
2.1.3.2. Soil moisture and evapotranspiration
As mentioned earlier soil moisture affects the partitioning of latent and sensible heat fluxes and hence
evapotranspiration. In estimating AET one of the most commonly used methods is to linearly relate
the PET with the relative soil moisture as shown below.
relp
act
ETET θ∝ 2-4
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
9
Where ETact is the actual evapotranspiration, ETp is the PET and θrel is the relative soil moisture. PET
could be estimated from meteorological data and in the case of RS methods by the combination of
meteorological data and energy balance considerations. As to the relative soil moisture different
definitions are found in literature. In Dingman (2002) relative water content is defined as:
pwpfc
pwp
rel θθ
θθθ
−
−= 2-5
Where θ is the current water content, θfc is the field capacity and θpwp is the permanent wilting point of
the root zone soil. In Van der Lee and Gehrels (1990) it is defined as:
r
rrel θφ
θθθ
−
−= 2-6
Where θr is the residual soil moisture and φ is the porosity of the soil. In the development of the SEBS
algorithm use is made of energy balance consideration at the limiting cases and the concept of relative
evaporation was developed as in the following equation.
wet
rE
E
λλ
=Λ 2-7
Where Λr, is the relative evaporation, λE is the evaporation, λEwet is the potential evaporation. This
idea was further developed into soil moisture by Su et al (2003) considering water balance of a soil
layer in the vertical direction at the limiting cases similar to the relative evaporation . It is shown that
the relative soil water content is directly related to the relative evapotranspiration.
wetwet
relE
E
λλ
θθ
θ == 2-8
Where θwet is the water content at limiting case taken as porosity of the soil. This potential maximum
wetness (limiting) value has also been shown to be approximated to a mid point between the field
capacity and the total water capacity (porosity) by Wagner et al.(1999a), after a histogram analysis of
gravimetric soil moisture data in Ukraine. This can be expressed as:
2
φθθ
+= fc
wet 2-9
All the above relations could be used in computing soil water content after computing the relative
evaporation from energy balance methods. Then the results could be validated with the ground truth
data of soil moisture from the REMEDHUS network in the Guareña catchment. Other ancillary data
like porosity and field capacity are also ground truth data collected from the field work of this
research and previous studies. This approach can also be used to validate the surface turbulent fluxes
indirectly.
2.1.4. Point measurement, RS and Scaling
RS has the potential to provide information on spatial variability of fluxes and state variables and this
information needs ground measurement for verification and validation. Ground instruments however
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
10
give only a point scale measurement. To compare the values of these parameters and variables
obtained from the RS models against the point scale ground measurements some means of averaging,
interpolating, up scaling or downscaling and aggregation or disaggregation are required.
For interpolation purposes nearest point, moving average and geostatistical methods (like ordinary
kriging and anisotropic kriging) are now some of the routine functions in a number of GIS softwares.
For soil moisture however Grayson and Western (1998) argue that geostatistical methods require
samples that are closely spaced relative to the correlation length of the spatial soil moisture fields and
hence these methods do not represent a practical alternative for large-scale area estimation of soil
moisture.
Different types of approaches are found in literature in scaling up soil moisture values from point
scale to field average scale. Antonio et al. (2005) used arithmetic average to obtain field average plant
available water content from point measurements. De Lannoy et al. (2007) explored some statistical
methods including a time-mean bias correction, a linear transformation and cumulative density
function to convert point measurement of soil moisture to field averaged soil moisture. Their analysis
was based on the temporal stability analysis which was also used by Cosh et al. (2004) to establish the
validity of this method to provide water shed scale soil moisture estimates for the purpose of satellite
validation.
According to Grayson and Western (1998) temporal stability of soil moisture can be thought of as
temporal invariance in the relationship between spatial location and statistical measures of soil
moisture or according to Cosh et al. (2004) as a technique that investigates the idea that a soil
moisture field maintains its spatial pattern over time. The idea was first proposed by (Vachaud et al.,
1985) and the implication of this idea is that soil moisture measured at a point could be highly related
with the average soil moisture of an area. Wagner et al. (2008) point out that given temporally stable
soil moisture patterns, time invariant relationships can be used for estimating regional soil moisture
from point scale measurements, a process they referred to as up scaling.
In this research the assumption is that if these up scaling methods mentioned in the cited literatures
can be used in computing field average soil moisture from point scale ground measurements, then it
could be also possible to up scale ground point measurement to a relatively coarse scale pixel size like
the MODIS pixels of 1km x 1km or conversely from the 1km x 1km MODIS pixels to point scales for
comparison and validation purposes.
2.1.5. Hydrological models
Different hydrological models are available nowadays to forecast and simulate rainfall-runoff
processes. Examples of such models are SWAT, TOPMODEL, MIKESHE, HEC HMS, HBV etc. In
selecting a model for this research the availability of the model, the availability of the data and
information with regards to the input to the model were considered. In line with this the HBV model
was selected. HBV is an acronym of Hydrologysca Byråns Vattenbalansavdelning (Hydrological
Bureau Water balance section), a former section in the Swedish Meteorological and Hydrological
Institute (SMHI) where the model was developed. The model has been applied in countries with
different meteorological conditions. One of the output variables in this model is soil moisture and use
can be made of this result in evaluating remote sensing approaches.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
11
2.2. Description of the study area
2.2.1. Location
The Rio Guareña basin is located in the Western part of Spain with an estimated area of 1056 km2. As
shown in Figure 2-1, it is a sub catchment of the Duero Basin which is the second largest watershed in
Spain covering 16 % of the country (78954 km2). The geographical location of this basin is between
5°23’W to 5°44’W and 40°53’N to 41°32’N. In this research the REMEDHUS network which mostly
falls in the Guareña basin was used in the validation processes. The environs of the basin were used
for comparison purposes as well.
2.2.2. Climate
2.2.2.1. Precipitation
The Guareña catchment has a semi-arid Mediterranean environment characterized by low annual
precipitation and hot dry summers. The six year average precipitation from three weather stations is
about 430 mm per year. With rainfall records of 8.4 mm and 77.3 mm, July and October are the driest
and wettest months respectively (refer Figure 2-2).
2.2.2.2. Temperature
Temperature of the basin varies considerably between summer and winter (refer Figure 2-3). The
average temperature is about 12 °C. July and August are the hottest months while December and
January are the coldest. Temperatures records as high as 37 °C and as low as -10 °C were recorded
during the period 2002 to 2007.
Figure 2-1 Study area.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
12
monthly average precipitation(2002-2007)
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
Janu
ary
Febru
ary
Mar
chApr
il
May
June
July
Augus
t
Septe
mbe
r
Octob
er
Nov
embe
r
Dec
embe
r
month
pre
cip
itati
on
(mm
)
Figure 2-2 Monthly average precipitation.
monthly average temperature(2002-2007)
0.0
5.0
10.0
15.0
20.0
25.0
Janu
ary
Febru
ary
Mar
chApr
il
May
June
July
Augus
t
Septe
mbe
r
Octob
er
Nov
embe
r
Dec
embe
r
month
tem
pe
ratu
re (
°C)
Figure 2-3 Monthly average temperature.
2.2.2.3. Reference evapotranspiration
The average reference evapotranspiration based on meteorological data from station VA_02
calculated using the Penman-Monteith equation is shown in Figure 2-4. The total annual average
reference evapotranspiration computed for 2001-2007 is about 1200 mm per year.
average reference evapotranspiration(2001-2007)
0
50
100
150
200
250
Janu
ary
Febru
ary
Mar
chApr
il
May
June
July
Augus
t
Sep
eptembe
r
octobe
r
Nov
embe
r
Dec
embe
r
month
ET
o(m
m)
Figure 2-4 Monthly average reference evapotranspiration (based on Penman-Monteith equation).
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
13
2.2.3. Land cover and land use
The land use in the area is mainly rain fed agriculture, with a small but significant proportion of
supplementary irrigation schemes. Rain fed crops include wheat, sunflower and grapes. Beets and
maize were observed during the field work in the irrigated areas. Figure 2-5 shows a reclassified map
from the Corine Land Cover (CLC) project established by the European Union. CLC provides
comparable digital maps of land cover for most of Europe.
Figure 2-5 Land cover map.
2.2.4. Soils
There are mainly two types of soils in the area classified as luvisols and cambisols according to FAO
classification (Martinez-Fernandez and Ceballos, 2003). The soil texture analysis of the collected
samples based on mechanical sieve analysis shows more than 90% sand content. However previous
vast studies conducted by the same authors revealed that the predominantly sandy texture in the area
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
14
account for 71% with very low organic content and clayey horizons at the bottom of the profiles. For
comparison of the results look at Appendix table 2, Appendix table 3 and Appendix table 6 with the
locations of the samples also shown in the accompanying Appendix Figure 1. The lithological map of
the area is also shown in Figure 2-6.
2.3. Data collection and availability of ground data
2.3.1. Instrumentation
The Guareña basin and the REMEDHUS network consist of a series of 23 soil moisture stations. In
each station there are two types of soil moisture measuring instruments. There are three weather
stations inside the basin and two more on the edge of the REMEDHUS network. There are also three
discharge stations though two are no more functional. The location of the instruments in the basin is
as shown in .
Figure 2-6 Lithology of Guareña.
2.3.2. Meteorological data
Meteorological data was collected from three weather stations within the catchment namely Villamor,
Ema Granja and Ema Canizal and two stations outside the catchment, VA_02 and ZA_03. The
stations with in the catchment are monitored by the REMEDHUS program. Data on radiation,
precipitation, wind speed, relative humidity and temperature at two meters height was collected from
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
15
three stations (VA_02, ZA_03, and Villamor) starting from 2000. Two of the stations in the basin,
Ema Canizal and Ema Granja, are relatively new measuring since 2007. Stations VA_02 and ZA_03
located near the basin are monitored by Info Riego of the Institute of Agricultural Technology under
the local government. Table 2-1 shows the location and the resolution of each station.
Table 2-1 Location and resolution of meteorological stations.
Station Coordinate-X
(UTM 30N in m)
Coordinate-Y
(UTM 30N in m) Start period
Temporal
resolution
Villamor 281892 4568453 2000 ten minute
Ema Granja 301960 4576763 2007 ten minute
Ema Canizal 302117 4562652 2007 ten minute
VA_02 314502 4566415 2000 daily
ZA_03 290044 4597772 2000 daily
Figure 2-7 Guareña-REMEDHUS instrumentation.
2.3.3. Data on physical properties of soil
Below there is a listing of the physical properties of the top soil in the study area analyzed in the
laboratory. In total 40 samples were collected during the field work (Figure 2-8). The results were
compared and integrated with previously collected samples by the University of Salamanca. About
150 samples were collected from a 3km x 3km grid in the REMEDHUS network. To determine the
field capacity values, a soil water characteristic software (Soil Water Characteristics, Version 6.02.74
by Keith E. Saxton) was used. All the computed data are shown in Appendix table 2.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
16
Particle density: is the mass of the soil particles (mineral grains) in a given volume given as the mass
of the soil divided by the volume of the mineral grains only. It is used with bulk density data to
calculate the porosity of a soil. A value of 2.65 g cm-3 was assumed for most soils.
Bulk density: is the dry density of the soil given as the mass of the soil divided by the total volume of
the soil sample which is the sum of the volumes of the air, the moisture and the mineral particles. The
value is determined by dividing the mass of a sample dried for 16 hours or more at 105 °C by the
volume.
Porosity: is the proportion of pore spaces in a volume of soil. The value is given by dividing the total
volume of the pore space (air and water) by the volume of the mineral particles only. It can also be
calculated by subtracting the ratio of the bulk density to the particle density from one.
Field capacity: is the water content that can be held against gravity. It is the moisture content below
which further decrease in soil moisture occurs at a negligible rate.
Figure 2-8 Soil samples collected during field work.
2.3.4. Soil moisture data
The top soil layer (5cm) soil moisture data was collected from two types of probes, Hydra probes and
TDR probes shown in Figure 2-9.
