ESTIMATION OF SPATIAL AND TEMPORAL DISTRIBUTION OF ...Thesis submitted to the International Institute for Geo-information Science and Earth Observation in ... surface energy balance

ESTIMATION OF SPATIAL AND TEMPORAL DISTRIBUTION OF EVAPOTRANSPIRATION BY

SATELLITE REMOTE SENSING A case study in Hupselse Beek, The Netherlands

Kitsiri Weligepolage March, 2005

ESTIMATION OF SPATIAL AND TEMPORAL DISTRIBUTION OF EVAPOTRANSPIRATION BY

SATELLITE REMOTE SENSING A case study in Hupselse Beek, The Netherlands

by

Kitsiri Weligepolage Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation in Water Resources and Environmental Management programme specialisation: Watershed Management, Conservation & River Basin Planning Thesis Assessment Board Chairman Prof.dr.ir. Z. Su Head- WRS Department, ITC, Enschede External Examiner Ir. E.J. Moors, Alterra Wageningen Primary Supervisor Dr. A.S.M. Gieske WRS Department, ITC, Enschede Second Supervisor Prof.dr. R. Bos WRS Department, ITC, Enschede.

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme 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

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Abstract

This study was carried out in Hupselse Beek catchment in the Netherlands, comparing the remote sensing methods with conventional ground based methods to estimate spatially distributed actual evapotranspiration. During the primary data collection, seven TERRA-ASTER images acquired during February 2002 and April 2004 were identified covering the study area. Also the hourly meteorological data measured at KNMI Hupselse station was available from January 2000 to October 2004. During the field campaign meteorological parameters and surface fluxes were measured intensively by installing a temporary weather station and scintillometer equipment in the site. Also the ground truth information of catchment land use was recorded through a GPS survey. During the preliminary data preparation stage the pre-processing of ASTER images was carried out using ILWIS software incorporating the outputs of SMAC for ILWIS and MODTRAN- 4 software for atmospheric corrections. The broad band albedo, surface temperature and broad band emissivity were estimated using pre-processed satellite data. Having estimated the basic input parameters, the two surface energy balance models SEBAL & S-SEBI were applied together with ancillary data to estimate the daily actual evapotranspiration on 7 days within February 2002 and April 2004. Also the daily reference evapotranspiration with full temporal coverage for the same period mentioned above and the actual evaporation on 14 days using the combined scintillation measurements at Hupselse and Haarweg stations were estimated. Preliminary results indicated that the NDVI & broad band albedo values estimated with atmospherically corrected reflectance are far from the values estimated from top of the atmosphere reflectance. The daily estimates of reference evapotranspiration from Penman-Monteith method and Makkink equation indicated that they are closely correlated. The Makkink results appeared to be more consistent where as the results of Penman method indicated some deviations especially under the extreme conditions. The actual ET estimates from SEBAL & S-SEBI are also in close agreement with each other both spatially as well as temporally. The scintillation method also exhibited comparable results with actual ET estimated by both the energy balance models. The spatial variation revealed that the highest rate of evapotranspiration occurs in Woodlands. The Maize area has shown the highest variation of daily ET both spatially and temporally. The single crop coefficients (Kc) of maize calculated based on the spatially averaged actual ET of grass on different dates, are found to be reasonably matching with decade mean values of Kc for Maize given in the literature for corresponding growth stages.

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Acknowledgements

I would like to express my sincere gratitude to the Government of Netherlands through the Netherlands Fellowship Programme (NFP) for granting me this opportunity to study for a Master of Science degree. I am grateful to my employer, Irrigation Department of Sri Lanka for providing me this opportunity to pursue higher studies. I am greatly indebted to my first supervisor Dr. Ambro S. M. Gieske for his excellent guidance and encouragements throughout my study period and especially during the research work. I appreciate his constructive criticism and valuable advises which helped me to take this research in the right direction. I would like to thank my second supervisor Prof. Rien Bos for guiding me with valuable advices and comments to improve my research work. Many thanks go to all the dedicated staff members of WREM department at ITC for imparting this valuable knowledge during past 18 months and especially to the Programme Director Ir. Arno van Lieshout for his excellent assistance and cooperation. I would like to appreciate Mr. Jacque Koel from Waganingen University for providing the meteorological data required for the study and Mr.& Mrs. Rudi Smeerk of Hupselse Beek for their hospitality extended to us during the field work at Hupselse. I am grateful to all my group mates in WREM 2003 especially Parth, Joseph and Marcela for their continuous support and friendship bestowed on me during last one and half years. Many thanks to my friends with whom I shared my days at Enschede for eighteen months especially to Saman, Divithura, Perera, Saliya, Aroos, Chandana, Manjula, Vajira, Lal and Tenne. I wish to express my gratitude to my family members for their continuous moral support and encouragement during my endeavour to complete this work, especially to my wife Jayani and my two kids Dulana & Dinoki for their inspiration love and patience during the long period of our separation.

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Table of contents

1. General introduction............................................................................................................1

1.1. Rationale .................................................................................................................................1 1.2. Problem statement...................................................................................................................2 1.3. Research objectives ................................................................................................................2

1.3.1. General objective................................................................................................................2 1.3.2. Specific objectives..............................................................................................................2

1.4. Research questions..................................................................................................................3 1.5. Research approach / Methodology .........................................................................................3 1.6. Logical sequence of the methodology ....................................................................................4 1.7. Outline of the thesis ................................................................................................................5

2. Literature review & primary data collection. .....................................................................7 2.1. Physical background of land surface evapotranspiration .......................................................7

2.1.1. Soil Evaporation .................................................................................................................7 2.1.2. Transpiration ......................................................................................................................8 2.1.3. Evapotranspiration Concepts..............................................................................................8 2.1.4. Estimation of potential / reference evapotranspiration ......................................................9 2.1.5. Estimation of actual evapotranspiration.............................................................................9

2.2. Primary data collection .........................................................................................................11 2.2.1. Selection of a study area:..................................................................................................11 2.2.2. Brief description of historical studies in the area:............................................................12 2.2.3. Availability of ground data...............................................................................................13 2.2.4. Availability of Satellite data:............................................................................................14 2.2.5. Atmospheric correction data: ...........................................................................................15

3. Description of the study area and secondary data collection.............................................19 3.1. Description of the study area ................................................................................................19

3.1.1. Location............................................................................................................................19 3.1.2. Climate .............................................................................................................................20 3.1.3. Precipitation......................................................................................................................21 3.1.4. Land Use...........................................................................................................................21

3.2. Secondary data collection during field work........................................................................22 3.2.1. Weather Station for meteorological data collection.........................................................22 3.2.2. Scintillometer Experiment................................................................................................29 3.2.3. Ground truth collection by a GPS survey.........................................................................33

4. Pre-processing of ASTER images .....................................................................................35 4.1. Introduction to ASTER imageries ........................................................................................35 4.2. Importing of ASTER level 1A/1B Data using ILWIS..........................................................36

4.2.1. ASTER Radiometric calibration ......................................................................................36 4.2.2. Geometric correction of ASTER images..........................................................................37 4.2.3. Conversion of radiance to reflectance at top of the atmosphere ......................................38

4.3. Atmospheric correction for satellite imageries.....................................................................39 4.3.1. Atmospheric correction methods......................................................................................39

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4.3.2. SMAC: Simplified Method for Atmospheric Correction.................................................40 4.3.3. SMAC for ILWIS .............................................................................................................40 4.3.4. Correcting the images of the study area using SMAC for ILWIS....................................42 4.3.5. Application of MODTRAN 4 for thermal bands .............................................................44 4.3.6. Temperature Emissivity Separation .................................................................................47

5. Remote sensing surface energy balance models................................................................49 5.1. Introduction...........................................................................................................................49 5.2. Surface energy balance equation ..........................................................................................49 5.3. Remote sensing techniques and energy balance models ......................................................50

5.3.1. Evolution of remote sensing models ................................................................................50 5.3.2. Primary data for the energy balance models ....................................................................50 5.3.3. Expansion in remotely sensed data ..................................................................................50

5.4. The Surface Energy Balance Algorithm for Land (SEBAL)................................................51 5.4.1. Estimation of Surface parameters.....................................................................................52 5.4.2. Bio Physical parameter estimation ...................................................................................53 5.4.3. Estimation of energy balance components .......................................................................55

5.5. Simplified Surface Energy Balance Index (S-SEBI) ............................................................60 5.5.1. S-SEBI algorithm..............................................................................................................60

6. Analysis of results and discussion.....................................................................................63 6.1. Comparison of results from ground based methods. ............................................................63

6.1.1. Comparison of actual evapotranspiration (AET) by scintillation method .......................66 6.2. Comparison of temporal variation of RS based Actual ET (AET)......................................71

6.2.1. Comparison of catchment averaged AET values of SEBAL and S- SEBI.......................71 6.2.2. Comparison of AET from Energy Balance Models and Scintillation method.................72 6.2.3. Comparison of daily ET estimated from 5 different methods..........................................73

6.3. Comparison of spatial distribution of actual evapotranspiration..........................................74 6.3.1. Spatial Variation over the entire catchment .....................................................................74 6.3.2. Spatial variation of actual evapotranspiration in different Land Use classes ..................76 6.3.3. Comparison of single crop coefficient (Kc) for Maize.....................................................78

6.4. Discussion.............................................................................................................................80 6.4.1. Summary of the results from ground based methods .......................................................80 6.4.2. Summary of the results from remote sensing methods.....................................................81 6.4.3. Spatial distribution of AET over the catchment...............................................................81 6.4.4. Spatial distribution within the Land Use classes..............................................................81 6.4.5. Crop coefficients based on AET of grass.........................................................................82

