validation of rs approaches to model surface ... · the study reveals that there is a satisfactory...

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



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?



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



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.



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.



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



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),



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



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



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.



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.



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).



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



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



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.



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.



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.



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.



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



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_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



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.



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.



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



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.



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:



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 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


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.



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



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



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%


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



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





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.


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


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


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.



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



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.



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



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.



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



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.



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.



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:



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.



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



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:



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



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.



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



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.



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



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

QQ

QQ

R

1

2)()(

1

2)()(

2

)(

)(

1 6-1



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

QQ

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.



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.



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.



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).



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.



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.



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.



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



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



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.



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.



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



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.



59

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