Stevens Hydra probes: These instruments measure the soil moisture and temperature at 5, 25, 50 and
100 cm depth with hourly measurement starting from 2005. Hydra probes are capacitance based
measuring instruments capable of measuring dielectric constant and conductivity with outputs in a
Campbell TDR probe
Stevens Hydra probe
Figure 2-9 Soil moisture probes.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
17
series of four voltages read by the data loggers in millivolts. The high frequency electrical
measurements indicating the capacitive and conductive properties of the soil are then directly related
to the soil moisture and soil temperature with a data reduction algorithm provided by the
manufacturer. Data filtering was required for some days as the instruments give values above the
potential moisture values (the porosity of the soil) as shown in Figure 2-10.
Campbell TDR probes: These instruments measure the soil moisture at the same depth but readings
have been taken every 14 days starting from 1999 (Figure 2-11). The TDR Reflectometer (Tectronix
1512c) generates a very short rise time electromagnetic pulse that is applied to the TDR probes and
then samples and digitizes the resulting reflection waveform for analysis and storage. The elapsed
travel time and pulse reflection amplitude contain information used by the onboard processor to
quickly and accurately determine the soil volumetric water content.
Guarena-REMEDHUS soil moisture 2007
0%
10%
20%
30%
40%
50%
60%
70%
Dec-06 Feb-07 Apr-07 May-07 Jul-07 Sep-07 Oct-07 Dec-07
date
so
il m
ois
ture
(%
vo
l)
F6
M5
H7
L7
O7
K9
K10
M9
N9
Q8
Figure 2-10 Soil moisture data from some Hydra probes (as seen, few stations need data filtering)
Guarena-REMEDHUS soil moisture 2007
0%
10%
20%
30%
40%
50%
Dec-06 Feb-07 Mar-07 May-07 Jul-07 Aug-07 Oct-07 Dec-07
date
so
il m
ois
ture
(%)
J3L3K4F6I6M5H7L7O7E10H9K9K10M9N9Q8F11H13J12M13J14H11I3
Figure 2-11 Soil moisture data TDR probes.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
18
The evolution of the soil moisture with precipitation for the years 2007-2008 is shown in Figure 2-12.
The precipitation is the average of three stations while the moisture is the average of the 22 stations in
the network measured using the hydra probes.
average soil moisture VS average precipitation 2007 and 2008(REMEDHUS)
0
5
10
15
20
25
30
Jan-07 Mar-07 May-07 Jun-07 Aug-07 Oct-07 Dec-07 Feb-08 Apr-08
date
so
il m
ois
ture
(%
vo
l)
0
10
20
30
40
50
60
pre
cip
ita
tio
n(m
m)
average precip. REMEDHUS average SM REMEDHUS
Figure 2-12 Average soil moisture versus average precipitation.
2.3.5. Discharge data
The functional discharge station at the outlet of the Guareña River has a ten minute recording data
since 2001 and a daily data starting from 1976.The data collected was also recording a non-desirable
discharge from the San Jose canal carrying water from the Duero River. Fortunately the dates when
the canal was flushed into the Guareña are known and filtering was performed easily. The partial
reading for the year 2002 is shown in Figure 2-13.
Guarena discharge 2002 (partial)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Jan-02 Jan-02 Mar-02 Apr-02 May-02 May-02 Jun-02 Jul-02
Time
pre
cip
ita
tio
n(m
m)
0
0.5
1
1.5
2
2.5
3d
isc
ha
rge
(m3
/s)
precip. Villa mor discharge station 129
Figure 2-13 Guareña discharge 2002.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
19
3. Pre processing of images
3.1. Introduction to MODIS images
The Moderate Resolution Imaging Spectroradiometer (MODIS) is a passive imaging
spectroradiometer carrying 490 detectors, arranged in 36 spectral bands that cover the visible and
infrared spectrum (Barbieri et al., 1997). MODIS is flown on board the satellites EOS AM-1(TERRA-
descending node) and EOS PM-(AQUA-ascending node). The two satellites were launched in 1999
and 2000 respectively orbiting the earth in a sun synchronous near polar orbit at 705 km. MODIS is a
high signal-to-noise instrument designed to satisfy a diverse set of oceanographic, terrestrial, and
atmospheric science observation needs. MODIS is making global moderate-resolution narrow-band
radiance observations over 36 spectral regions using a continuously rotating, double sided, scan
mirror which views the earth, internal calibrators, and space at 20.3 rpm: that is, one side of the mirror
traverses 360 degree every 1.477 seconds. The swath dimensions are 2330 km (cross track) x 10 ° of
latitude (along track at nadir) .The relevant spectral characteristics of the sensor are shown in Table
3-1 .
Table 3-1 Spectral characteristics of MODIS used in this research.
Band wavelength (µm) Resolution(m) Band 1 (VIS) 0.62 to 0.67
Band 2 (NIR) 0.841 to 0.876
250
Band 3 (VIS) 0.459 to 0.479
Band 4 (VIS) 0.545 to 0.565
Band 5 (NIR) 1.23 to 1.25
Band 6 (SWIR) 1.628 to 1.652
Band 7 (SWIR) 2.105 to 2.155
500
Band 31 (TIR) 10.78 to 11.28
Band 32 (TIR) 11.77 to 12.27
1000
3.2. Importing MODIS level 1B data into ILWIS
The Level 1B MODIS products (Table 3-2) do not directly contain images; rather they contain the
calibrated data that can be used by software applications to construct the images (Toller et al., 2006).
The calibrated MODIS earth view data are stored as scaled integers (SI) scientific data sets with in
Level 1B output files. The data available to users are stored in the Hierarchical Data Format (HDF).
These data were down loaded from the website http://ladsweb.nascom.nasa.gov/data/search.html
consisting of the calibrated Erath View data at 1km resolution, including the 250m and 500m
resolution aggregated to 1km resolution and also the Geo location files. The MODIS Re-projection
Tool Swath was then used to pre-process the nine bands required in SEBS along with the Geo
location files. Pre-processing in this software include spatial sub setting, re-sampling, projection and
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
20
converting into Geo TIFF format. Then the images in the Geo TIFF format were imported into ILWIS
using the GDAL import tool.
3.3. Pre processing for SEBS
3.3.1. Radiance and reflectance at TOA
The images imported into ILWIS using the GDAL import tool are stored in scaled integers (SI). They
need to be converted into radiance and reflectance values at the Top of Atmosphere (TOA) for further
processing. The conversion was performed using the scale and offset terms stored as attributes
contained in the level 1B outputs. To read the header files, the Software HDF Viewer, version 2.4 for
Windows Vista was used. The conversion from the scaled integers (SI) to reflectance and radiance
values is conducted using the following expressions as given by Xiong et al. (2005).
Reflectance = reflectance scale * (SI - reflectance offset)
Radiance = radiance scale * (SI – radiance offset)
The solar and satellite zenith and azimuth angles are corrected by multiplying the raw data by a factor
of 0.01. The radiance and reflectance scales and the offsets as read from the header files for each
image are tabulated in Appendix Table 1.
Table 3-2 MODIS level 1B products selected for further processing in SEBS.
Product group Selected bands MOD021KM EV_250_Aggr1km_REFSB_b0-band 1
EV_250_Aggr1km_REFSB_b1-band 2
EV_500_Aggr1km_REFSB_b0-band 3
EV_500_Aggr1km_REFSB_b1-band 4
EV_500_Aggr1km_REFSB_b2-band 5
EV_500_Aggr1km_REFSB_b3-band 6
EV_500_Aggr1km_REFSB_b4-band 7
EV_1km_Emissive_b10-band 31
EV_1km_Emissive_b11-band 32
MOD03 Solar azimuth
Solar zenith
Sensor azimuth
Sensor zenith
Height
3.3.2. Brightness temperature computation
The brightness temperature, the temperature for an ideal blackbody with an observed radiance, may be
calculated from Planck’s law. The Planck’s law is given as:
1
2 52
−=
−
Tk
hc
e
hCL
λ
λ 3-1
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
21
Where L is radiance in watts/m2/steradian/m, h is Planck’s constant (6.626 x 10-34 joule second), k is
Boltzmann’s gas constant (1.381x 10-23J/K), C is the speed of light (2.998 x 108m/s), λ is central
wave length in m and T is the temperature in K. Hence the brightness temperature for bands 31 and 32
can be computed after inserting the constants and inverting equation 3-1 as:
+
=1ln
52
1
πλλ
L
C
CT 3-2
Where C1= 0.0143843 mK and C2 = 3.74192 x 10-16 W/m2.
3.3.3. Atmospheric correction
Sensors on board satellites receive radiometric signals that require atmospheric correction to allow
multi-temporal processing. The influence in the radiance can be either additive or subtractive when
compared to the radiance of the target on the Earth’s surface. There are several atmospheric
corrections methods such as the Lowtran or Modtran, 5s or 6s, Turner and Spencer’s model etc.
According to Rahman and Dedieu (1994), Royer et al. (1988) compared a number of the so called
exact methods of atmospheric correction methods and found that 5S (Simulation of Satellite Signal in
the Solar Spectrum) is faster and simpler. However they iterated that even the 5S is too expensive and
time consuming to be used on an operational basis for large field of view instruments and developed
simpler and faster method, the SMAC (Simplified Method for Atmospheric Correction) algorithm
based on 5S for satellite measurements in the solar spectrum.
The SMAC algorithm is based on a set of equations with coefficients which depend on the spectral
band of the sensor. Advantages mentioned on the paper are faster correction performance, ability in
retrieving top of atmosphere reflectance (TOA) from ground reflectance or conversely retrieving
surface reflectance from the TOA reflectance and its capacity to be implemented on new sensors by
updating sensor coefficients only. However due to the simplifications adopted, the accuracy of the
method decreases if the solar and viewing angles are greater than 60° and 50° respectively and if the
horizontal visibility is less than 5km. Owing to this condition, the selected cloud free MODIS images
in this research are further screened and the number of selected images used has decreased from 22 to
13 accordingly.
3.3.4. Application of SMAC in ILWIS
SMAC in ILWIS is built with a user friendly interface to correct maps of the TOA reflectance. The
coefficients used in the atmospheric correction are also built as a data file in ILWIS for sensors
including ATSR2, SPOT, MERIS and MODIS. The maps and parameters required for the correction
are as follows:
TOA reflectance maps
A value map of the top of atmospheric reflectance at a particular band, in this case band 1 to band 7.
The values range between 0 and 1.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
22
Solar and sensor zenith angle maps or constant values
These are the values of the angles between the local zenith and the line of sight to the sun and to the
sensor. This information for MODIS images is found with the geo location files. After multiplying by
the appropriate factor given in the files, values range between 0° and 90°.
Solar and sensor azimuth angle maps or constant values
It is the angle between the line from the observer to the sun or satellite projected on the earth’s
surface and the line from the observer to the north measured clockwise. This information for MODIS
images is found with the geo location files. After multiplying by the appropriate factor, values range
between 0° and 360°.
Aerosol optical thickness maps at 550 nm or constant values
Aerosol optical thickness (AOT) describes the extent to which aerosols impede the direct transmission
of sunlight of a certain wavelength through the atmosphere. It is calculated by Ångstrom’s turbidity
formula as:
αβλτ −= 3-3
Where τ is the AOT, β is Ångstrom’s turbidity coefficient, λ is wavelength in micrometers, and α is
Ångstrom’s exponent. For SMAC τ at 0.550 µm is estimated from best fit AOT trend line of other
wavelengths as shown in Figure 3-1. The AOT data for this study was downloaded from the Aerosol
Robotic Network website (http://aeronet.gsfc.nasa.gov/). The nearest station to the study site having
all the required data for all selected imageries is the Caceres station. In this station level 1.5 data at
different wavelengths are observed and synchronized instantaneous measurements for each satellite
pass were collected. The values for this parameter range between 0.05 and 0.8.
Water vapor maps or constant values
This represents a physical simplification of the vertical distribution of water vapor in the different
atmospheric stratus. It is the weight of a column of 1 cm2 of atmospheric water assuming that all the
atmospheric moisture can be condensed. This data was also collected from the Aerosol Robotic
Network for each satellite overpass. Values range between 0 and 6 gcm-2.