7. Conclusions and recommendations. ..................................................................................83 7.1. Conclusions...........................................................................................................................83 7.2. Limitations ............................................................................................................................84 7.3. Recommendations.................................................................................................................85

Appendices ...............................................................................................................................87 Appendix A- Data collected at Hupselse from 29-09-2004 to 06-10-2004 .......................................87 Appendix B- Wet & Dry pixel information with SEBAL constants................................................102 Appendix C- S-SEBI constants obtained from reflectance- temperature plots................................103 Appendix D- Calculation of reference evapotranspiration (ET0) ....................................................104

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Appendix E- ILWIS scripts for SEBAL & S-SEBI algorithms. ......................................................105 Appendix F- Comparison of actual ET (mmd-1) - SEBAL vs. S-SEBI......................................108 Appendix G- Temporal variation of daily actual ET in Grass area .................................................110 Appendix H- Temporal variation of daily actual ET in Maize area ................................................111 Appendix I - Temporal variation of daily actual ET in Wooded area .............................................112

References...............................................................................................................................113

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List of figures

Figure 1-1 Three major phases and important steps in the methodology ...............................................4 Figure 1-2 Two different approaches used in the methodology with the main steps highlighted ..........5 Figure 2-1 Location of the study area ...................................................................................................11 Figure 2-2 Existing Hydro-meteorological network .............................................................................12 Figure 2-3 Location of the Haarweg Agro-Meteo station in Wageningen ...........................................13 Figure 2-4 Temporal coverage of ASTER L1B images........................................................................14 Figure 2-5 Chart of Aerosol Optical Thickness ....................................................................................15 Figure 2-6 Estimation of AOT at 0.550 micro meters ..........................................................................16 Figure 2-7 TOMS site for Ozone observations .....................................................................................17 Figure 3-1 Location of the Hupselse Beek catchment ..........................................................................19 Figure 3-2 Mean Monthly Solar radiation ............................................................................................20 Figure 3-3 Mean Monthly Temperature and Relative humidity ...........................................................20 Figure 3-4 Mean Monthly precipitation................................................................................................21 Figure 3-5 Topography of Hupselse Beek Catchment ..........................................................................21 Figure 3-6 Weather station at Hupselse ................................................................................................22 Figure 3-7 Technical diagram of the tower...........................................................................................23 Figure 3-8 Dimensions of the CNR1-Net Radiometer ..........................................................................24 Figure 3-9 Switching Anemometer .......................................................................................................25 Figure 3-10 Potentiometer Windvane ...................................................................................................25 Figure 3-11 Sensor equipments for temperature & humidity measurements........................................25 Figure 3-12 Soil temperature profile.....................................................................................................26 Figure 3-13 Soil Temperature Probes ...................................................................................................26 Figure 3-14 Soil Heat Flux Plate...........................................................................................................26 Figure 3-15 The charts of Short wave radiation , Long wave radiation and Air Temperature.............27 Figure 3-16 The Charts of Relative Humidity, Wind Speed and Soil Temperature .............................28 Figure 3-17 Location of the instrument ................................................................................................29 Figure 3-18 Minimum installation height as a function of path length.................................................30 Figure 3-19 The behaviour of the signal strength when transmitter/receiver is turned horizontally....31 Figure 3-20 Two masts standing in the field with transmitter and the receiver fixed on top of each ..32 Figure 3-21 Way Points collected by GPS survey ................................................................................33 Figure 3-22 Land use classes in Hupselse Beek ...................................................................................33 Figure 4-1 Form of input parameters for SMAC interface ...................................................................41 Figure 4-2 Flow chart of image pre-processing to derive surface reflectance maps.............................42 Figure 4-3 Comparison of Band 1 reflectance before and after atmospheric corrections ....................43 Figure 4-4 Change in NDVI with atmospheric corrections ..................................................................43 Figure 4-5 Change in broad band albedo with atmospheric corrections...............................................44 Figure 4-6 MODTRAN tape 5 showing the basic input parameters.....................................................45 Figure 4-7 Basic steps for estimating atmospheric radiances and transmittance in MODTRAN 4 .....46 Figure 5-1 Surface Energy Balance Components .................................................................................49 Figure 5-2 Conceptual scheme of SEBAL showing principal components..........................................51 Figure 5-3 Calculation process of different vegetation indices ............................................................53 Figure 5-4 Feature space plot of NIR and RED with showing the soil line..........................................53

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Figure 5-5 Solving for constants using wet and dry pixels ...................................................................57 Figure 5-6 Iterative process to calculate sensible heat flux ..................................................................58 Figure 5-7 Schematic representation of temperature – reflectance relationship...................................61 Figure 5-8 Feature space plot of reflectance and temperature with Hmax and LEmax lines ....................61 Figure 6-1 Time series of daily reference evapotranspiration (2002-2004) .........................................64 Figure 6-2 Comparison of reference ET for June to August.................................................................64 Figure 6-3 Comparison of reference ET for November to January ......................................................65 Figure 6-4 Comparison of reference ET for the entire period ..............................................................65 Figure 6-5 Comparison of Sensible Heat Flux values at Hupselse and Haarweg.................................66 Figure 6-6 Estimation of Actual ET at Hupselse from Scintillation method........................................67 Figure 6-7 Daily ET calculated on Image dates from ground based methods (see Table 6-3) .............68 Figure 6-8 Daily ET calculated for field work period by ground based methods (see Table 6-3) .......69 Figure 6-9 Actual ET from scintillation against reference ET from Makkink .....................................70 Figure 6-10 Actual ET from scintillation against the reference ET from Penman-Monteith ...............70 Figure 6-11 Comparison of Actual ET from S-SEBI against SEBAL (dates see Table 6-4) ..............71 Figure 6-12 Comparison of actual ET from SEBAL against the scintillation (dates see Table 6-4)....72 Figure 6-13 Comparison of actual ET from S-SEBI against the scintillation (dates see Table 6-4) ....72 Figure 6-14 Time series of daily evapotranspiration at Hupselse (2002-2004)....................................73 Figure 6-15 Time series of decadal mean of daily evapotranspiration at Hupselse (2002-2004).........73 Figure 6-16 Spatial variation of daily AET on 08-05-2003 ..................................................................74 Figure 6-17 Spatial variation of actual ET from SEBAL .....................................................................75 Figure 6-18 Spatial variation of actual ET from S-SEBI ......................................................................75 Figure 6-19 Spatial variation of SEBAL based actual ET in different Land use classes .....................76 Figure 6-20 Spatial variation of SEBAL based ET in different land use classes .................................77 Figure 6-21 Spatial variation of S-SEBI based ET in different land use classes..................................77 Figure 6-22 Comparison of Crop coefficient for Maize in different growing stages ...........................78 Figure 6-23 Comparison of Kc for Maize from May 2003 to October 2003........................................79

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List of tables

Table 2-1 Meteorological data availability at Hupselse weather station ..............................................13 Table 2-2 Meteorological data being collected at Haarweg .................................................................14 Table 2-3 Image availability for the study area.....................................................................................15 Table 2-4 AOT value for different wavelengths ..................................................................................16 Table 2-5 Atmospheric correction data.................................................................................................17 Table 3-1 Different parameters measured and the type of instrument used .........................................23 Table 3-2 Technical data of the instrument ..........................................................................................29 Table 3-3 Typical values of the measured variables.............................................................................32 Table 4-1 Spectral characteristics of ASTER ........................................................................................35 Table 4-2 Unit conversion coefficients for calibration ..........................................................................37 Table 4-3 Values of Earth-Sun distance and solar zenith angle ............................................................38 Table 4-4 Exo-Atmospheric irradiance values of ASTER bands ..........................................................38 Table 4-5 Properties of input parameters for SMAC.............................................................................41 Table 4-6 Summary of the MODTRAN output for average values of Lup, Ldn and τ..........................47 Table 5-1 Coefficient for converting narrowband to broadband emissivity ..........................................52 Table 6-1 Different estimation methods and their temporal and spatial coverage ................................63 Table 6-2 Acquisition of different energy flux components..................................................................67 Table 6-3 Daily evapotranspiration from different ground based methods ...........................................68 Table 6-4 Daily actual evapotranspiration from different methods .......................................................71 Table 6-5 Statistics of spatial variation of actual ET over the catchment .............................................75 Table 6-6 Statistics of daily ET distribution in different land use classes.............................................76 Table 6-7 Numerical values of spatial variation of daily ET in different land use classes ...................77 Table 6-8 Statistics of distribution of Kc for Maize at two different stages ..........................................78 Table 6-9 Average crop coefficients calculated based on actual ET of Grass......................................79 Table 6-10 Single crop coefficient of Maize crop .................................................................................79

ESTIMATION OF SPATIAL AND TEMPORAL DISTRIBUTION OF EVAPOTRANSPIRATION BY SATELLITE REMOTE SENSING

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1. General introduction

1.1. Rationale

It is widely accepted that, water resources management strategies should be formulated at river basin scale. To implement such strategies effectively, it is necessary to understand how hydrological processes operate at this scale. Hydrological processes inside river basins are complex due to the combined nature of the natural processes and man made features. Also properties of media forming hydrological systems display a degree of heterogeneity at various scales (Bronstert and Bardossy 2003). Carrying out detailed hydrological studies in river basins is of paramount importance for proper understanding of this complex behaviour of the different component of the hydrological cycle. Water balance study or the quantitative analysis of the components of the hydrological cycle, within a river basin is essential for systematic planning and management of the basin water resource. Out of all the components of the hydrological cycle, land surface evapotranspiration estimates are crucial for water balance studies. Also this is perhaps the most difficult component to estimate because of complex interactions between this and the components of the land- plant-atmosphere system. Evapotranspiration is the combined process of water evaporating from open water surface or soil and the transpiring from plant leaves. Hence the term evapotranspiration is widely used to mean the evaporation from mixture of vegetation and soil, such as from agricultural fields. Land surface evapotranspiration is governed by the conditions of the lower part of the atmosphere, the presence and the properties of the vegetation layer and the sub surface soil moisture conditions (Gieske 2003). The condition of the lower part of the atmosphere depends on the supply of heat energy and the vapour pressure gradient, which, in turn, depend on meteorological factors such as temperature, wind speed, atmospheric pressure, and solar radiation. These factors also depend on other factors, such as geographical location, season, time of day, etc. All these factors make the evapotranspiration an entity which is highly variable in space and also with time. Traditionally actual evapotranspiration has been computed as a residual in water balance equations, from estimates of potential evapotranspiration or, indirectly from field measurements. Field methods and meteorological station based methods are described by FAO-24 and FAO-56 (Allen et al. 1998). However some of the recently developed methods use eddy correlation techniques, scintillometers and hydrological models to compute the actual evapotranspiration especially for experimental studies (Kite and Droogers 2000). Satellite remote sensing as a branch of earth science started in early sixties with the NOAA/AVHRR, METEOSAT and LANDSAT programs. During the past 20 years, the capacity of satellite remote sensing to monitor land surface processes has been developed to a great extent through a number of such programs. The recently launched TERRA satellite with on board ASTER and MODIS sensors marks a new phase in monitoring the processes occurring on or near the earth surface and lower atmosphere. Especially the ASTER mission is to improve understanding of the local and regional scale processes occurring on or near the earth’s surface and lower atmosphere. With the increasing ability to estimate the land surface parameters such as surface reflection and land surface temperature


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using remotely sensed data, scientists have developed a variety of methods to compute the actual evapotranspiration particularly based on energy balance methods. SEBAL (Bastiaanssen et al. 1998) S-SEBI (Roerink et al. 2000) and SEBS (Jia et al. 2003) are few examples for the energy balance algorithms developed in the recent past for estimating actual evapotranspiration based on remotely sensed data.

1.2. Problem statement

Whatever the method applied, essentially the spatial scale of the computed value depends on the method used to estimate the evapotranspiration. The climate-station based techniques produce point estimates where as remote sensing methods produce actual areal values. Meanwhile the estimates from the scintillometer technique are at an intermediate scale. In a basin scale water balance study, estimates of spatially distributed actual evapotranspiration are crucial. More often this is done by extrapolating the data measured at discrete points to the basin scale. However ground measurements of land surface fluxes are representative of a relatively small area. Land surface fluxes at the basin scale, therefore are difficult to deduce from a limited number of in situ field stations even if equipped with advanced measurement devices. Meanwhile recently developed satellite remote sensing methods based on energy balance techniques are capable of estimating surface fluxes from one pixel up to the regional scale with a certain degree of accuracy. Integration of both the ground based methods and the satellite based methods would provide spatially distributed estimates of evapotranspiration that would greatly improve the resolution and certainty of river-basin water balances. Alternatively, the scintillation method can estimate areally representative surface fluxes of sensible heat at scales comparable to remote sensing methods. Hence the results from the scintillation method can be used to cross validate the estimates from remote sensing methods.

1.3. Research objectives

1.3.1. General objective

The main objective of the study is to estimate the spatially distributed actual evapotranspiration using remotely sensed data and to compare the results as determined with meteorological data and ground based point flux measurements. In order to achieve the general objective the following specific objectives will be addressed.

1.3.2. Specific objectives

• Estimation of reference evapotranspiration with point based data from meteorological stations using Penman-Monteith & modified Makkink equations.

• Evaluate the practical application of scintillation method to estimate the actual evapotranspiration over grass.

• Update the land use map of the study area with recently acquired information. • Application of two of the energy balance models SEBAL and S-SEBI to estimate the

spatially distributed actual evapotranspiration using ASTER images • Comparison of results from different methods to analyse the spatial & temporal distribution of

actual evapotranspiration in the study area.


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1.4. Research questions

• What meteorological parameters are critical in estimating the reference evapotranspiration and which of the two methods will derive comparatively better results in the study area?

• What are the possible atmospheric correction methods applicable for ASTER imageries in visible and short wave infra red bands?

• Are the spatially averaged actual evapotranspiration results of remote sensing methods comparable with results of ground based methods?

• Which of the two algorithms used in the remote sensing method derive better estimates when compared with the results from the scintillation method?

• Can the spatial and temporal variations of the catchment land use be interpreted from the actual evapotranspiration results from remote sensing methods?

1.5. Research approach / Methodology

The methodology consists of 3 different stages. During the initial stage primary data was collected. This included searching and downloading satellite data from the archives, collecting standard meteorological data, surface flux measurements and also the other parameters needed for the atmospheric correction such as AOT, ozone quantity, water vapour thickness etc. Also a comprehensive literature survey was carried out during this stage to understand the different methods available for calculation of evapotranspiration, surface energy balance techniques and use of remote sensing data for estimation of evapotranspiration. In the second stage a field campaign was carried out in the selected study area for secondary data collection. During this stage extensive measurements of meteorological data and surface flux measurements were made by installing a temporary weather station and a scintillometer apparatus in the site. Also ground control points were recorded covering the study area using a hand held GPS for land use classification purposes. In the third and the final stage all the data processing, analyzing and integration was done in order to estimate the ground based and the satellite based actual evapotranspiration. Firstly the meteorological data obtained during the primary data collection were processed to compute the reference evapotranspiration using conventional methods. Secondly the satellite images were pre-processed and corrected for atmospheric effects. The corrected images were further processed in order to derive the land surface parameters as required by the energy balance models to derive ET. Having estimated the land surface parameters, the two energy balance models SEBAL and S-SEBI were employed to estimate daily actual evapotranspiration. Furthermore the scintillometer measurements were processed to estimate the sensible heat flux and used together with the other ground based flux measurements to estimate actual ET. Finally a comprehensive analysis was made comparing the results obtained from different techniques. During the analysis of results, firstly the estimates of evapotranspiration obtained by different ground based methods were compared with each other. Afterwards a comparison was made between the results obtained by the two energy balance models based on remotely sensed data and subsequently the results of ground based methods were compared against remote sensing based results.


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1.6. Logical sequence of the methodology

Pre-field work phase

Field work phase Post field world phase

Figure 1-1 Three major phases and important steps in the methodology

Literature survey

Search for satellite imageries

Search for existing meteorological data

Search data for Atmospheric correction

Meteorological data collection

Ground Truth collection

Scintillometer experiment

Image pre-processing

Meteorological data processing

Scintillometer data processing

Atmospheric correction

Energy balance modeling

Surface flux analysis

Meteorological data analysis

Ground data correlation

ET Estimation


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RS based Ground based

Figure 1-2 Two different approaches used in the methodology with the main steps highlighted

1.7. Outline of the thesis

The thesis consists of seven chapters. Chapter 2 contains the literature review on physical background of land surface evapotranspiration, some important concepts in evapotranspiration and various estimation methods of evapotranspiration based on different approaches. Also this chapter discusses the primary data collection which was outlined in the methodology. Chapter 3 contains an introduction to the study area and a detailed description about the secondary data collection during the field campaign.

Satellite imageries from ASTER archives

Image pre-processing and atmospheric

correction.

Surface energy balance modeling

Estimate land surface parameters

Estimate Actual ET

Meteorological & scintillation data from Haarweg and Hupselse

Processing ground based data

Estimate surface fluxes

Apply standard methods

Estimate Actual ET

Compare & analyze results

Extrapolate surface fluxes

Estimate reference ET


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Chapter 4 explains the image pre-processing and atmospheric correction methods applied for ASTER imageries in both the visible and thermal bands. Chapter 5 discusses about the energy balance methods and remote sensing techniques for estimation of actual evapotranspiration. Chapter 6 illustrates the results obtained from different methods applied and discusses the comparison study. Chapter 7 concludes the report with recommendations stating the limitations of the study and suggestions for improvements.


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2. Literature review & primary data collection.

2.1. Physical background of land surface evapotranspiration

Evapotranspiration is a collective term for all the processes by which water in the liquid or solid phase at or near the earth’s land surface becomes atmospheric water vapour (Dingman 2002). Since the available water resource for the direct human consumption is the difference between precipitation and evapotranspiration over the long term, quantitative understanding of evapotranspiration is vital for water resource management. Since the evapotranspiration is the most difficult and expensive component to measure directly compared to precipitation and stream flow, hydrologists have developed an array of methods to estimate the evapotranspiration based on more readily measured quantities. Since most of these methods for estimating ET are physically based, it is important to analyze the physical background of the evapotranspiration process. As stated earlier evapotranspiration is essentially the combination of two processes where by water is lost on the one hand from the soil/water surface by evaporation and on the other hand from the crop by transpiration. Hence the individual processes are described below in order to explain the combined process.

2.1.1. Soil Evaporation

When the evaporating surface is bare soil it is called the soil evaporation. In addition to the so called climatological parameters the soil evaporation is affected by the degree of shading of the crop canopy and the amount of water available at the soil surface. The evaporation from a bare soil generally occurs in two distinct stages. In the first stage which is known as the atmosphere controlled stage, the evaporation will be governed by the same meteorological factors as those that control the evaporation losses from an open water surface. Hence at this stage the rate of evaporation is largely determined by the surface energy balance and mass transfer conditions and independent of soil water content. The evaporation rate at this stage is occurring at or near the free water evaporation. In the second stage which is known as the soil controlled stage, the evaporation rate is determined by the rate at which water can be conducted to the surface in response to potential gradients induced by upward decreasing soil water contents. Hence evaporation from a soil surface at this rate in general is less than that from an open water surface. This additional factor affecting the soil surface evaporation is known as evaporation opportunity. In saturated soil the evaporation opportunity can even exceed 100%, since the irregularity of the soil surface causes that the net surface from where evaporation can take place is much bigger than the case of an open water surface. Where ever the soil is able to supply water fast enough to satisfy the evaporation demand, the evaporation from soil is determined only by the meteorological conditions.