Ozone concentration maps or constant values
This parameter represents the vertical concentration of ozone in the atmosphere. Total ozone over any
location can be obtained from the Ozone Processing Team of NASA through the website
http://jwocky.gsfc.nasa.gov/teacher/ozone_overhead_v8.html. Values range between 0 and 0.7
grams.atm.cm. The summary of all the above three parameters is shown in Table 3-3.
Surface pressure maps or constant values
This is the air pressure at the surface of the study area in hecto-Pascal. The variation in pressure of
each pixel can be calculated with the following formula as given in (Allen and FAO, 1998) using the
height map imported with the Geo location files.
26.5
293
0065.02931013
−=
ZP 3-4
Where P is the atmospheric pressure in hpa and Z is elevation in meters (DEM map) above sea level.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
23
Optical depth calculation at 550nm for 2007
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
wavelength(micrometers)
Aero
so
l O
pti
cal T
hic
kn
ess
Sep-01
Sep-04
Sep-07
Oct-10
Oct-13
Oct-19
Nov-01
Nov-03
Nov-05
Nov-14
Dec-13
Dec-16
Power (Sep-01)
Power (Sep-04)
Power (Sep-07)
Power (Oct -10)
Power (Oct -13)
Power (Oct -19)
Power (Nov-01)
Power (Nov-03)
Power (Nov-05)
Power (Nov-14)
Power (Nov-14)
Power (Dec-16)
Power (Dec-13)
Figure 3-1 Estimation of AOT at 550 nm.
Figure 3-2 AOT and water vapor data for September 07, 2007.
Table 3-3 Summary of AOT, Water vapor and Ozone data.
overpass date and time UTC AOT at 550 nm
Ozone (grams.atm.cm)
Water vapor (cm)
04/09/2007 11:05 0.224 0.301 1.850
07/09/2007 11:35 0.176 0.294 1.770
14/11/2007 11:10 0.031 0.269 0.650
16/12/2007 11:10 0.097 0.288 0.513
08/03/2008 11:40 0.073 0.352 1.35
27/04/2008 11:30 0.189 0.268 1.61
01/05/2008 11:05 0.095 0.348 0.65
18/06/2008 11:05 0.078 0.321 1.42
27/06/2008 11:00 0.117 0.297 1.5
30/06/2008 11:30 0.104 0.310 1.22
09/07/2008 11:25 0.104 0.308 1.21
27/07/2008 11:00 0.070 0.311 1.85
10/08/2008 11:25 0.094 0.298 2.12
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
24
MODIS coefficients for each band
These are ASCII file containing the description of the sensor calibration curve and responses for a
particular sensor and band. The coefficients are incorporated in SMAC for ILWIS for each sensor and
its specific bands.
The effect of the atmospheric correction can be visualized using the cross graph built in the ILWIS
software. As observed from the cross graphs of Figure 3-3 for the images of September 7, 2007, the
surface reflectance (y-axis) of some pixels has increased from the TOA reflectance (x-axis) while for
some others it has decreased due to the atmospheric correction.
Band 1
Band 2
Figure 3-3 Comparison of TOA reflectance with ground reflectance for Sept 7,2007.
Similarly for a randomly selected pixel on the irrigated area the change in broad band albedo before
and after the atmospheric correction is shown in Figure 3-4, as an example of the effect of the
algorithm.
0.00
0.05
0.10
0.15
0.20
Aug-07 Sep-07 Nov-07 Jan-08 Feb-08 Apr-08 Jun-08 Jul-08 Sep-08
TOA albedo surface albedo
Figure 3-4 Comparison of TOA broad band albedo with ground albedo for a single pixel.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
25
4. Analysis of point soil measurement values and up/down scaling
4.1. Introduction
The estimation of soil moisture from RS currently focuses mostly on the top surface layer of the
earth’s surface and its validation also requires ground measurement data from the same surface layer.
The REMEDHUS network as mentioned earlier has 23 soil moisture stations in which currently 22
are functional each having two types of instruments. In this study the Hydra probes are selected for
the validation process as they give hourly data which could be matched and compared with any
satellite overpass and its cloud free images for any given day. As the accuracy of these ground
measurements for validation of satellite derived data is vital, unreliable data from the measurements is
manually filtered to a maximum porosity value of 60% which was about 2.1 % of the total data.
4.2. Temporal stability analysis
The idea of temporal stability was explored in a number of researches (Cosh et al., 2004; De Lannoy
et al., 2007; Grayson and Western, 1998; Martinez-Fernandez and Ceballos, 2003; Wagner et al.,
2008) after its introduction in 1985. The idea is valuable in identifying a location of point
measurement which probably represents a field scale or large area soil moisture value. The two
statistical techniques commonly used in temporal stability analysis are the mean relative difference
and the standard deviation of the mean relative difference. The relative difference δij is defined as:
avg
avgij
ijS
SS −=δ 4-1
Where Sij is the volumetric soil moisture content at location i at time j and Savg is the area average soil
moisture content at time j calculated from the following formula:
∑=
=N
i
ijavg SN
S1
1 4-2
Where N is the number of measurement locations. The mean relative difference for each measurement
station therefore is defined as:
∑=
=t
i
ijiavgt 1
1δδ 4-3
Where t is the number of measurement hours or days. The standard deviation of the mean relative
difference for each ground measurement location is defined as:
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
26
21
1 1)( ∑
=
−
−=
t
j
iavgij
it
stdevδδ
δ 4-4
The mean relative difference indicates how the particular measurement location compares to the area
average soil moisture. This measure can be used to identify a representative site if it is close to zero.
A small standard deviation (close to zero) indicates that the particular location has a similar temporal
evolution in soil moisture as the area average soil moisture. If a measurement location has both these
properties it can be selected as a representative site that would predict the average field scale soil
moisture.
4.3. Results of temporal stability analysis
For the soil moisture measurements of the top 5 cm layer, the mean relative difference and the
standard deviation between the instrument locations and the area average of the whole REMEDHUS
network were calculated based on hourly and daily data for different time periods. The instrument
locations were ranked according to their mean deviation from the area average soil moisture as shown
in the following figures and tables. The error bars indicate one standard deviation above and below
the mean. The stations with negative mean relative difference are drier when compared with the field
average soil moisture and those with positive are wetter, while those with close to zero values have
about the same moisture as the area average.
Table 4-1 Mean relative difference and standard deviation for hourly moisture data.
rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
time 2006 to 2008 E10 I6 K4 K10 F11 J3 L3 O7 J14 H7 H13 M5 M9 F6 K9 L7 K13 Q8 N9 M13 H9 J12
mean relative diff. -92% -91% -82% -74% -70% -65% -60% -54% -41% -27% -26% -19% 19% 24% 35% 57% 58% 63% 66% 102% 132% 162%
Standard deviation 8% 9% 9% 19% 18% 15% 14% 18% 39% 19% 25% 63% 20% 22% 72% 25% 149% 74% 34% 55% 53% 62%
time 2005 to 2008 I6 E10 K4 K10 F11 J3 L3 O7 J14 H13 H7 M5 K9 M9 F6 K13 L7 Q8 N9 M13 H9 J12
mean relative diff. -90% -87% -83% -74% -71% -67% -62% -59% -31% -30% -21% -13% 21% 23% 32% 41% 51% 51% 66% 108% 127% 179%
Standard deviation 9% 15% 9% 21% 17% 18% 14% 21% 36% 25% 37% 67% 75% 29% 37% 149% 29% 67% 33% 55% 53% 98%
Mean relative difference based on hourly moisture
data(2006-2008)
E10 K4F11J3 L3
H13
M 9J14
J12
H9
M 13
N9
Q8
K13
K9
M 5K10
H7
F6
I6
O7
L7
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
0 2 4 6 8 10 12 14 16 18 20 22 24
Rank
Mean
rela
tive d
iffe
ren
ce
a
Mean relative difference based on hourly moisture
data(2005-2008)
K4F11J3 L3
H13
L7
O7
E10I6
K10
J12
H9
M 13
N9Q8
K13
F6M 9
K9
H7J14
M 5
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
0 2 4 6 8 10 12 14 16 18 20 22 24
Rank
Mean
rela
tive d
iffe
ren
ce
b
Figure 4-1 Mean and standard deviation of relative difference for hourly moisture data.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
27
Table 4-2 Mean relative difference and standard deviation for averaged daily data.
rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
time 2006 to 2008 E10 I6 K4 K10 F11 J3 L3 O7 J14 H7 H13 M5 M9 F6 K9 L7 K13 Q8 N9 M13 H9 J12
mean relative diff. -92% -91% -82% -74% -70% -65% -60% -54% -41% -27% -26% -18% 19% 24% 35% 57% 58% 63% 66% 102% 132% 161%
Standard deviation 8% 8% 8% 18% 17% 14% 13% 17% 37% 19% 24% 53% 19% 21% 72% 24% 148% 70% 33% 55% 52% 59%
time 2005 to 2008 I6 E10 K4 K10 F11 J3 L3 O7 J14 H13 H7 M5 M9 K9 F6 K13 Q8 L7 N9 M13 H9 J12
mean relative diff. -90% -87% -83% -73% -71% -67% -62% -59% -31% -30% -21% -12% 18% 21% 32% 41% 50% 51% 66% 109% 128% 178%
Standard deviation 8% 15% 9% 20% 17% 17% 14% 20% 35% 23% 36% 59% 36% 74% 37% 147% 66% 28% 32% 53% 51% 97%
The data was analyzed for different periods and different temporal scales. The different temporal
scales as shown in Figure 4-1 and Figure 4-2 are for hourly data and daily data respectively. This
helps in identifying variation, if any, for using different temporal scales. It can be seen that there is no
difference in the rank of the stations for both cases. The different periods of 2005 to 2008 and 2006 to
2008 show some change in the order of the rank for the stations with relatively low and medium mean
relative difference (H7 and H13, M5 and M9, F6 and F9, and L7, Q8 and K13). However the wettest
stations remain in the wettest zone and the driest do the same.
Mean relative difference based on daily
moisture data(2006-2008)
E10 I6 K4F11J3 L3
H7
M 9 F6
O7
K10
H13J14
J12
H9
M 13
N9
Q8
L7
K9
M 5
K13
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
0 2 4 6 8 10 12 14 16 18 20 22 24
Rank
me
an
re
lati
ve
dif
fere
nc
e
a
Mean relative difference based on daily
moisture data(2005-2008)
E10 K4F11J3 L3
H13
L7
O7K10
I6
N9
J12
H9
M 13
Q8
K13
F6K9
H7J14
M 5
M 9
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
0 2 4 6 8 10 12 14 16 18 20 22 24
Rank
Me
an
re
lati
ve
dif
fere
nc
e
b
Figure 4-2 Mean and standard deviation of relative difference-daily moisture data.
Table 4-3 Mean and standard deviation of relative difference for one year data-hourly data (year starts at the end of dry period in September)
rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
time 2005 to 2006 K13 I6 K4 E10 F11 J3 K10 O7 L3 H13 J14 K9 H7 M5 Q8 M9 L7 F6 N9 H9 M13 J12
mean relative diff. -97% -89% -85% -77% -73% -73% -72% -68% -65% -39% -10% -7% -4% 1% 28% 32% 40% 48% 63% 116% 120% 212%
Standard deviation 5% 9% 9% 21% 17% 23% 24% 25% 15% 21% 15% 73% 60% 74% 43% 42% 33% 54% 32% 51% 51% 140%
time 2006 to 2007 E10 I6 K4 F11 K10 J3 L3 O7 H13 H7 J14 M5 K13 F6 M9 K9 L7 Q8 N9 M13 H9 J12
mean relative diff. -91% -89% -82% -75% -72% -63% -61% -55% -27% -23% -21% -14% 1% 19% 23% 26% 58% 64% 75% 105% 148% 157%
Standard deviation 9% 10% 8% 14% 20% 17% 14% 19% 24% 23% 32% 87% 114% 18% 18% 74% 21% 75% 34% 56% 49% 68%
time 2007 to 2008 E10 I6 K4 K10 J3 F11 J14 L3 O7 H7 H13 M5 M9 F6 K9 L7 N9 Q8 K13 M13 H9 J12
mean relative diff. -93% -93% -82% -76% -67% -64% -60% -58% -54% -31% -25% -23% 15% 29% 43% 55% 58% 61% 94% 99% 117% 166%
Standard deviation 7% 8% 9% 18% 12% 19% 36% 13% 16% 15% 26% 19% 21% 23% 69% 28% 30% 72% 157% 54% 42% 55%
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
28
The data was also analyzed for distinct one year time periods starting and ending at the beginning of
the hydrological year as shown in Figure 4-3 and Table 4-3. The graphs illustrate that all except one
station (K13) keep their position in the wetter or drier region apart from the fact that there were
changes in the rank of the stations with in the regions. Measurement in Station K13 started in late
June 2006 and it reflected a different behavior for the year 2005-2006.