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2.1.2. Transpiration

Transpiration consists of vaporization of the liquid water contained in plant tissues and the vapour removal to the atmosphere. Crops predominantly lose their water through stomata, which are small openings on the plant leaf through which gasses and water vapour pass. The vaporization occurs within the leaf, namely in the intercellular spaces, and the vapour exchange with the atmosphere is controlled by the stomatal aperture. Similar to the direct evaporation, transpiration too depends on the energy supply, vapour pressure gradient and wind. Hence, radiation, air temperature, air humidity and wind speed are the important factors to consider when assessing transpiration. The soil water content and the ability of the soil to conduct the water to the root zone also affect the rate of transpiration. In addition to that the rate of transpiration is also influenced by crop characteristics, environmental aspects and cultivation practices. Different kinds of plants may have different transpiration rates and the same plant has different transpiration rates ant different crop development stages. (Allen et al. 1998)

2.1.3. Evapotranspiration Concepts

Although the basic physics were discussed for evaporation and transpiration separately, the two processes occur simultaneously and there is no easy way of distinguishing them. By summarizing all the affecting factors discussed under evaporation and transpiration it is possible to say that evapotranspiration is governed by the conditions in the lower part of the atmosphere, the presence and the properties of the vegetation layer and the subsurface soil moisture conditions. All these extremely variable conditions both spatially and temporally, make determination of evapotranspiration an exceptionally difficult task. However during the past 50 years scientists have put much effort on understanding the biophysical process behind this important hydro-meteorological phenomenon and, have come up with number of methods for quantifying it, especially after the introduction of Penman’s (1948) surface energy balance combination equation. “Despite the encouraging progress made in biophysical understanding of the evapotranspiration processes, simple and robust procedures to directly estimate actual rates of evapotranspiration from the land surface in to the atmosphere are still under development. The major bottleneck is that routine weather stations provide variables that can be used only to compute the reference and potential evapotranspiration. These provide an upper limit, the actual vapour flux density usually being lower”(Gieske 2003). The above statement introduces some useful concepts which have been used for quantifying evapotranspiration under different circumstances. Definitions of these concepts are given below in order to explain the principal differences. Potential evapotranspiration (ETpot) The maximum possible rate at which evapotranspiration would occur from a large area completely and uniformly covered with growing vegetation which is not short of water under given atmospheric conditions. Reference crop evapotranspiration (ETref) The evapotranspiration rate from a reference surface (hypothetical grass surface with specific characteristics) which is excellently managed, large, well watered field that achieves full production under given climatic conditions. The reference crop evapotranspiration is a redefinition of potential evapotranspiration by fixing the properties of the evaporating surface with specific terms.


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Actual evapotranspiration (ETact) The actual rate at which evapotranspiration occurs owing to the conditions of the atmosphere, with the presence of various stress functions and actual soil moisture conditions.

2.1.4. Estimation of potential / reference evapotranspiration

A Number of approaches has been developed for estimating the ETpot or ETref based on different theoretical concepts. Most commonly applied methods for hydrological studies can be classified into four categories on the basis of their data requirement. 1) Temperature based methods- use only daily average air temperature and some times the day length. 2) Radiation based methods- use both the net radiation and air temperature data for estimating ET. 3) Combination- use net radiation, air temperature, wind speed and relative humidity data based on the Penman-Monteith combination equation. 4) Pan measurement- use pan evaporation with modifications depending on wind speed, temperature and humidity. The methods that do not require information about the nature of the surface, estimate the reference crop evapotranspiration rate where as others are surface specific and do require information about albedo, vegetation height, maximum stomatal conductance, leaf area index and other factors. Some comparison studies have shown that out of all the methods Penman-Monteith combination method gives the best overall results under different climatic conditions.

2.1.5. Estimation of actual evapotranspiration

Potential-Evapotranspiration approach It is a known fact that other than the regions with abundant rainfall throughout the year where the potential rate of evapotranspiration is limited by the available energy, the actual evapotranspiration rate is water limited and essentially less than the potential rate. Hence the methods have been developed to estimate actual ET by relating it to the potential ET which can be estimated by methods mentioned earlier with empirical equations. Some of these equations use average precipitation whereas some relationships are based on soil moisture functions. However the most common methods used by the hydrologists for determining actual ET are based on either the water balance approach or the energy balance methods. Water balance approach This approach involves applying the water balance equation to the water body of interest over a time period of ∆T and solving the equation for evapotranspiration: ET = P - Q - Gout - ∆∆∆∆S where, ET = Evapotranspiration P = Precipitation Q = Stream flow Gout = Ground water out flow ∆∆∆∆S = Change in storage


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In this approach evapotranspiration is determined as a residual term and, all the other components given in the above equation have to be either measured or estimated. Generally it is accepted that the water balance approach yield an acceptable degree of error in evapotranspiration estimation if performed on longer periods e.g. decades or months. Other approaches are recommended whenever daily estimates of evapotranspiration are required. One of the widely used techniques on the basis of the water balance approach is the Lysimeter method (Dingman 2002). A Lysimeter is an artificial soil volume which can be used to determine the actual evaporation in a natural environment by accurately measuring the other components of the water balance; i.e. precipitation, soil moisture storage and deep percolation. Since Lysimeters offer the possibilities for precise measurements for water loss from soil and crop canopy surface, this method has been widely used especially in developing and testing methods to estimate actual ET. Energy balance approach The energy balance approach is nearly similar to the water balance method but deals with energy budget of an evaporating body instead of water flow. The complete energy balance equation for an evaporating body is given by Rn = H + λE + D +G +δA Where Rn = net energy input H = energy flux conducted as sensible heat λE = energy flux that utilized for evaporation D = horizontally advected net energy flux G = heat flux in to the sub medium δA = net energy flux that is stored in the water body causing a temperature change Further the relationship between the evapotranspiration (ET) and λE can be written as λE = ρw x λv x ET where ρw = density of water λv = latent heat of vaporization . The Penman-Monteith method and the Bowen-Ratio method (Dingman 2002) based on the energy balance approach, have been widely used for estimating evapotranspiration and the latter method being known as one of the most accurate methods to determine actual evapotranspiration. Apart from those techniques, the Eddy Correlation method (Brutsaert 1982) and the recently developed Scintillation method (De Bruin et al. 1995) are mentioned in the literature as techniques for accurate measurement of actual evapotranspiration based on the energy balance approach. These methods generally require data loggers for storing data for long periods of time with sensors capable of recording high speed measurements of meteorological parameters and surface fluxes. The energy balance method and its individual components will be further discussed in the chapter 5 of this thesis under energy balance methods and remote sensing techniques for estimating actual evapotranspiration.


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Use of Hydrological models Hydrological models are used to simulate the transformation of precipitation in to stream flow within a region. The models simulate by taking all the intermediate processes such as evapotranspiration, interception, infiltration, ground water flow etc. into account. Hence the models are capable of estimating evapotranspiration at many points within a large area. Physically based agro-hydrological model SWAP (Soil, Water, Atmosphere, Plant,) (Droogers 2000) and the basin scale hydrological model SLURP (Semi distributed Land Use based Runoff Processes) (Kite 2000) are some of the popular hydrological models mentioned in the literature.

2.2. Primary data collection

2.2.1. Selection of a study area:

As mentioned in the research objectives the study area was selected in such a manner that sufficient ground information is available to facilitate the calibration of energy balance models based on remotely sensed data. The Hupselse Beek catchment (Figure 2-1) situated in the eastern part of The Netherlands is a well-known experimental basin for hydrological research activities, studied in particular by the Wageningen University. Various scientists have studied the ground water and the surface water aspects of the catchment extensively over the past few decades. As a result, a number of research publications are available on the catchment accumulating a continuous and detailed data record, which can be used for further studies in the catchment. Also the catchment has a well-established automated meteorological network enabling the use of ground based energy balance methods to calculate actual evapotranspiration (Study-Group-Hupselse-Beek 1976). However, in the literature, there are few hydrological studies in which actual evapotranspiration has been determined independent of water balance considerations. Although no significant remote sensing work has been carried out before, sufficient number of satellite imageries covering the area could be found in ASTER archives. Hence the area offers much scope for calibration of energy balance models developed using remotely sensed data.

Figure 2-1 Location of the study area

EIBERGEN

GROENLO

Hupselse


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2.2.2. Brief description of historical studies in the area:

The history of the hydrological studies in the area goes back to early sixties when the 5200 ha. catchment area of Leerink Beek, in which the Hupselse Beek is one of the sub catchments, was instrumented for detailed hydrological studies mainly focusing on rainfall and runoff. Later on the need of meteorological data in the area to calculate actual and potential evapotranspiration was felt. In 1976 the meteorological equipment to measure net radiation and temperature profiles in the soil as well as in the lower boundary layer (up to 3 m), was installed in the area and subsequently the data collection system was automated. These data provided the possibility to calculate actual evaporation indirectly from sensible heat flux and energy-balance measurements, which are of fundamental value for hydrological (basin) research. Having assembled a valuable set of micro-meteorological data this huge program of measurements has been followed up in 1987 by a program similar to that at standard meteorological stations. In addition to that the stream flow is being measured at different locations with flumes and the rainfall runoff process is being studied using the so-called Wageningen model, which was developed in the 1970s (http://www.dow.wau.nl/whh/old/research/hupselbeshr.html).

Figure 2-2 Existing Hydro-meteorological network


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2.2.3. Availability of ground data

Hupselse (KNMI) weather station: With the completion of the programme for hydrological studies by the Wageningen University the Hupselse metrological station has been operated as on of the stations under the purview of the Dutch National Meteorological Institute. (KNMI). Table 2-1 shows the availability of meteorological parameters from the Hupselse KNMI during the period from January 2000 to October 2004. Table 2-1 Meteorological data availability at Hupselse weather station

Parameter Frequency Period Ave. air temperature Relative humidity Global radiation Ave. wind speed Precipitation

Hourly data -do- -do- -do- -do-

January 2000 to October 2004 -do- -do- -do- -do-

Haarweg weather station The weather station at the Haarweg in Wageningen is a special Agro-Meteo Station maintained by the meteorology and air quality department of the Wageningen University. The coordinates of the location are 51° 58’ NB; 5° 38’ OL; and is about 7 meters above the sea level.

Figure 2-3 Location of the Haarweg Agro-Meteo station in Wageningen The station is equipped with number of instruments measuring all the important meteorological parameters together with some useful surface fluxes at the location. The Table 2-2 shows the list of parameters and the particular instrument being used for measuring those.

N


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Table 2-2 Meteorological data being collected at Haarweg Data Instruments Type Relative humidity Hair hygrometer

Thermo-Hygrometer Temperature and relative humidity Ventilated Thermo-Hygrometer Air Pressure Wind speed at 4 levels Cup anemometer (4x) Wind direction Wind vane Short wave radiation Pyranometers Kipp en Zonen CM11 Long wave radiation Pyrgeometer Amount of precipitation Precipitation meter Mierij Meteo Precipitation duration Precipitation meter Thies Sun duration Sunshine Sensor Kipp&Zonen CSD1 Soil temperature (bare soil and grass)

Thermocouple PT 100

Soil heat flux Heat flux plates Sensible heat flux Large Aperture Scintillometer Self construction All instruments are connected to a Campbell 23X data logger, which stands inside a small building at the field. The measurements are done at every 5 seconds, and 10-minute averages are stored in the data logger. The stored data are transported by a telephone line to the department of Meteorology and Air quality where the processing of raw data is done. The processed data (10-minutes averages) are available on line through the university website from year 2001 May to date and freely downloadable. (http://www.met.wau.nl/).

2.2.4. Availability of Satellite data:

The primary data search for the satellite images in the TERA-ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) archives through EOS Data Gateway resulted with a granule list of 58 level 1A images covering the study area between the period of January 2000 and June 2004. Out of the 58 Level 1A images, 15 images have been processed to level 1B and the Figure 2-4 shows the temporal coverage of the list of level 1B images

Figure 2-4 Temporal coverage of ASTER L1B images


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After scrutinizing all the available images from both the level 1A and level 1B lists for the concerned location using a segment map of the catchment boundary, it was possible to identify 7 cloud free images spanning over the period from February 2002 to April 2004. The Table 2-3 describes the selected list of images for the proposed analysis Table 2-3 Image availability for the study area

2.2.5. Atmospheric correction data:

Atmospheric corrections are essential to rectify the scattering and absorption effects due to the presence of several constituents in the atmosphere. Out of all the elements amounts of Aerosols, water vapour and Ozone are important especially for the atmospheric corrections of images in visible and short wave infra red bands. Aerosol Optical Thickness at 550 nm and water vapour data: Presence of aerosols in the atmosphere affects the visibility and it is expressed as the Aerosol Optical Thickness (AOT). The hourly variation of AOT at different wavelengths (440 nm to 1020 nm) is observed at number of locations and the values are presented in the site of Aerosol Robotic Network (AERONET) under data. Also the same link provides the information about water vapour over the particular locations and expressed in units of gcm-2 or as a depth of water in cm. The Figure 2-5 shows level 2 data for AOT on 31st May 2003 observed at the station The Hague.

Figure 2-5 Chart of Aerosol Optical Thickness

Aster Image Acquisition Date Over-pass time(UTC) Level 1B 14th February 2002 10:40:20 Level 1B 16th August 2002 10:46:34 Level 1B 8th September 2002 10:52:36 Level 1B 8th May 2003 10:39:03 Level 1B 31st May 2003 10:45:22 Level 1B 3rd August 2003 10:44:07 Level 1A 15th April 2004 10:44:54


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The corresponding AOT values for different wave lengths at the satellite overpass time obtained from the AOT chart are given in Table 2-4. The AOT values so obtained were plot against the wavelength and based on the relationship derived for AOT and wavelength the AOT550 was calculated as given below. Table 2-4 AOT value for different wavelengths

λλλλ (µ (µ (µ (µm)))) 0.44 0.67 0.87 1.02 AOT 0.59 0.32 0.195 0.145

Chart of AOT vs Wavelengthy = 0.1545x-1.6688

R2 = 0.9952

0.1

0.2

0.3

0.4

0.5

0.6

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Wavelength (micro meters)

AO

T

Figure 2-6 Estimation of AOT at 0.550 micro meters AOT = β(λ)−α

Where β= 0.1545 and α = 1.6688 (from trend line equation of above chart) AOT_550 = 0.1545x [0.55]-1.6688 AOT_550 = 0.419 Also from the relevant chart given in AERONET site for water vapour data, the corresponding value for the satellite overpass time was obtained.


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Total Ozone Total Ozone over a location on a particular day can be obtained from the values observed by the Ozone Processing Team – NASA/GSFC code 916 using the Total Ozone Mapping Spectrometer (TOMS), available via the link given below. (http://jwocky.gsfc.nasa.gov/teacher/ozone_overhead_v8.html)

Figure 2-7 TOMS site for Ozone observations A Dobson Unit 1000 Dobson units is equal to 1 atm.cm The daily air pressure values collected by the nearest KNMI weather station at the Twenthe (Location: 52o 16’N, 06o 54’E, Elevation 34.5m) measured by a digital barometer, are available through the KNMI website (www.knmi.nl). The Table 2-5 shows all the relevant parameters required for atmospheric correction which were obtained as explained above for each an every image selected for the analysis. Table 2-5 Atmospheric correction data

Image Date Over-pass time AOT550 Water vapour Total Ozone Air pressure (dd-mm-yy) (UTC)- hh:mm:ss (cm) (Dobson units) (haPa)

14-02-02 10:40:20 0.063 0.44 324 1028.9 16-08-02 10:46:34 0.270 2.79 309 1019.1 08-09-02 10:52:36 0.172 3.02 303 1011.8 08-05-03 10:39:03 0.540 1.55 360 1018.1 31-05-03 10:45:22 0.419 2.00 353 1013.0 03-08-03 10:44:07 0.119 2.00 301 1022.4 15-04-04 10:44:54 0.450 0.80 331 1018.5


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3. Description of the study area and secondary data collection

3.1. Description of the study area

3.1.1. Location

The Hupselse Beek catchment is situated in the eastern part of The Netherlands in the province of Gelderland, between the townships of Groenlo and Eibergen. The catchment is geographically located between the latitudes from 52o 02’ to 52o 05’ North and longitudes from 6o 37’ to 6o 41’ East. The catchment area covers 6.5km2 and its altitude varies between 33m above NAP (reference level in the Netherlands) in the west to 24m near the outlet in the east. The average slope of the area is about 0.8%. The Hupselse Beek is the upper reach of the Leerink Beek, which discharges into the river Berkel, a tributary of the river IJssel. With its mild gradient of 0.06% to 0.25% the main river is 4km long and it has tributaries varying in length between 300 and 1500 meters.

Figure 3-1 Location of the Hupselse Beek catchment


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3.1.2. Climate

The study area is geographically located in the so called “temperate zone”. However due to strong maritime influences the regional climate is much milder than average conditions at the same latitudes. This average annual temperature of the study area is about 10 0C whilst the average annual temperature at latitude 52 0N is close to 4 0C. The average winter temperatures are around 3 0C and the average summer temperatures are around 16 0C. Also the mean monthly values of relative humidity are varying between 75% and 90% in the area. The figures 3-2 and 3-3 show the mean monthly variation of solar radiation, temperature and relative humidity of the study area from 2000 to 2004. (Source- Hupselse KNMI)

Mean Monthly Solar Radiation (2000-2004)

0

100

200

300

400

500

600

700

Jan.

Feb.

Mar.

AprMay

.Ju

n. Jul.

Aug.

Sep.

Oct.Nov

.Dec

.

Month

Rad

iati

on

(M

J/m

2)

Figure 3-2 Mean Monthly Solar radiation

Mean monthly variation of Temperature and Relative Humidity (2000 - 2004)

0

5

10

15

20

Jan. Feb. Mar. Apr May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Month

Tem

pera

ture

(oC

)

60

70

80

90

100

Rel

ativ

e H

umid

ity (%

)'

Mean Temperature Mean Relative Humidity

Figure 3-3 Mean Monthly Temperature and Relative humidity


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3.1.3. Precipitation

Mean monthly precipitation observations in the Hupselse catchment between the period of 2000 and 2004 show that the distribution is more or less uniform throughout the year. The mean annual precipitation calculated for the same period is about 870mm (Source -Hupselse KNMI).

Mean Monthly Precipitation (2000-2004)

0

20

40

60

80

100

120

140

Jan.

Feb.M

ar.

AprM

ay.

Jun. Ju

l.Aug

.Sep

.Oct.

Nov.

Dec.

Month

Pre

cipi

tatio

n (m

m)

Figure 3-4 Mean Monthly precipitation

3.1.4. Land Use

The land use of the area is predominantly agricultural and about 70% of the area is covered with grass. The arable land which is mostly covered with maize accounts for 21% of the total area. The rest of the area is classified as woodland and wasteland with 6% and 3% of the total area respectively.