Considering all the above analysis it is clear that there are stations that demonstrate a consistent
behavior with relatively low mean relative difference and standard deviation even though not that
close to zero. Those stations can be selected as site representative stations for further up scaling and
validation processes. In line with this, stations F6, H7, H13, J14 and M9 were selected for further
consideration. The soil moisture values of these stations were plotted against the mean soil moisture
of the whole REMEDHUS network leaving the selected station out in the averaging.
Mean relative difference based on hourly moisture
data(2005_2006)
K4L3
H13
N9F6
J3I6
K10
K13K9
J14
M5H7 Q8
M9
L7
H9
M13
J12
E10
F11
O7
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
350%
400%
0 2 4 6 8 10 12 14 16 18 20 22 24
Rank
Me
an
re
lati
ve
dif
fere
nc
e
Mean relative difference based on hourly moisture
data (2006-2007)
I6 K4 K10J3 L3
H7
M9
K13
K9
J14
M5
O7F11
E10
Q8
J12H9
M13
N9L7
F6
H13
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
0 2 4 6 8 10 12 14 16 18 20 22 24
Rank
Me
an
re
lati
ve
dif
fere
nc
e
Mean relative difference based on hourly moisture
data (2007-2008)
H7
K4
K9
I6
F11J14
J3
M9
L7H13
M5
L3K10
E10
Q8
J12
H9
M13
K13
N9
F6
O7
-150%
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
0 2 4 6 8 10 12 14 16 18 20 22 24Rank
Mean rela
tivediffe
rence
Figure 4-3 Mean and standard deviation of relative difference for one year data-hourly data.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
29
4.4. Up/down scaling
As discussed in section 4.3 few stations were selected for further up/down scaling process after
checking the representativeness criteria. The values from the individual soil moisture stations were
then compared with the field scale average soil moisture. Stations M9 and F6 give better coefficient
of correlation (r>0.85) when compared with the field average soil moisture as shown in Figure 4-4
and were selected for further comparison and analysis.
M9(2005-2006) y = 0.8946x + 0.0395
R2 = 0.8701
0%
5%
10%
15%
20%
25%
30%
35%
0% 5% 10% 15% 20% 25% 30% 35%
mean of 21 stations
sta
tio
n M
9
F6(2006-2007) y = 1.0243x + 0.0223
R2 = 0.8433
0%
5%
10%
15%
20%
25%
30%
35%
0% 5% 10% 15% 20% 25% 30% 35%
Mean of 21 stations
sta
tio
n F
6
Figure 4-4 soil moisture of stations M9 and F6 against the whole REMEDHUS site.
According to De Lannoy et al (2007), a simple linear transformation model and a cumulative density
function matching were found to be the best conversion factors from point measurements to field
scale average soil moisture. The idea of simple linear transformation model was also explored by
Wagner et al. (2008) to describe a relation between point measurements and regional soil moisture
fields. In this research the same principle was adopted to downscale the pixel level soil moisture value
to a point scale soil moisture value for comparison. In general the form of the linear model is as
follows.
ba fp += θθ 4-5
Where θp is the point soil moisture value, θf is the field scale average soil moisture and a and b are
constants. In this research the coefficients of the best fit line obtained from the actual data as shown in
Figure 4-4 were used to scale down the pixel level soil moisture value obtained from the RS method.
4.5. Potential soil wetness
As explained in section 2.1.3.2 there are different expressions in literature defining the potential
wetness capacity of soils. In this study to estimate the soil moisture from the relation between the
relative evaporation and the relative soil moisture, the potential wetness map of the study area was
required. Data on moisture and physical properties of the soil are mostly available in the
REMEDHUS network and the comparison was limited in this region. To compute the wetness map
for the top soil, the required physical properties are the field capacity and the porosity. These
properties were computed based on the samples collected in previous studies from 150 points in the
REMEDHUS network and appended by the samples collected during the field work for this research.
Two alternatives were used in this research in computing the point potential wetness values. The first
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
30
one was the average of the field capacity and the porosity as computed by the formula given in
equation 2-9 and the second alternative was to use just the field capacity as it is. The point values
were then interpolated using the inverse distance method in ILWIS to produce the raster map as
shown in Figure 4-5 and values were compared with those given in literature. The aggregated field
capacity map as shown in the figure has mainly a value of 5-20%. This area as shown in the lithology
map of Figure 2-6 corresponds with the class ‘sands, sand micro conglomerates and clay’. For such
class of soils the field capacity value in literature ranges, for example, from 8 to 25 % as given in
(Dingman, 2002).
Figure 4-5 Alternative top soil potential wetness maps for REMEDHUS network.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
31
5. The Surface Energy Balance System-SEBS
5.1. Introduction
The Surface Energy Balance System (SEBS) was developed to estimate atmospheric turbulent fluxes
and surface evaporative fraction using satellite data in the visible, near infrared, and thermal infrared
frequency range in combination with meteorological data. It requires three sets of information as
inputs. The first set consists of land surface albedo, temperature, fractional vegetation coverage and
leaf area index, and the height of the vegetation (or roughness height). The second set consists of
meteorological data like air temperature and pressure, humidity and wind speed at a reference height.
The third set includes downward solar radiation and downward long wave radiation. Most of the
following extracts are based on the SEBS article (Su, 2002b) and related ancillary papers. In general
the processes in SEBS are schematized as shown in Figure 5-1.
5.2. Estimation of parameters
The outputs of the pre processed MODIS images in the visible, near infrared and thermal bands in the
years 2007 and 2008 were used for further processing and estimation of the surface parameters
required in the SEBS algorithm as discussed in the following sections.
5.2.1. Surface and bio physical parameters
Surface roughness length for momentum transport
In this study the land cover in the study area was attributed with Zom values from literature as shown
in Figure 5-2. The vegetation height (h) and the displacement height do in SEBS then are
approximated by the following formulas.
136.0omZh = 5-1
hdo3
2= 5-2
Surface roughness length for heat transport
The scalar roughness height for heat transfer, Zoh, is computed as:
)exp( 1−
=kB
ZZ om
oh 5-3
Where kB-1 is the excess resistance for heat transfer and is estimated in SEBS as follows:
21
*
)(2
2/*
1*
)1(2
)1()(
4vs
t
h
z
hu
u
vvvn
t
d PkBC
kPPP
ehu
uC
kCkB
om
ec
−
−
− +−+−
= 5-4
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
32
Where Cd is the drag coefficient of the foliage elements, u (h) is the horizontal wind speed at the
canopy top, Ct is the heat transfer coefficient of the leaf, nec is the with-in canopy wind speed profile
extinction coefficient, Pv is the fractional canopy coverage, Ct* is the heat transfer coefficient of the
soil, and kBs-1 is the value for bare soil.
MODIS
images
VIS,NIR,TIR
Brightness
temperatureLand use land
cover map
NDVI
Pre-processing
Calibration and
atmospheric
correction
Ground surface
reflectance
Surface
albedo
Emissivity &
emissvity
difference
Pv LAI Zom
KB-1
Zoh
Ancillary
parameters
LST
NET RADIATION
Meteorological data
Air pressure
air temperature
air humidity
wind speed
solar radiation
Similarity theory
Soil heat flux
Dry sensible
heat flux
Hdry
Wet and actual
sensible heat flux
External and
internal
resistances
Potential
evaporation LEwet
Actual latent heat
flux
Evaporative
fraction
Relative
evaporation
Soil potential
wetness map
Soil moisture
Actual daily
evapotranspiration
Figure 5-1 SEBS processes.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
33
Land surface albedo
The land surface broad band albedo was computed from six surface reflectance maps corrected for
atmospheric effects. The formula by Liang (2001) was used.
0015.07018.0
5112.04116.03243.02291.01160.0
−
+++++=
b
bbbbbα 5-5
Where α is the broad band albedo and b1 to b5 and b7 are atmospherically corrected surface
reflectance bands from MODIS.
Figure 5-2 Land cover and momentum roughness height (Zom) map for Guareña and environs.
NDVI
The Normalized difference vegetation index was calculated from the atmospherically corrected red
and near infrared bands (band 1 and band 2 of MODIS respectively) with the following equation:
21
12
bb
bbNDVI
+−
= 5-6
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
34
Land surface emissivity
The land surface emissivity map was calculated for four different types of surfaces based on NDVI,
vegetation cover and albedo values as given in Sobrino and Raissouni (2000) and adapted in SEBS
for MODIS in Lichun (2008).
• For bare soils, NDVI < 0.2 and emissivity and emissivity difference are given respectively as:
1051.09825.0 bande ×−= 5-7
10041.00001.0 bande ×−−=∆ 5-8
• For mixed pixels 0.2=<NDVI<=0.5 and emissivity and emissivity difference are given
respectively as:
VPe ×+= 018.0971.0 5-9
)1(006.0 vPe −×=∆ 5-10
Where Pv is the vegetation cover given by the following formula:
2
minmax
min
−
−=
NDVINDVI
NDVINDVIPv 5-11
Where NDVImax=0.5 and NDVImin=0.2 and for pixels with NDVI value of less than 0.2 Pv is 0
(bare land) and if greater than 0.5 the pixel is assumed to be fully vegetated and a Pv value of 1 is
assigned.
• For vegetation pixels NDVI > 0.5
990.0=e 5-12
0=∆e 5-13
• For water surfaces surface albedo is less than 0.035 and
995.0=e 5-14
Leaf area index (LAI)
The leaf area index used in calculating the kB-1 is computed in the current model using a formula from
Su (2000) as cited in Lichun (2007) is as follows:
2/1
6101
)1(*
+−
+=
−NDVI
NDVINDVILAI 5-15
Land surface temperature (LST)
To compute the LST, a formula developed by Sobrino and Raissouni (2000) based on split window
technique, and adapted for MODIS was used.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
35
ewew
wbtmbtmw
btmbtmwbtmLST
∆∗−−−∗−
+−+−∗−
−−∗++=
)4.20119()1()35.75.64(
)067.002.0()21()08.0026.0(
)21()2.097.1(1
5-16
Where btm1 and btm2 are band 31 and 32 brightness temperatures respectively and w is water vapor
content. If there is no data for water vapor it can be calculated from the following formula as given in
Li et al.(2004) and adapted for MODIS by Lichun (2008).
32
31662.1373.13T
Tw ∗−= 5-17
Where: T31 and T32 are the transmittances of band 31 and band 32 respectively.
5.2.2. Weather and other parameters
Reference height
The meteorological variables in the study area are measured from stations having a 2m height above
the ground surface. When land use maps were used to assign Zom values the reference height was
scaled to 10 m and when the NDVI approach was used to estimate the Zom values the reference height
was taken at 2m.
PBL height
The thickness of planetary boundary layer which directly affects the turbulent fluxes varies between
500m and 2000m (Brutsaert, 1982). In this study a constant height of 1000m was considered for all
days.
Specific humidity
The specific humidity q is defined as the mass of water vapor per unit mass of moist air (Brutsaert,
1982).
dv
vqρρ
ρ+
= 5-18
Where: ρv and ρd are the density of the water vapor and density of the dry air without the water vapor
respectively. After some simplification the specific humidity can be calculated as:
))1(( eP
eq
εε−−
= 5-19
Where q is in kg/kg, ε is the ratio of the molecular weights of water and dry air (0.622), P is the total
air pressure in hpa calculated from equation 3-4 and e is the partial pressure of the water vapor in hpa.