Figure 3-5 Topography of Hupselse Beek Catchment


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3.2. Secondary data collection during field work

As mentioned in the second phase of the methodology a field campaign was carried out from 15th September 2004 to 6th of October 2004 in Hupselse Beek catchment, in order to collect the field information for the planned study. Following methods were used in order to gather sufficient field information for the analysis.

• Installing a temporary weather station for recording meteorological data and surface fluxes

• Running the scintillometer test using the LAS • Collecting ground truth with a hand held GPS for land use classification

3.2.1. Weather Station for meteorological data collection

The temporary weather station installed at the Hupselse was in operation from 29th September to 6th October 2004 recording most of the standard meteorological data including the radiation, soil temperature and soil heat flux. The weather station was put up in a large grass field centrally located in the Hupselse catchment. The coordinates of the location are 52 0 03’ 37 ” N; 06 0 38’ 51” E; and the station was about 24 metres above the sea level. A Mast of 4m high (30cm x 30cm. frame structure) with horizontal booms fixed at 2 levels was used to fixed all the sensor equipments and the data logger. The Table 3-1 show the full list of parameters recorded including the name and the type of the instrument used. The Figure 3-7 illustrates the technical diagram of the tower with all dimensions indicated in centimetres.

Figure 3-6 Weather station at Hupselse


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Table 3-1 Different parameters measured and the type of instrument used Data Instrument Model

Short wave Radiation (global and reflected, )

Net Radiometer (CM3 Pyranometer) 2 Nos

Kipp & Zonen –CNR1

Long wave radiation(incoming, outgoing )

Net radiometer (CG3 Pyrgeometer) 2 Nos

Kipp & Zonen –CNR1

Wind speed at 2 levels Switching Anemometer (2nos) A100R Wind direction Potentiometer Windvane W200P Air temperature and relative humidity at 2 levels

Combined humidity temperature probe with radiation Screen (2 nos.)

MP101A

Soil temperature at 4 levels

Soil Temperature Probe (4 nos)

Soil heat Flux Self calibrating Heat Flux Sensors (3 nos)

HFP01SC

Data logger Micrologger CR23x

18

110

80

40

197

177

15

18

110

80

40

197

177

15

Figure 3-7 Technical diagram of the tower


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12V DC batteries were used as the power supply and the voltage was maintained above 12V by regularly checking and replacing the discharged batteries. The data logger has been programmed to record the measurements at one second interval. The down loading of data was done twice a day in the morning and in the evening into a personal computer using the Loggernet software. The downloaded files with 1 second data were later on converted to 1 minute averages and the out put text files were converted to Excel format for processing. The memory of the data logger was refreshed every evening after downloading and, the Loggernet programme was reloaded to the data logger in order to start collecting. Radiation measurement The total radiation at a particular point on the earth surface depends on the net Solar radiation or the short wave radiation (0.3 to 3 micrometers) balance and the net Far Infrared radiation or the long wave radiation (5 to 50 micrometers) balance. The CNR1 Net Radiometer is an instrument intended for the analysis of the radiation balance of Solar and Far Infrared radiation. The most common application is the measurement of Net (total) Radiation at the earth's surface. The instrument is a combination of 2 pyranometers (CM3) for measuring incoming and reflected Solar Radiation and, 2 pyrgeometers for measuring incoming and outgoing Far Infrared radiation. Depending on the connections the instrument can measure all four components individually or the net radiation component. In this case the Net Radiometer was used to measure all 4 components separately. In addition to that the Net Radiometer is attached with a Pt -100 signal for accurate measurement of the body temperature of the equipment. This is required for the calculation of Far Infrared radiation components. As the instrument was mounted about 4m above the surface the input to the lower sensor are spatial averages from an area of 40m radius below the sensors.

Figure 3-8 Dimensions of the CNR1-Net Radiometer


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Wind Speed and Wind Direction measurement Wind speed is a scalar quantity specifying the magnitude of the horizontal movement of air. Two switching anemometers were used to measure the wind speed at 2m and 4m above the ground level. Wind direction is a measure of the direction of the horizontal air movement. It is the direction a wind vane is pointing, reported in degrees clockwise from true north. The Wind Direction was measured at 4m above the ground using a Potentiometer Windvanes. Air Temperature & Relative Humidity measurement Air temperature and relative humidity were measured at 2m and 4m above ground level using the MP101A combined humidity and temperature probe. The equipment has the humidity sensor (Rotronoc Hygromer C94) which is capable of recording the full range (0-100%) with high accuracy and the temperature sensor (Pt 100RTD) is capable of operating between -40oC and 60oC with high accuracy.

Figure 3-9 Switching Anemometer

Figure 3-10 Potentiometer Windvane

Figure 3-11 Sensor equipments for

temperature & humidity measurements


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Soil Temperature measurement Soil temperature is simply the temperature of the soil at a particular level and was measured under grass at 4 different levels using soil temperature probes. The Figure 3-12 shows the measured profile indicating the distances from the surface to the temperature probes in centimetres.

Soil Heat Flux measurement Soil heat flux is the rate at which heat is being conducted through the soil. It was measured using three self calibrating soil heat flux plates. The HFP01SC Soil Heat Flux plate consists of a thermopile and a film heater. The thermopile measures temperature gradients across the plate. The amount of power used to generate the calibration heat flux is measured by the data logger.

Figure 3-14 Soil Heat Flux Plate

Figure 3-12 Soil temperature profile

Figure 3-13 Soil Temperature Probes


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Figure 3-15 The charts of Short wave radiation , Long wave radiation and Air Temperature


28

Figure 3-16 The Charts of Relative Humidity, Wind Speed and Soil Temperature


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3.2.2. Scintillometer Experiment

About the instrument The Large Aperture Scintillometer (LAS) is an instrument designed for measuring the path-averaged structure parameter of the refractive index of air (Cn

2) over horizontal path lengths from 250 m to 4.5 km. Structure parameter measurements obtained with the LAS and standard meteorological observations (air temperature, wind speed and air pressure) can be used to derive the surface sensible heat flux (H). The LAS optically measures intensity fluctuations (known as scintillations) using a transmitter and receiver horizontally separated by several kilometres. The scintillations seen by the instrument can be expressed as the structure parameter of the refractive index (Cn

2) of air. The light source of the transmitter operates at a near-infrared wavelength of 880 nm. At this wavelength the observed scintillations are primarily caused by turbulent temperature fluctuations. Therefore (Cn

2) measurements obtained with the scintillometer can be related the sensible heat flux. Table 3-2 Technical data of the instrument Operating Temperature -20 oC to +50 oC

Voltage 12V DC nominal Optical wavelength of LED 880 nm

Modulation frequency 7 kHz (duty cycle 0.5) Aperture diameter 0.152 m (6 inch) Setting up procedure The instrument was setup in the same grass field where all the other meteorological and flux measurements were made. The receiver was placed on top of the same mast which was used for the weather station and an identical frame structure was used to place the transmitter which was at a distance of 680m from the receiver. The locations of the receiver and the transmitter site were selected so that the instrument was approximately in the North-South orientation to avoid problems caused by low sun angles.

Coordinates of the transmitter: 520 03’ 58” N 060 38’ 44” E Coordinates of the receiver: 520 03’ 37” N 060 38’ 51” E

Figure 3-17 Location of the instrument


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Minimum Height – Path length selection According to the theory the scintillometer can operate only under weekly scintillating conditions. When the scintillation intensity rises above a certain limit, the theory on which the scintillation measurement method is based, becomes invalid. When this occurs the relationship between the measured amount of scintillations and the structure parameter of the refractive index of air (Cn

2) fails. This phenomenon is called saturation. It is also known that for a given surface condition the scintillation intensity or the Cn

2 depends on optical wavelength (λ), the aperture diameter (D), the measurement height (ZLAS) and the path length (L). Since all the other parameters are fixed apart from (ZLAS) and L the saturation can be avoided only by selecting the Correct Height- Path length combination. A scintillometer installed at a height close the earth’s surface; will see more scintillations than a scintillometer installed high above the surface. As the path length increases more scintillation will be observed. Furthermore, the measured amount of scintillations depends on the surface conditions. Over dry areas the surface sensible heat flux is large, resulting in higher Cn

2 values than over wet surfaces where the sensible heat flux is small. The charts (Figure 3-18) have been developed for minimum height as a function of path length for different surface conditions ranging from very dry (H= 500Wm-2) to very wet (H= 50Wm-2) to avoid so called saturation condition (extracted from LAS instruction manual). According to the charts even for the driest surface conditions, the minimum height for a path length of 680m is around 3m. Hence the selected height of 4m was safe enough for the instrument to operate in the saturation free zone. Alignment procedure In order to establish the optimal signal strength for horizontal line-of-site transmission, the transmitter and the receiver has to be aligned through an iterative process. This is done by basically rotating the transmitter and the receiver around both vertical and horizontal axes. The coarse adjustments were done before completely tightening the bolts that are used to fix the adjuster bottom flange to the base plates of the towers. Once those bolts are tightened the finer adjustments for horizontal alignment

Figure 3-18 Minimum installation height as a function of path length


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(pan) and vertical alignment (tilt) were done using special adjustment screws and bolts provided for pan adjustments and tilt adjustments. Once the equipments were mounted on their respective positions two persons carried out the alignment, one at the receiver and one at the transmitter while communicating with a walkie-talkie. The two telescopes which have been factory aligned each for transmitter and the receiver was mounted to the rails on top of the respective devices. First the transmitter was moved both horizontally and vertically such that the crosshairs of the telescope mounted on it were centred on the receiver. The same was done with the receiver relative to the transmitter. Then the bolts were tightened fixing the tilt and pan adjusters to the supporting structure leaving room for further adjustments by pan fine adjustment screws. In the next stage both the transmitter and the receiver were connected to 12V DC supplies. The receiver mode was set to the “signal” and the “current adjust” knob in the transmitter was set such that a signal can be detected in the receiver through the analogue meter at the control panel of the receiver. Then the transmitter was turned slowly horizontally from left to right using the adjustment knob while observing the signal strength of the analogue meter at the receiver. The centre of the beam was established using the optimal signal strength as observed at the receiver. In the same manner the transmitter was adjusted vertically by turning up-wards and down-wards while observing the signal strength to find the beam centre or the mid point between the vertical edges of the beam. Once the transmitter was properly adjusted both horizontally and vertically the position was fixed by tightening the bolts. The same procedure was adopted for the receiver as well to find the mid point between the horizontal and vertical edges of the beam, while keeping the transmitter position fixed. The whole procedure was repeated until the optimum alignment is reached. When the instrument is properly aligned the two horizontal fine-adjustments bolts and the two vertical adjustment bolts at the transmitter and receiver were tightened.

Figure 3-19 The behaviour of the signal strength when transmitter/receiver is turned horizontally.


32

Setting path length potentiometer Once the instrument was installed and properly aligned the Path length dial knob was set to the proper value using the correct distance between the transmitter and the receiver. The value was calculated using the equation given below.

4723.5)10.3314.0474.4(

81.54751233/7

−��

�

�

��

�

�

+=

−LDPotLAS

Where D is the aperture diameter and L is the distance between the receiver and the transmitter.

Figure 3-20 Two masts standing in the field with transmitter and the receiver fixed on top of each Recording data The same logger (Campbell scientific - 23x) was used to record the output signal UCn

2 (log of Cn2),

σUCn2 (Variance of UCn

2), PUCn2 (Scaled UCn

2) and the demodulated carrier or the Demod at every 1 second interval. The Table 3-3 shows some typical values observed for measured variables after converting to 1 minute average values. Table 3-3 Typical values of the measured variables Date & Time UCn

2 σσσσUCn2 PUCn2 Dmod

9/30/04 12:30 -1.068 0.011 88.0280 -0.200 9/30/04 12:31 -0.992 0.013 105.6400 -0.202 9/30/04 12:32 -0.927 0.013 122.5600 -0.197 9/30/04 12:33 -0.893 0.007 130.4200 -0.200 9/30/04 12:34 -0.931 0.010 120.3800 -0.196 9/30/04 12:35 -0.943 0.009 116.9700 -0.200 9/30/04 12:36 -0.999 0.008 102.2600 -0.197 9/30/04 12:37 -1.053 0.011 91.0730 -0.196 9/30/04 12:38 -1.033 0.010 95.2860 -0.196 9/30/04 12:39 -0.965 0.008 111.0200 -0.194 9/30/04 12:40 -1.007 0.013 101.7900 -0.195


33

3.2.3. Ground truth collection by a GPS survey

In order to prepare an accurate land use map ground truth collection was done in the Hupselse catchment using a hand held GPS. The boundaries of the plots having different land use types were traversed with the GPS and sufficient ground control points were collected together with the land use information. The figure below shows the point map created in ILWIS using the GPS information.

Figure 3-21 Way Points collected by GPS survey

The image classification was done using the most recent image obtained on 15th April 2004. The ground information collected together with the map of the control points was used for identifying different land use plots in the satellite image. Once they are identified polygons were created for each and every plot and classified into some 7 selected land use classes.

Figure 3-22 Land use classes in Hupselse Beek


35

4. Pre-processing of ASTER images

4.1. Introduction to ASTER imageries

The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) is an advanced multispectral imager that covers a wide spectral region with 15 bands from the visible to the thermal infrared with high spatial, spectral and radiometric resolution. The images are acquired by 3 different telescopes and the spatial resolution varies from 15 m in the visible and near-infrared (VNIR) to 90 m in the thermal infrared (TIR). Each ASTER scene covers an area of 60 x 60 km. The ASTER instrument produces two types of Level-1 data: Level-1A (L1A) and Level-1B (L1B). ASTER L1A data are formally defined as reconstructed, unprocessed instrument data at full resolution. They consist of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data, thus maintaining original data values. The L1B data are generated by applying these coefficients for radiometric calibration and geometric resampling. ASTER images are files written in the Earth Observation System Hierarchical Data Format (EOS-HDF) format, which is a type of the HDF4 file format. Table 4-1 Spectral characteristics of ASTER Wave length

region Band Number Spectral range

(µm) Spatial

resolution (m.) Swath width

(km)

1 0.52-0.60 2 0.63-0.69 3N 0.78-0.86

VNIR

3B 0.78-0.86

15

4 1.60-1.70 5 2.145-2.185 6 2.185-2.225 7 2.235-2.285 8 2.295-2.365

SWIR

9 2.360-2.430

30

10 8.125-8.475 11 8.475-8.825 12 8.925-9.275 13 10.25-10.95

TIR

14 10.95-11.65

90

60

(Extracted from ASTER User Handbook)


36

4.2. Importing of ASTER level 1A/1B Data using ILWIS

The import functionality in ILWIS supports the HDF 4 file format and hence the ASTER level 1A/1B data can be imported directly in ILWIS. The level 1A import will result in an object collection of 15 raster images (4 VNIR bands, 6 SWIR bands and 5 TIR bands), 15 georeferences (1 for each band) and one coordinate system whereas level 1B import will result in an object collection of 15 raster images and 4 georeferences (1 for each sensor group) and 1 coordinate system.

4.2.1. ASTER Radiometric calibration

ASTER level 1A data are Digital Numbers (DN) describing the voltage measured by the sensor distributed within a suitable range (0-255 for VNIR and SWIR bands and 0-4094 for the TIR bands). These Raw DN can be converted to spectral radiance (expressed in unit of Wm-2sr-1µm-1) using the coefficients given in the radiometric correction tables embedded in the HDF file. ILWIS uses following formulae to convert the raw Digital Numbers to spectral radiances. Lij = AjVij / G + Dj (for VNIR and SWIR bands) Lij = C0,i + C1,iVij + C2,iVij

2 (for TIR bands) Where Vij is the raw DN value at a specific row, column position and Lij is the radiance value of the corresponding pixel with all the other constants are extracted from the metadata in the HDF file. ASTER Level-1B data are offered in terms of scaled radiance as sensor calibrated Digital Numbers (DN). To convert DN to radiance at the sensor, the unit conversion coefficients are used. Radiance (spectral radiance) is expressed in unit of Wm-2sr-1µm-1. The relation between DN values and radiances is expressed below: (i) a DN value of zero is allocated to dummy pixels (ii) a DN value of 1 is allocated to zero radiance (iii) a DN value of 254 is allocated to the maximum radiance for VNIR and SWIR bands (iv) a DN value of 4094 is allocated to the maximum radiance for TIR bands (v) a DN value of 255 is allocated to saturated pixels for VNIR and SWIR bands (vi) a DN value of 4095 is allocated to saturated pixels for TIR bands The maximum radiances depend on both the spectral band and gain setting. The sensor calibrated DN values are converted to spectral radiance in ILWIS using the unit conversion coefficient of each band as follows; L = (DN value – 1) * C ; where L = Calculated spectral radiance (Wm-2sr-1µm-1) DN = Sensor calibrated DN value C = Unit conversion coefficient from HDF file. Unit conversion coefficient used by ILWIS for different bands and for different gain settings are shown in Table 4-2.


37

Table 4-2 Unit conversion coefficients for calibration Band No. Unit conversion coefficient (Wm-2sr-1µm-1)/DN

High Gain Normal Gain Low Gain 1 Low Gain 2 1 0.676 1.688 2.25 N/A 2 0.708 1.415 1.89 3N 0.423 0.862 1.15 4 0.1087 0.2174 0.29 0.29 5 0.0348 0.0696 0.0925 0.409 6 0.0313 0.0625 0.0830 0.390 7 0.0299 0.0597 0.0795 0.332 8 0.0209 0.0417 0.0556 0.245 9 0.0159 0.0318 0.0424 0.265 10 N/A 6.822 x 10-3 N/A N/A 11 6.780 x 10-3 12 6.590 x 10-3 13 5.693 x 10-3 14 5.225 x 10-3 (Extracted from ASTER User Handbook)

4.2.2. Geometric correction of ASTER images

As mentioned earlier when importing ASTER level 1A data in ILWIS, a georeference is created for each and every band using the geometric data given in the HDF files. Different formulae have been given in the ASTER user’s guide to convert the tie point coordinates given in geocentric Lat-Lons of each image to geodetic (WGS84) Lat-Lons. The Lat-Lon tie points are converted to Universal Transverse Mercator (UTM) coordinates of the appropriate zone. A third order transformation is used for this process using the full set of tie points available in the HDF file. Unlike level 1A data, level 1B data contains images already resampled to the geometry of the appropriate UTM projection with the WGS84 Datum. Each sensor group has a common georeference with 4 corners as tie points. Once the images were approximately georeferenced using the ephemeris data, each geo-reference was corrected using ground control data. The ground control information was collected by digitizing some linear features like roads, rivers and canals from an existing topographical map of the area with the same projection system. After overlaying these vector features on the automatic georeference the observed shift was corrected manually by editing the tie point coordinates. The procedure was repeated for each georeference used by different sensor groups in the case of level 1B images. In the case of level 1A georeference, each spectral band was separately treated for correcting them using the vector data.