The final input was a single constant value from the average of the three weather stations with in the
study area.
Wind speed
Instantaneous measurements of the wind speed(m/s) at 2m and scaled to speed at 10 m were averaged
for the overpass time from four weather stations and a constant value was used. The formula for
scaling up to 10m is shown in Appendix table 5.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
36
Air temperature
Instantaneous measurements (°C) at 2m were averaged for the overpass time from three weather
stations and a constant value was used. The SEBS algorithm requires information on air temperature
at one point in the actual version of the software.
Pressure at reference height
The air pressure was calculated using equation 3-4 from the height map imported along with the other
MODIS images. A height of 2m or 10m was added on the height map to derive the pressure at the
required reference height.
Pressure at surface
The air pressure in units of Pascal was calculated using equation 3-4 from the height map imported
along with the other MODIS images.
All the final inputs of the above parameters for the SEBS processing are shown below in Table 5-1.
Table 5-1 Instantaneous weather parameters at satellite over pass time.
overpass date and time UTC
instantaneous wind speed at 2m and 10m
(m/s)
instantaneous vapor pressure
(kpa)
instantaneous relative
humidity (%)
instantaneous temperature
(°C)
instantaneous specific
humidity (kg/kg)
visibility estimated
(km)
instantaneous solar radiation
(w/m2)
04/09/2007 11:05 4.62,5.63 1.199 65.47 16.28 0.0081 27 570
07/09/2007 11:35 3.02,3.68 1.115 54.68 18.04 0.0076 39 658
14/11/2007 11:10 0.99,1.42 0.679 81.66 4.43 0.0046 244 410
16/12/2007 11:10 2.15,3.09 0.411 83.97 -2.90 0.0028 98 325
08/03/2008 11:40 1.26,1.81 0.657 61.63 8.18 0.0044 141 638
27/04/2008 11:30 2.01,2.88 1.354 59.86 19.78 0.0092 35 670
01/05/2008 11:05 1.25,1.79 0.639 53.43 10.11 0.0043 100 678
18/06/2008 11:05 1.96,2.39 1.196 52.14 20.24 0.0081 130 723
27/06/2008 11:00 3.51,4.27 1.394 61.83 19.60 0.0095 74 692
30/06/2008 11:30 1.89,2.30 1.516 62.51 20.81 0.0103 89 745
09/07/2008 11:25 0.88,1.07 0.828 29.33 23.04 0.0056 89 769
27/07/2008 11:00 1.20,1.46 1.478 58.67 21.24 0.0100 147 643 10/08/2008 11:25 1.69,2.05 1.207 60.74 17.41 0.0081 120 753
5.3. Energy balance components
Net radiation
The net radiation is defined as the difference between the incoming short wave radiation and the
outgoing long wave radiation or the sum of the net shortwave radiation and the net long wave
radiation. In equation form it can be described as:
netsnetn LRR += 5-20
The net short wave radiation component of the net radiation is calculated by:
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
37
swdR)1(R snet α−= 5-21
The net long wave radiation component of the net radiation is expressed as:
4sinnet TLL εσε −= 5-22
Where Rsnet is the net shortwave radiation, Rswd is the incoming shortwave solar radiation, Lnet is the
net long wave radiation, Lin is the incoming long wave radiation all in Wm-2, α is the surface albedo, ε
is the surface emissivity, σ is the Stefan-Boltzmann constant which is equal to 5.67 x 10-8 Wm-2K-4
and Ts is the surface temperature in K.
The incoming long wave radiation, Lin can be computed from the following formula.
4aain TL σε= 5-23
Where εa is the atmospheric emissivity and Ta is the air temperature in K at the reference height. The
atmospheric emissivity can be estimated as given in (Su, 2002a) :
26 )15.273(102.9 +×= −
aa Tε 5-24
In SEBS α, ε and Ts can be derived from RS data from the visible to the infrared regions of the
electromagnetic spectrum. In this research the instantaneous incoming shortwave solar radiation was
collected from weather stations. How ever it can also be calculated in the algorithm using the
following formula as given in Iqbal (1983).
τθ m
zoscswd eeIR −= cos 5-25
Where Isc = 1367 Wm-2 is the solar constant, eo is the eccentricity factor, θz is the solar zenith angle,
m is the air mass and τ is the optical thickness. These last two can be replaced by the overall
transmissivity calculated from broadband solarimeters on the ground.
Soil Heat flux
Soil heat flux when compared with the other energy terms is small or negligible especially for
computations on daily basis. It should however be considered for computations on hourly basis or for
instantaneous calculations in RS. In RS applications empirical relations are used. The soil heat flux is
related with the net radiation and the type of the surface whether it is bare soil or fully vegetated or
mixed. In SEBS it is given as:
))(*)1(( csvcnO TTPTRG −−+= 5-26
Where Tc, which is the ratio of soil heat flux to net radiation for full vegetation canopy, is equal to
0.05, Ts is equal to 0.315 for bare soil and Pv is the fractional vegetation coverage as computed in
section 5.2.1.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
38
Sensible heat flux
Sensible heat flux is the flow of energy due to the temperature gradient of the air upwards or
downwards depending on the time of the day. In the day time it is directed upwards and during the
night it is downwards. In SEBS the sensible heat flux is derived independent of the other energy
balance components and the derivation requires only the wind speed and temperature at the reference
height and the surface temperature. It is calculated by solving the system of three non linear equations
(equations 5-27 to 5-29) involving the friction velocity and the Obukhov stability length.
Ψ+
−Ψ−
−= ∗
L
z
L
dz
z
dz
k
uu om
mo
m
om
oln 5-27
Ψ+
−Ψ−
−=−
L
z
L
dz
z
dz
Cku
H ohh
oh
oh
o
P
ao ln*ρ
θθ 5-28
Where u is the mean wind speed in ms-1, θo is the potential temperature at the surface in K, θa is the
potential air temperature in K at height z, H is the sensible heat flux in Wm-2, u* = (τo/ρ) 1/2 is the
friction velocity in ms-1, τo is the surface shear stress in Nm-2, ρ is the density of air in kgm-3, k=0.4 is
the von Karman’s constant, z is the height above the surface in m, do is the zero plane displacement
height in m, Zom is the roughness height for momentum transfer in m, Zoh is the scalar roughness
height for heat transfer in m, Ψm and Ψh are the stability correction functions for momentum and
sensible heat transfer respectively, and L is the Obukhov stability length in m defined as the ratio
between the kinetic energy produced by convective and mechanical forces:
kgH
uCL vP θρ 3
*−= 5-29
Where g is the acceleration due to gravity in ms-2and θv is the potential virtual temperature in K near
the surface.
Limits of sensible heat flux
The actual sensible heat flux defined in equation 5-28 is constrained in the range set by the sensible
heat fluxes at the dry limit and the wet limit considering the energy balance at limiting cases.
• Sensible heat flux at the dry limit (Hdry)-Under the dry-limit, the latent heat (λEdry) becomes
zero due to the limitation of soil moisture and the sensible heat flux is at its maximum value
and from equation 2-1 it follows that:
Ondry GRH −= 5-30
• Sensible heat flux at the wet limit (Hwet) – Under the wet limit where the evaporation takes
place at the potential rate (λEwet), the sensible heat takes its minimum value (Hwet),
wetonwet EGRH λ−−= 5-31
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
39
The sensible heat at the wet limit can be derived by combining equation 5-31 with the Penman-
Monteith combination equation 2-3 repeated here as equation 5-32 in which the resistance terms are
grouped into the bulk internal or surface resistance (ri ) and the external or the aerodynamic resistance
(re) both in sm-1.
i
aspane
r
eeCGRrE
γγ
ρλ
++∆
−+−∆=
)(
)()( 5-32
At the wet-limit, the internal resistance ri is zero by definition. Inserting this value into equation 5-32
and solving for the sensible heat flux at the wet limit:
∆+
−−−
=
γ
γ
ρ
1
*)(ee
r
CGR
H
s
ew
p
on
wet 5-33
The external resistance depends also on the Obukhov length, L, which in turn is a function of the
friction velocity and sensible heat flux (equation 5-29). The external resistance is calculated from
equation 5-28 as:
Ψ+
−Ψ−
−=
L
z
L
dz
z
dz
kur oh
ho
h
oh
oe ln
1
*
5-34
And the same for the external resistance at the wet limit:
Ψ+
−Ψ−
−=
w
ohh
w
oh
oh
oew
L
z
L
dz
z
dz
kur ln
1
*
5-35
The stability length at the wet limit can be determined as:
λ
ρ)(*61.0*
3*
on GRkg
uL
−= 5-36
Relative evaporation
The relative evaporation Λr is evaluated as:
E
EE
E
E wet
wet
r λλλ
λλ −
−==Λ 1 5-37
Substitution of equations 2-1, 5-30 and 5-31, into equation 5-37 and after some algebra:
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
40
wetdry
wet
rHH
HH
−
−−=Λ 1 5-38
Evaporative fraction
The evaporative fraction is defined as the ratio of the latent energy to the available energy.
)()( on
wetr
on GR
E
GR
E
−
Λ=
−=Λ
λλ 5-39
Latent heat flux
Finally by inverting 5-39 the instantaneous latent heat flux can be calculated.
)( on GRE −Λ=λ 5-40
Daily AET
Assuming that the daily evaporative fraction is the same as the instantaneous evaporative fraction
given by equation 5-39 and also assuming that the net daily soil heat flux is close to zero, the actual
daily latent heat of evapotranspiration from the average daily net radiation can be calculated as:
dailyndaily RE Λ=λ 5-41
The daily net radiation can be calculated using equations 5-21 to 5-23 by averaging the incoming and
outgoing short and long wave radiations to 24 hour period. Finally the evaporation in mm day-1 is
given as:
ndailydaily RE Λ= 0353.0 5-42
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
41
6. Hydrological modelling (HBV)
6.1. Introduction
The HBV model is a semi distributed conceptual model. The approach uses sub basins as primary
hydrological units, and with in these an area elevation distribution and a simple classification of land
use (forest, open cover and lakes) are made (Rientjes, 2007). The model consists of subroutines for
snow accumulation and melt, soil moisture accounting procedure, routines for run off generation and
simple routing procedure. The schematic of the model is shown in Figure 6-1.
SF =snow fall
RF=rainfall
IN= infiltration
EA=actual evaporation
EI=evaporation from interception
EL=evaporation from lake
LP=limit for potential evaporation
SM=soil moisture storage
FC=max. soil moisture storage
CF=capillary rise
R=seepage
UZ=storage in upper response
box
PERC=percolation
LZ=storage in lower response box
Qo=direct run off from upper box
Q1=base flow from lower box
Q=total discharge
Figure 6-1 Schematic representation of HBV model (SMHI).
Model input data have been kept simple. Input information to the model include precipitation records
on daily or shorter time steps, air temperature records, monthly estimates of evapotranspiration, runoff
record for calibration and geographical information about the river (SMHI, 2006).
6.2. Digital elevation model
Elevation information for the study area was obtained from ftp://e0srp01u.ecs.nasa.gov/srtm/ which
provides version 2 of the SRTM 90m elevation data. SRTM stands for the Shuttle Radar Topography
Mission flown on Space Shuttle Endeavour in February 2000. Two SRTM3 1 degree tiles namely,
N40W006 and N41W006 were imported into ILWIS and after assigning geographic coordinates a
mosaic containing the whole study area was created. Using the mosaic image and the HYDRO
processing tool in ILWIS the catchment boundary and the drainage were extracted.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
42
Figure 6-2 Guareña catchment with SRTM extracted and digitized rivers(digitized rivers source Eng. Guido Baroncini, University of Salamanca).
As illustrated in Figure 6-2 the drainage network is comparable with the digitized river network from
areal photo map produced for another study in the area. Some attributes of the catchment as computed
in ILWIS HYDRO processing are shown in Table 6-1.
Station Area
(km2)
Weight
(%)
elevation
(m)
Villamor 364.0 34 890
VA_02 615.4 58 766
ZA_03 76.5 8 639
Figure 6-3 Coverage of the precipitation stations in the catchment.
Table 6-1 Some characteristics of the Guareña basin as computed in ILWIS HYDRO processing.
Description unit quantity
Total area Km2 1055.8
Total drainage length km 337.4
Longest drainage length km 83.3
Longest flow path length km 88.3
Drainage density m/km2 319.6
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
43
6.3. Catchment behavior and assessment of discharge data
The Guareña catchment has three meteorological stations with in the basin and two more at the edge
of the catchment as shown in and Figure 6-3 . The two stations in the center of the catchment have a
one year old data only. The longest data within the basin is recorded only in the Villamor station
which is also situated at the edge of the catchment. The station recording discharge is currently only
one.
The behavior of the catchment in response to rainfall events is seen to be varying every year. At times
there could be no response at all and at times a normal response hydrograph is observed. The
distribution of the rainfall and response of the sub basins would have been understood better if there
were more discharge stations. The graph in Figure 6-4 shows contrasting discharge and top soil
moisture responses for the rain events in the years 2007 and 2008. Another interesting point is the
amount of the discharge recorded at the out let. As shown in Figure 6-5 and Figure 6-6 for the yearly
and the monthly data, the ratio of the discharge to the input rainfall is very small (shown in the boxes
for the yearly data).
Rainfall vs discharge and soil moisture
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
01/01/07 02/03/07 01/05/07 30/06/07 29/08/07 28/10/07 27/12/07 25/02/08 25/04/08
date
dis
ch
arg
e(h
ecto
lite
r/sec)
an
d
so
il m
ois
ture
(%
vo
l)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
pre
cip
itati
on
(mm
)
average precip average SM discharge measured
Figure 6-4 Catchment response for rainfall events in Guareña (discharge is in hectolitres/sec).
0.03 0.05 0.07 0.04 0.020.04
0.0
200.0
400.0
600.0
year
2002
year
2003
year
2004
year
2005
year
2006
year
2007
t i me
rain
fall
an
d d
isch
arg
e (
mm
)
rainfall discharge
0.0
20.0
40.0
60.0
80.0
100.0
120.0
Oct Jun Feb Oct Jun Feb Oct Jun Feb Octtime
rain fall(mm) discharge (mm)
Figure 6-5 Yearly and monthly rainfall and discharge comparison with the discharge to rainfall ratios
shown in the boxes.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
44
0.0
20.0
40.0
60.0
80.0
100.0
Oct
Jan-
2001
Apr Jul
Oct
Jan-
2002
Apr Jul
Oct
Jan-
2003
Apr Jul
Oct
Jan-
2004
Apr Jul
Oct
Jan-
2005
Apr Jul
Oct
Jan-
2006
Apr Jul
Oct
Jan-
2008
Apr
time
rain
fall a
nd
dis
ch
arg
e (
mm
)
rain fall(mm) discharge (mm)
Figure 6-6 Monthly rainfall and discharge comparison.
.
Elevation class (m)
Land cover
Area(km2) Percentage (%)
700 field 28.94 2.8 700 forest 2.75 0.03 800 Field 379.43 38.49 800 Forest 10.28 1.00 900 Field 58334 56.49 900 forest 12.42 1.20
Figure 6-7 Reclassified land cover and elevation maps of Guareña as required in HBV.
6.4. Input data
In HBV the input data required are rainfall, temperature, PET and measured discharge data. The
potential evaporation was estimated using the FAO Penman-Monteith formula. All the meteorological
data and the discharge data were prepared in text format read by the program. The elevation and
weights of each recording station are also included as input. For the modeling three meteorology
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
45
stations having the longest precipitation data namely, Villamor, VA_02 and ZA_03 were selected out
of the five stations and the weight of each station was computed based on the Thiessen polygon
method as shown in Figure 6-7. In addition the proportion of each land cover type on each elevation
class is an input data for the model. The land cover and the elevation were reclassified based on the
model guide line which states that any land cover apart from water body and forest is classified as
field. Accordingly the land cover was reclassified into two classes namely field and forest. The
percentages of each land cover class with the reclassified elevation maps are also shown in Figure 6-7.
6.5. Model parameters
The general classifications of the parameters in the HBV modeling are found in the SMHI manual
(SMHI, 2006). For this specific study the following parameters were used in the calibration process.
Seven of them (except cflux) were found to be the most important sensible parameters in previous
HBV studies (Booij et al., 2007).
Soil moisture routine parameters:
Fc Field capacity (mm).
Lp Limit for potential evaporation.
Beta Exponent in formula for drainage from soil.
Cflux Maximum capillary flow from upper response box to soil moisture zone (mm/day). Response routine parameters:
Khq Recession coefficient for the upper box when water discharge equals hq. hq is
calculated using the discharge data and the area of the catchment using the equation
given in the manual.
Perc Percolation capacity from upper to lower response box (mm/day).
k4 Recession coefficient for lower response box (day-1).
Alfa measure of the non linearity of the discharge from the upper reservoir.
6.6. Model calibration
Initially the process of model calibration in this study was attempted manually. As explained in
section 6.3 the strange behavior of the catchment couldn’t allow the manual calibration. The next step
was to try the calibration automatically. To this end the Monte Carlo Simulation was used. According
to Booij et al (2007) the Monte Carlo Simulation is a technique, where through numerous model
simulations with randomly generated model parameter sets, an optimum value for the objective
functions was sought. The two objective functions used in this study in selecting the optimum
parameters are as follows.
The Nash-Sutcliffe coefficient (NSC): This coefficient of efficiency is used to assess the
predictive powers of models and the values can range from -∞ to 1. If the value is grater than 0.75 the
model is said to have stronger predictive power. The formula is given as follows.
∑
∑
=
=
−
−−=
n
i
ioio
n
i
isio
R
1
2)()(
1
2)()(
2
)(
)(
1 6-1
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
46
Where Qo(i) is observed discharge, Qs(i) is simulated discharge )(ioQ is the average of the observed
discharge.
The Relative volume error (RVE): This is given by the following formula. The values of this
function range from -∞ to ∞. The closer the value to zero the better the performance of the model is.
%100*
1)(
1 1)()(
−
=
∑
∑ ∑
=
= =n
i
is
n
i
n
i
isio
Q
RVE 6-2
Where the terms are as defined for the NSC.
6.7. Results and discussion
The available data for the calibration and validation was all in all 6 and half year long starting
September 21, 2001 and ending May 31, 2008. It should be noted that there is data for the discharge
starting from 1976 but the corresponding meteorological data available is only from 2001. The first
run of the simulation was done for the period starting September 21, 2001 and ending September 21,
2005.The model parameter space for the parameters specified in section 0 and the corresponding
optimum parameters selected, after the Monte Carlo Simulation for the HBV was run 60000 times, are
shown in Table 6-2. The corresponding NSC value for this run was 0.522. Visual inspection of the
result shows not much agreement between the modeled and observed discharges.
Table 6-2 Model parameters for the first run in HBV
Parameter Interval Optimum value
Fc 100-1500 1143.1
Beta 1-4 1.02779
Lp 0.1-1.0 0.19309
alpha 0.1-3.0 0.27245
Khq 0.0005-0.15 0.01186
K4 0.0005-0.15 0.01804
Perc 0.1-2.5 0.39963
cflux 0-2 0.60911
Since the first run was not satisfactory, the actual discharge hydrograph was inspected visually and
part of the hydrograph which has normal response to the rainfall was selected for re running. Two
runs were conducted. For the period June 2002 to September 2004 the results are even worse with a
NSC value of 0.392. For the period starting June 2003 and ending September 2004 relatively better
results were obtained. The corresponding results are shown in Table 6-3 and Figure 6-8. The
corresponding values for the NSC and RVE are 0.728 and 6.649 % respectively.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
47
Table 6-3 Model parameters for the period June 2003 to September 2004.
Parameter Interval Optimum value
Fc 100-1500 536.6
Beta 1-4 1.71932
Lp 0.1-1.0 0.36050
alpha 0.1-3.0 0.86212
Khq 0.0005-0.15 0.03457
K4 0.0005-0.15 0.02468
Perc 0.1-2.5 1.07377
cflux 0-2 1.07589
Observed vs simulated discharge
0
0.5
1
1.5
2
2.5
3
8/30/2003 10/30/2003 12/30/2003 2/29/2004 4/30/2004 6/30/2004 8/30/2004
time
dis
ch
arg
e (
m3
/s)
0
6
12
18
24
30
36
rain
fall (
mm
)
Rainfall Qobs Qsim
Figure 6-8 Observed and simulated hydrograph for the period June 2003 to September 2004.
The aim of running and calibrating the hydrological modeling was to compare the outputs of the soil
moisture from this model with the results found in the RS method and the measured values from the
hydra probes on catchment level. In general for the whole period the simulated discharge is not in
good agreement with the observed discharge and hence no comparison could be made.
Possible reasons for this mismatch could be attributed to the high infiltration of the water to the deep
ground due to the sandy texture of the soil or a large amount of abstraction from the catchment which
most probably dries the aquifer throughout the whole season. The area percentage of the irrigated land
in the Guareña catchment is about 8 % (80 km2) as estimated from the available land use map. A
simple calculation of water requirement for maize or beet in the irrigated area reveals that the amount
of water consumed may reach up to 25 % the total water input to the area. On the other hand the
effluent from the small towns in the area may have the opposite effect even though the quantity might
be very small. To identify the causes proper monitoring of all the abstractions in the area is essential.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
48
7. Analysis of results and discussion
In this chapter the results and findings of the outputs of this thesis will be discussed. First the soil
moisture results obtained from the RS methods are compared with the selected ground stations.
Second the average field scale soil moisture from the RS will be compared against the average of all
soil moisture stations in the study area. Then the AET will be compared with the complementary
approach method and the single crop coefficient (Kc) for wheat. Then the effect of the type of input
data on the performance of the SEBS algorithm will be discussed. The limitations observed are also
mentioned at last.
7.1. Comparison of soil moisture
7.1.1. Selected soil moisture stations
In this research top soil moisture retrieval was attempted from RS methods developed for estimation
of surface turbulent fluxes. Comparison of ground point measurements with RS derived information is
a difficult task if not impossible due to the disparity between the scales of the ground measurements
and the RS. The comparison was approached with the temporal time stability analysis method as
discussed in chapter 4. Two alternatives were considered for the potential wetness capacity of the top
soil. The first one was the average of the porosity and the field capacity (equation 2-9) and the second
one was the field capacity. A preliminary result from the first option indicated overestimation and was
dropped from further consideration. In the second option the results of the relative evaporation from
the SEBS algorithm were multiplied by the field capacity of the soil to get the soil moisture. The
results for the pixels containing station M9 and F6 (refer section 4.4) are shown in Figure 7-1.
As seen from the graphs there is no definite correlation between the measured soil moisture and the
RS derived soil moisture values for station M9 and the correlation with station F6 is weak. In case of
station M9 the RS method overestimates the moisture values while it underestimates the moisture in
the case of station F6. The results were also checked if there is any dependence on the season of the
year. A better correlation was observed for the dry season in the case of station M9 but for F6 there
was no improvement.
The results of the temporal analysis in section 4.3 show that the best values close to zero are in the
range of +15-20%.In the temporal stability analysis there are no well defined limits as to how close
should the mean relative difference be to zero. The results of the temporal analysis in this study are as
such not close to zero and this might have contributed for the weak correlation at the point scale.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
49
Julian day
SEBS derived soil moisture
for Pixel containing station M9
Pixel M9 down scaled to point
value θp=0.89*θr+0.03
station M9 measured by hydra probe
247 9.6% 12.5% 14.5%
250 13.6% 16.1% 13.2%
318 12.9% 15.5% 12.9%
350 10.0% 12.9% 17.0%
68 22.4% 24.0% 17.8%
118 21.6% 23.3% 13.6%
122 17.2% 19.3% 12.0%
170 22.0% 23.6% 18.7%
179 16.6% 18.8% 13.1%
182 14.6% 17.0% 12.4%
191 20.1% 21.9% 10.6%
211 17.0% 19.1% 9.2%
223 14.9% 17.3% 8.3%
soil moisture comparison SEBS
derived vs station M9
R2 = 0
0.0%
4.0%
8.0%
12.0%
16.0%
20.0%
24.0%
28.0%
0.0% 4.0% 8.0% 12.0% 16.0% 20.0% 24.0% 28.0%
SEBS dow n scaled
M9 m
easu
red
Julian day
SEBS derived soil moisture
for Pixel containing station F6
Pixel F6 down scaled to point
value θp=1.02*θr+0.02
station F6 measured by hydra probe
247 7.8% 10.2% 8.8%
250 6.4% 8.8% 8.5%
318 5.3% 7.7% 16.5%
350 11.5% 14.0% 18.8%
68 11.5% 14.0% 23.6%
118 9.2% 11.6% 24.0%
122 6.4% 8.8% 21.7%
170 9.8% 12.3% 17.7%
179 7.1% 9.5% 15.5%
182 7.3% 9.7% 15.7%
191 9.7% 12.2% 14.3%
211 8.7% 11.2% 12.2%
223 7.1% 9.5% 9.9%
soil moisture comparison SEBS
derived vs station F6
R2 = 0.2
0.0%
4.0%
8.0%
12.0%
16.0%
20.0%
24.0%
28.0%
0.0% 4.0% 8.0% 12.0% 16.0% 20.0% 24.0% 28.0%
SEBS down scaled
F6 m
easu
red
Figure 7-1 Comparison of ground measured and SEBS derived soil moisture (2007-2008).
7.1.2. Field scale average soil moisture
The field scale average soil moisture from the soil moisture stations is the arithmetic average of the 22
stations in the REMEDHUS network. Data from each station was available during each satellite
overpass time. The RS average is also the arithmetic mean of all pixels in the network. Figure 7-2
shows the results obtained. The spatial variation of the soil moisture is also shown for some selected
days in Figure 7-3. The result illustrates that there is a good matching between SEBS estimated soil
moisture values and ground measured values on the field scale level with a strong correlation
coefficient of r > 0.8 (r2=0.65).
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
50
Julian day
Field average
soil moisture
SEBS derived
Field average soil moisture
ground measured
247 11.2% 10.4%
250 9.6% 9.7%
318 12.1% 12.5%
350 14.5% 14.0%
68 17.1% 17.0%
118 15.3% 14.1%
122 15.0% 12.2%
170 15.6% 15.1%
179 11.8% 13.3%
182 11.4% 12.3%
191 14.8% 12.7%
211 12.9% 9.6%
223 10.8% 8.6%
Field average Soil moisturey = 0.85x + 0.01
R2 = 0.65
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%
SEBS derived g
rou
nd
measu
red
Figure 7-2 Comparison of ground measured and SEBS derived average soil moisture (2007-2008).
7.2. Comparison of AET
The comparison of the daily values of AET from the RS method, SEBS, was carried out in two
approaches. The first one against the complementary approach and the second one was by comparing
SEBS derived Kc values with FAO standard guide lines. In the complementary approach the
comparison was done for pixels containing the meteorological stations.
7.2.1. Comparison with the Complementary approach
As mentioned in section 2.1.2.2 the complementary (advection–aridity) method gives AET based on
the PET approach. The advantage of this method is that it uses only meteorological parameters to
estimate the AET. Although it has limitations on its theoretical basis (Brutsaert, 2005), it gives
estimates comparable with other energy balance approaches (Dingman, 2002). The basic formula is
given in equation 2-2 and the practical formula for calculating the AET is given in Appendix table 4.
For comparison purposes tables and graphs in Figure 7-4 and Figure 7-5 show the results from the RS
method SEBS, the complementary approach and the reference evapotranspiration calculated based on
the Penman-Monteith Formula. Analyses of the graphs, as expected, indicate that during summer
periods the reference evapotranspiration is high compared with the actual as there is no limit on the
moisture (by definition for the reference grass) and the evaporative energy is high. The actual
evaporation, being limited by the moisture content, reflects relatively lower values during the dry
periods.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
51
Figure 7-3 Soil moisture for selected days in the REMEDHUS network.
In the winter period the limitation is due to the energy available and as shown in Figure 7-4 the
reference evaporation and the AET rates are close from November to early March.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
52
ET for pixel containing Meteo station VA_02
0.0
2.0
4.0
6.0
8.0
day 247 day 250 day 318 day 350 day 68 day 118 day 122 day 170 day 179 day 182 day 191 day 211 day 223
date
evap
otr
an
sp
irati
on
(mm
/day)
reference Eto SEBS AET Complementary AET
a Station VA_02
ET for pixel containing Meteo station Canizal
0.0
2.0
4.0
6.0
8.0
day 247 day 250 day 318 day 350 day 68 day 118 day 122 day 170 day 179 day 182 day 191 day 211 day 223
date
evap
otr
an
sp
irati
on
(mm
/day)
reference Eto SEBS AET Complementary AET
b Station Canizal Figure 7-4 Comparison of actual and reference evapotranspiration.
The comparison of the AET by the RS method SEBS with the complementary approach as shown in
Figure 7-5 illustrates the good correlation between the two methods. The correlation coefficients for
the 4 weather stations in the study area were computed. The maximum and the minimum coefficients,
r>0.95 (r2=0.91) and r> 0.92 (r2=0.86) for two weather stations, namely Granja and Villamor, are
shown in Figure 7-5.
7.2.2. Single crop Coefficient (Kc)
The single crop coefficient is used to calculate crop evapotranspiration. It is a factor in expressing the
difference between the crop reference evapotranspiration of the ideal standard surface and crop
evapotranspiration (Allen and FAO, 1998). It combines the transpiration of the crops and the
evaporation of the soil. It can be used as an indicator of the performance of the SEBS algorithm. With
the available imageries the Kc was computed for 4 stages of wheat development, namely the initial,
the crop development, the mid season and the late season and then compared with the tabulated values
of the FAO guidelines. In the study area the sowing dates for winter wheat vary from October to
November and the harvesting dates vary from June to July.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
53
date reference ETo
SEBS AET
Complementary AET
day 247 5.4 2.8 1.4
day 250 6.1 2.6 0.8
day 318 1.1 1.1 0.0
day 350 0.7 1.0 0.0
day 68 2.2 2.3 1.4
day 118 5.3 4.1 3.3
day 122 3.9 3.0 3.6
day 170 6.1 4.9 4.9
day 179 7.3 4.3 3.9
day 182 6.4 4.1 4.9
AET for pixel containing Meteo station
Villamory = 0.6339x + 1.4887
R2 = 0.8575
0.01.0
2.03.0
4.05.0
6.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0
SEBS AET(mm/day)
co
mp
lem
en
tary
AE
T(m
m/d
ay)
(No meteo data was available after day 182 for this station) .
date reference
Eto SEBS AET
Complementary AET
day 247 5.1 2.6 2.3
day 250 5.7 2.5 1.4
day 318 0.9 1.4 0.0
day 350 0.6 1.0 0.0
day 68 2.1 2.2 1.4
day 118 5.3 4.1 3.5
day 122 4.1 3.1 3.5
day 170 6.1 4.8 5.0
day 179 7.7 4.0 4.0
day 182 6.6 4.9 4.8
day 191 6.9 4.5 3.7
day 211 5.9 3.5 3.9
day 223 6.0 3.6 3.0
AET for pixel containing Meteo station
Granja
y = 0.7216x + 1.2135
R2 = 0.9107
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0SEBS AET (mm/day)
co
mp
lem
en
tary
AE
T(m
m/d
ay)
Figure 7-5 Comparison of AET between SEBS and the complementary approach.
The pixel selected for the comparison (UTM x-306691, y-4557405) was observed during the field
work to have stalks of harvested wheat. The nearest two meteorology stations, Canizal and VA_02,
were selected to calculate 10 day average crop reference evapotranspiration. The 10 day average ETo
was calculated based on the estimations of 5 consecutive days before and after the imagery date.
Table 7-1 shows the results of the comparison. It is clearly seen that the results of the Kc values are in
good agreement with the values given in the FAO guide lines.
Table 7-1 Comparison of Kc values.
stage image date daily ETact
(mm/day)
average
daily ETact
(mm/day
10 day
average ETo
(mm/day)
Kc average
calculated
ETact/ETo
Kc FAO
guide lines
initial day 318(Nov 14,2007) 1.09 1.09 1.57 0.70 0.7 day 350(Dec 16, 2007) 0.99 0.99 0.83 1.19 0.7-1.15 crop
development day 68 (Mar 8,2007) 2.17 2.17 2.18 1.00 0.7-1.15 day 118(Apr 27, 2008) 4.47 mid season day 122(May 1,2008) 3.65
4.06 4.23 1.19 1.15
late season day 170(Jun 18,2008) 4.98 4.98 5.75 0.87 1.15-0.25
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
54
7.3. Effect of input data
SEBS algorithm built for SEBS has surrogate options on the interface to partly overcome the problem
of data scarcity. In this section the performance of SEBS with two approaches for the same input
parameter, the momentum roughness height, was assessed. The roughness height for momentum
transfer (Zom) can be retrieved from wind profiles which according to Su (2002a) is probably the
accurate method. In this research the roughness height was assigned from pre determined values
tagged to each cover class as per the land cover map. The other alternative method given in SEBS as a
substitute is the use of the vegetation index NDVI. In the current SEBS model built for ILWIS the
NDVI value is related with the vegetation height using the following formula.
)(*)(
)(min
minmax
minmaxmin NDVINDVI
NDVINDVI
hhhh −
−
−+= 7-1
Where h is the vegetation height to be related with the roughness height according to equation 5-1,
hmin and hmax are minimum and maximum heights given as 0.0012 and 2.5 m respectively, NDVImin and
NDVImax are given as 0.0 and 0.8 respectively. For comparison and assessment two images were
selected. Figure 7-6a shows actual evaporation for the whole study area and its environs and Figure
7-6b shows the same but only for the forest land cover extracted from the image for day 318 of 2007.
a With all land cover
b Forest land cover only
Figure 7-6 Actual evaporation for day 318 of 2007 (Nov 14).
The same types of figures are repeated for another day, day 122 of 2008(May 1) in Figure 7-7. As
seen from both figures the actual evaporation for the whole site has increased by 25% and 3% for
days 318 and 122 respectively when Zom values were changed from the predetermined tagged values
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
55
to the NDVI related values. For the forested land the increment is about 40% for day 318 and 27% for
day 122.
a With all land cover.
b Forest land cover only.
Figure 7-7 Actual evaporation for day 122 of 2008 (May 1).
It is clear from the above comparison that SEBS gives higher actual evaporation estimations when
NDVI is used as a substitute for the land cover map. The variation is dependent on the type of land
cover. As a consequence, surface roughness remains one of the most sensitive parameters in the SEBS
approach, although the importance might diffuse for AET calculations on cumulative days. If a
relation is developed for the two cases it could be still advantageous to use NDVI for remote areas
where land use maps are not easily available.
7.4. Limitations
In this research the SEBS model has been explored to retrieve soil moisture using the relation between
evapotranspiration and soil moisture. The available energy is partitioned into the sensible heat flux
and the latent heat flux. The instantaneous sensible heat flux in SEBS is limited between the dry and
the wet limits of the sensible heat flux as discussed in section 5.3.As observed in forest and orchard
covered areas which have high values of displacement heights and roughness heights for heat transfer,
the aerodynamic resistance becomes low. This in combination with higher difference in the surface air
temperature leads to high values of the instantaneous heat flux only to be limited by the dry sensible
heat limit, Hdry. This in turn leads to zero relative evaporation values according to equation 5-38 and
the daily evaporation and the evaporative fraction values will become zero. As a result the soil
moisture will also be zero.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
56
For similar weather conditions in the same region however, the soil below forests is expected to have
some moisture (hence evaporation) and there should also be forest transpiration in contrary to what is
seen for some pixels in the dry season images. This event may have happened due to a combination of
low radiation with high albedo or higher wind speed. The mean daily evapotranspiration varies
considerably as the wind speed changes. The sensitivity for the whole study area and for forests only
is shown in Figure 7-8. The day selected was day 247, 2007 when the instantaneous wind speed
measurement at the satellite overpass time varied between 1.7 m/s to 6.9m/s among the four weather
stations considered in the averaging of the instantaneous wind speed. The isolated zero values of soil
moisture for the forest areas are clearly seen in Figure 7-3. This clearly shows the importance of wind
speed map in such undertaking.
sensitivity of the whole study site
0
0.5
1
1.5
2
2.5
1.2 2.3 3.5 4.6 5.8 6.9 8.1
wind speed(m/s)
mean
daily
evap
ora
tio
n(m
m/d
ay
)
sensitivity of forest covered land
0
0.2
0.4
0.6
0.8
1.2 2.3 3.5 4.6 5.8 6.9 8.1
wind speed(m/s)
mean
daily
eva
po
rati
on
(mm
/day)
Figure 7-8 Sensitivity of evapotanspiration to wind speed.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
57
8. Conclusions and recommendations
8.1. Conclusions
Different RS techniques have been used to estimate soil moisture, evapotranspiration and other fluxes
in the past few decades. In this study the Surface energy balance system (SEBS) was used to estimate
soil moisture and AET for the Guareña catchment in Spain. To this end satellite images from the
sensor MODIS on board the satellite TERRA were collected for days from September 2007 to August
2008. Initially 24 cloud free images were downloaded and later reduced to 13 because of high sensor
and sun zenith angles which cause less accuracy in the atmospheric correction of the images. For the
atmospheric correction of the images the SMAC algorithm was used. Hourly ground measurements of
soil moisture from 23 stations in the study area including meteorological data from 5 weather stations
and data on soil properties were also collected.
In order to compute the soil moisture estimations from the RS method the relation between the
relative evaporation and the relative soil moisture was used. The proportional relation of the relative
soil moisture and the relative evapotranspiration has been in use for a long time to estimate AET from
potential evaporation. To compare pixel level estimates of the RS method with point scale ground
measurements, down scaling of the pixel level measurement was performed using the temporal
stability approach. The estimates from the RS method for the pixels containing the representative soil
moisture stations were down scaled and compared with the point measurement values.
The study reveals that there is a good correlation (r2=0.65) between the average field scale soil
moisture estimates of the RS method SEBS and the ground measurements. The computed average
field scale soil moisture from the ground measurements is the arithmetic average of the 22 soil
moisture stations in the study area while the average from the RS is the mean of all pixel level soil
moisture estimates. There is no definite correlation however between the RS estimates and the
measured soil moisture on the point scale level after the pixel wise estimate was downscaled to the
point scale ground measurements (0<r2<0.2). For one of the stations, station M9, the relation was
improved when only dry season estimates were considered. The AET estimates were compared with
the complementary (advection–aridity) method. The results indicate good correlation between the two
methods. For all the days compared the coefficient of determination, r2, is greater than 0.86. The
single crop coefficient was also computed based on the estimates of the evapotranspiration from the
RS and the values are found to be in good agreement with the values in the FAO guide lines.
Based on the validation of the results of this study the SEBS algorithm gives reasonably good
estimates of catchment level top soil moisture. The validation however also indicates that it was not
possible to retrieve point scale soil moisture from SEBS. The strong correlation of the estimates of
AET with the estimates of the complementary approach indicates that estimations of
evapotranspiration by SEBS are reliable. The use of vegetation index, the NDVI, as a surrogate for
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
58
land cover map to estimate the momentum roughness height has revealed an increase in the estimation
of evapotranspiration by 3% to 40 % depending on the land cover type.
8.2. Recommendations
The down stream side of the Guareña catchment is well instrumented for soil moisture measurements.
The upstream part of the catchment however is not instrumented as the initial project was meant for
other studies concentrating in the REMEDHUS network which mostly covers the down stream side
only. To integrate hydrological models in validation of remote sensing methods and even to validate
hydrological models against ground measurements, instrumentation schemes should envisage all the
sub basins in the catchment.
The validation of actual evapotranspiration from SEBS and other RS methods is important. This
catchment could be used for this purpose by allowing the direct estimation of evapotranspiration. This
could be established by deep moisture measurement combining moisture with water potential to
determine the zero flux plane.
It is clear that the hydrological modeling from the catchment needs further testing. There is clear
evidence that the natural runoff is being altered in some way (irrigation probably the main cause)
leading to modeling failure without accounting for these extractions. The dimension of the
Guareñacatchment is not simple to control with the limited resources of this thesis. As such the
operation of the two discharge stations now out of service would be of huge interest to improve
modeling. More over for proper determination of the water balance of the catchment the water
released into the river from the San Jose Canal should also be monitored quantitatively.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
59
References
Allen, R.G. and FAO, 1998. Crop evapotranspiration : guidelines for computing crop water requirements. FAO irrigation and drainage paper;56. FAO, Rome, 300 pp.
Antonio, C., Klaus, S., Wolfgang, W. and Martínez-Fernández, J., 2005. Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero Basin, Spain. Hydrological Processes, 19(8): 1549-1566.
Barbieri, R. et al., 1997. Draft of the MODIS level 1B algorithm Theoretical basis Document version 2.0[ATBMOD-01] MODIS MCST
Bastiaanssen, W.G.M. and Iwmi, 1998. Remote sensing in water resources management : the state of the art. International Water Management Institute (IWMI), Colombo, 118 pp.
Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A. and Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213: 198-212.
Booij, M.J., Deckers, D.L.E.H., Rientjes, T.H.M. and Krol, M.S., 2007. Regionalization for uncertainty reduction in flows in ungauged basins. In: Quantification and reduction of predictive uncertainty for sustainable water resources management : proceedings of symposium HS2004 at IUGG2007, Perugia, 7-13 July 2007. / ed. by E. Boegh ...[et.al] Wallingford : IAHS, 2007. ISBN 978-1-90150278-09-1(IAHS Publication ; 313) pp. 329-337.
Brutsaert, W., 1982. Evaporation into the atmosphere : theory, history, and applications. Copy. Reidel Publishing, Dordrecht etc., 299 pp.
Brutsaert, W., 2005. Hydrology : an introduction. Cambridge University Press, Cambridge, 605 pp. Chen, Z. et al., 2008. Monitoring and Management of Agriculture with Remote Sensing. In: S. Liang
(Editor), Advances in Land Remote Sensing, pp. 397-421. Cosh, M.H., Jackson, T.J., Bindlish, R. and Prueger, J.H., 2004. Watershed scale temporal and spatial
stability of soil moisture and its role in validating satellite estimates. Remote Sensing of Environment, 92(4): 427-435.
De Lannoy, G.J.M., Houser, P.R., Verhoest, N.E.C., Pauwels, V.R.N. and Gish, T.J., 2007. Upscaling of point soil moisture measurements to field averages at the OPE3 test site. Journal of Hydrology, 343(1-2): 1-11.
Dingman, S.L., 2002. Physical hydrology. Prentice Hall, Upper Saddle River, 646 pp. Grayson, R.B. and Western, A.W., 1998. Towards areal estimation of soil water content from point
measurements: time and space stability of mean response. Journal of Hydrology, 207(1-2): 68-82.
Immerzeel, W.W., Droogers, P. and Gieske, A.S.M., 2006. Remote sensing and evapotranspiration mapping : state of the art, FutureWater, Wageningen.
Iqbal, M., 1983. introduction to solar radiation. COPY. Academic Press, Toronto etc., 390 pp. Li, Z.-L., Jia, L., Su, Z., Wan, Z. and Zhang, R., 2004. A new approach for retrieving precipitable
water from ATSR2 split-window channel data over land area. International Journal of Remote Sensing, 24(24): 5095 - 5117.
Liang, S., 2001. Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2): 213-238.
Lichun, W., 2007. SEBS model. 52* North Initiative for Geospatial open source software GmbH 2007,Germany.
Lichun, W., 2008. Processing MODIS level_1B data for SEBS in ILWIS instructions. ITC Martinez-Fernandez, J. and Ceballos, A., 2003. Temporal stability of soil moisture in a large-field
experiment in Spain. Soil Science Society of America Journal, 67(6): 1647-1656. Norman, J.M. et al., 2003. Remote sensing of surface energy fluxes at 10(1)-m pixel resolutions.
Water Resources Research, 39(8). Rahman, H. and Dedieu, G., 1994. Smac - a Simplified Method for the Atmospheric Correction of
Satellite Measurements in the Solar Spectrum. International Journal of Remote Sensing, 15(1): 123-143.
Rientjes, T., 2007. Modelling in Hydrology (part two). ITC, 233 pp.
Validation of RS Approaches to Model Surface Characteristics in Hydrology:
A Case Study In Guareña Aquifer, Salamanca, Spain
60
Roerink, G.J., Su, Z. and Menenti, M., 2000. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(2): 147-157.
Royer, A., O'Neil, N., Davies, A. and Hubert, L., 1988. Comparison of radiative transfer models used to determine atmospheric optical parameters from space. In: L.S. Rothman (Editor), Modelling of the Atmosphere. The International Society of Optical Engineering, Washington D.C. .
Schultz, G.A., editor and Engman, E.T., editor, 2000. Remote sensing in hydrology and water management. Springer, Berlin etc., 483 pp.
SMHI, 2006. Integrated Hydrological Modelling System. Sobrino, J.A. and Raissouni, N., 2000. Toward remote sensing methods for land cover dynamic
monitoring: application to Morocco. International Journal of Remote Sensing, 21(2): 353 - 366.
Su, Z., 2000. Remote sensing of land use and vegetation for mesoscale hydrological studies. International Journal of Remote Sensing, 21(2): 213-233.
Su, Z., 2002a. An introduction to the surface energy balance system. ITC. Su, Z., 2002b. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes.
Hydrology and Earth System Sciences, 6(1): 85-99. Su, Z. et al., 2003. Assessing relative soil moisture with remote sensing data: theory, experimental
validation, and application to drought monitoring over the North China Plain. Physics and Chemistry of the Earth, Parts A/B/C, 28(1-3): 89-101.
Toller, G., Isaacman, A. and Kuyper, J., 2006. MODIS Level 1B Product User's Guide(for level 1B Version 5.0.6(Terra) and Version 5.0.7(Aqua) ). MODIS MSCT.
Vachaud, G., Passerat De Silans, A., Balabanis, P. and Vauclin, M., 1985. Temporal Stability of Spatially Measured Soil Water Probability Density Function. Soil Sci Soc Am J, 49(4): 822-828.
Van der Lee, J. and Gehrels, J.C., 1990. Modelling Aquifer Recharge: Introduction to the Lumped Parameter Model EARTH,Free University of Amsterdam,The Netherlands.
Wagner, W., Lemoine, G. and Rott, H., 1999a. A Method for Estimating Soil Moisture from ERS
Scatterometer and Soil Data. Remote Sensing of Environment, 70(2): 191-207. Wagner, W., Noll, J., Borgeaud, M. and Rott, H., 1999b. Monitoring soil moisture over the Canadian
Prairies with the ERS scatterometer. Ieee Transactions on Geoscience and Remote Sensing, 37(1): 206-216.
Wagner, W. et al., 2008. Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR). Sensors, 8(2): 1174-1197.
Wang, L., Qu, J.J., Zhang, S., Hao, X. and Dasgupta, S., 2007. Soil moisture estimation using MODIS and ground measurements in eastern China. International Journal of Remote Sensing, 28(6): 1413-1418.
Wen, J., Su, Z.B. and Ma, Y.M., 2003. Determination of land surface temperature and soil moisture from Tropical Rainfall Measuring Mission/Microwave Imager remote sensing data. Journal of Geophysical Research-Atmospheres, 108(D2).
Xiong, J. et al., 2005. MODIS Level 1B Algorithm Theoretical Basis Document,version 3, pp. 43.
top related