38

4.2.3. Conversion of radiance to reflectance at top of the atmosphere

Reflectance (ρ) is defined as the wavelength dependant ratio between reflected and incoming energy and can be expressed as

ZESUNdL

θπρλ

λ

cos

2

= Where;

Lλ = Radiance at the sensor (Wm-2sr-1µm-1) d2 = Earth Sun distance (AU) θZ = Solar zenith angle (degrees) ESUN = Band dependent Exo-atmospheric Irradiance (Wm-2µm-1) Earth Sun distance depends on the day of the year (Julian day) and the solar zenith angle depends on day and time of acquisition of the image and the latitude and the longitude of the location. Table 4-3 shows the calculated values for Earth Sun distance and solar zenith angle for the selected set of images. Table 4-3 Values of Earth-Sun distance and solar zenith angle

Image Date d2 (AU) θθθθ (degrees) 14-02-04 0.975 67.0 16-08-02 1.025 39.4 08-09-02 1.014 46.8 08-05-03 1.019 36.6 31-05-03 1.028 31.5 03-08-03 1.029 36.1 15-04-04 1.007 43.4 Table 4-4 Exo-Atmospheric irradiance values of ASTER bands Band ESUN (Wm-2µm-1) 1 1846 2 1555 3N 1120 4 231 5 79 6 74.4 7 70.5 8 59.6 9 56.3


39

4.3. Atmospheric correction for satellite imageries

The signals received by a multi spectral satellite sensor are significantly affected by the various constituents present in the atmosphere. Atmospheric components like aerosols and molecules scatter and absorb solar radiation, there by significantly influence the contrast and apparent resolution of the satellite imageries in the visible and near infrared bands. The radiance measured by the sensors may be increased or decreased depending on the prevailed atmospheric conditions at the acquisition time of the image. Therefore it is essential to consider the effects of the atmosphere and apply necessary corrections during the pre-processing of digital data for image interpretation work especially in time series analysis of remotely sensed imageries.

4.3.1. Atmospheric correction methods

The fundamental philosophy of atmospheric correction is to determine the optical characteristics of the atmosphere and then to apply this to correct the data (Kaufman 1989). There are number of atmospheric correction methods mentioned in the literature (few listed below). While discussing the effectiveness of some of the correction techniques, some authors have categorized the methods under two main types viz. absolute correction methods and relative correction methods (Hadjimitsis and Clayton 2004). The absolute corrections are the corrections that lead to surface reflectance and the relative corrections are the corrections that do not produce surface reflectance, according to this categorization. Also absolute correction methods have been sub divided as image based corrections or corrections that are applied principally using information extracted from the satellite image and, corrections that use atmospheric optical conditions including historical, standard or meteorological data and in situ data. Some typical examples for this particular category of atmospheric corrections are:

1. Lowtran or Modtran code (Kneizys and Gallery 1988) 2. 5S (Simulation of the Satellite Signal in the Solar Spectrum) model (Tanre 1990) [now the 6S

model] (Vermote 1996; Vermote et al. 1997).

3. SMAC (Simplified Method for Atmospheric Correction) model which is largely derived from 5S model (Quaife and Barnsley 1999; Rahman and Dedieu 1994).

The first two methods being exact models not only produce some accurate results but also need accurate information on atmospheric parameters. (aerosol properties, water vapour content etc.) This could be some times problematic specially to determine for large areas and long time periods. When compared to the first two models the 5S model was found to be simpler and faster. However this too was found to be expensive and time consuming, especially to be used on an operational basis for the atmospheric correction of vast number of satellite images. This is particularly true for the images acquired with large field of view instruments, since in these images each pixel has different observational geometry and each atmospheric parameter has to be calculated for each pixel. The Simplified Method for atmospheric correction or the SMAC model which is largely derived from the 5S model appeared to be a better method to deal with above problems as a fast and reasonably accurate technique for the atmospheric correction of satellite imageries in the solar spectrum.


40

4.3.2. SMAC: Simplified Method for Atmospheric Correction

SMAC a simplified method for atmospheric correction of satellite measurements in the solar spectrum (Rahman and Dedieu 1994) has been developed as a computationally fast reasonably accurate technique for the atmospheric correction of satellite measurements in the solar spectrum. The technique is supposed to be based on a unique set of equations with different coefficients which depend on the spectral band of the sensor. Semi- empirical formulae are use to describe the different interactions (absorption, scattering, etc) of solar radiation with atmospheric constituents during its traverse through the atmosphere. The method further explains that the sensor specific coefficients of each equation are determined using a best fit technique against the computations of the 5S code. Once coefficients for specific spectral band are determined, the inputs of the model are vertically integrated gaseous contents, aerosol optical depth at 550nm, geometric conditions and reflectance at the top of the atmosphere. (TOA). The results have been compared with the values obtained by 5S for a wide range of gaseous and aerosol contents, different illumination and observation conditions for different sensor spectral bands. The maximum relative error found was under 4% for sensors such as NOAA-9/AVHRR, METEOSAT-1, LANDSAT-5 /TM1 and LANDSAT-5/TM4 according to the comparisons. Also the paper has mentioned that the method has been particularly designed for the correction of huge amount of data acquired with large field of view and high temporal resolution. The ability of the method to perform atmospheric corrections several hundred times faster than the more detailed radiative models like 5S, capability to use for both in the direct and inverse mode and its capacity to accommodate new sensors only by updating the band specific coefficient files are mentioned as advantages of the SMAC over the other methods.

4.3.3. SMAC for ILWIS

SMAC for ILWIS is a windows based application for atmospheric effects correction for some specified sensors e.g. ASTER, MERIS, MODIS etc based on the SMAC algorithm. An interface has been developed to input a raster map of top of the atmosphere reflectance in ILWIS format with relevant atmospheric and geometric parameters and to obtain the output raster map for surface reflectance in ILWIS format applying the SMAC algorithm. The main steps in using the interface to correct top of the atmosphere reflectance for atmospheric effects are as follows.

• Set the working directory to the location where all the input ILWIS raster maps in value domain with same georeference are stored.

• Select the input map for top of the atmosphere reflectance. • Select input maps for aerosol optical depth at 550nm, water vapour content, ozone

concentration and surface pressure or provide a value otherwise. • Select the coefficient file (sensor specific and band specific) from the provided list. • Select input maps for solar zenith angle, solar azimuth angle, view zenith angle and view

azimuth angle or provide a value otherwise. • Specify a name for the output map for surface reflectance. • Press “OK” to run the application which will create the output map for the surface reflectance

in the specified working directory.


41

Figure 4-1 Form of input parameters for SMAC interface Table 4-5 Properties of input parameters for SMAC

Input parameter (Map/value)

Map Domain

Value range Units

TOA reflectance Value 0.0 - 1.0 -- AOT at 550nm Value 0.05 - 0.8 -- Water vapour content Value 0.0 – 6.0 gcm-2 Ozone concentration Value 0.0 – 0.7 atm.cm Surface pressure Value ha.p Solar zenith angle Value -360 - 360 degree Solar azimuth angle Value -360 - 360 degree View zenith angle Value -360 - 360 degree View azimuth angle Value -360 - 360 degree


42

4.3.4. Correcting the images of the study area using SMAC for ILWIS

Once the radiometric and geometric corrections are applied as described earlier in this chapter, sub maps were created covering the study area, for VNIR bands (band 1 to 3N) and the SWIR bands (band 4 to 9) of each ASTER scene. These sub maps of radiance were then converted to maps of reflectance values at the top of the atmosphere. Afterwards, previously calculated atmospheric parameters and the geometric parameters for each of the image (see Table 2-5) were used together with the top of the atmosphere reflectance map of a particular band of the image and the built in band specific coefficient file to estimate the surface reflectance map of the band in the SMAC algorithm.

Figure 4-2 Flow chart of image pre-processing to derive surface reflectance maps

ASTER Band (1 to 9)

Radiometric and Geometric corrections

Create Sub maps of radiances

Convert to TOA Reflectance

SMAC for ILWIS

Surface Reflectance Maps

Radiometric coefficients

Georeference data

Earth-Sun dist. and Cos(θ)

ESUN values

Atmospheric parameters

View Geometric parameters


43

Figure 4-3 Comparison of Band 1 reflectance before and after atmospheric corrections It is observed from the histograms of band 1 reflectance of image on 15th April 2004 the average reflectance has decreased from 0.12 to 0.07 after the atmospheric corrections. Also some bands show an increase in average reflectance when the corrections are applied to TOA reflectance for atmospheric effects. The figures 4-4 & 4-5 show the alterations caused in NDVI and broad band albedo derived using average band reflectances for different image dates, due to atmospheric effects.

Change in NDVI due to atmospheric effects

0.40

0.50

0.60

0.70

0.80

0.90

1/1/2002 6/30/2002 12/27/2002 6/25/2003 12/22/2003 6/19/2004

Date

ND

VI

From TOA reflectance From surface reflectance

Figure 4-4 Change in NDVI with atmospheric corrections


44

Change in broad band albedo due to atmospheric effects

0.10

0.14

0.18

0.22

1/1/2002 6/30/2002 12/27/2002 6/25/2003 12/22/2003 6/19/2004

Date

Bro

ad b

and

albe

doFrom TOA reflectance From surface reflectance

Figure 4-5 Change in broad band albedo with atmospheric corrections

4.3.5. Application of MODTRAN 4 for thermal bands

In thermal bands the radiance measured at the sensor is altered due to the atmospheric effects. The parameters which cause this modification are the up welling and downwelling components of the atmospheric radiance and the atmospheric transmittance. The upwelling radiance augments the ground radiance (black body radiance and the reflected component of downwelling radiance) whereas the atmospheric transmittance attenuates the ground radiance, when the signal travels through the atmosphere to the satellite. Hence the radiance measured at the jth band of the satellite (Lj

sat) can be expressed as;

( )[ ] jjjjBB

jjsat

j LLLL ↑↓ +−+= τεε 1

Where, jτ is the atmospheric transmittance, jL↑ the upwelling radiance, jL↓ the downwelling

radiance and jBBL the black body radiance defined by Plank’s Law.

It is obvious from the above equation that in order to determine the radiant temperature and the surface emissivity from the satellite radiance, the atmospheric components should be eliminated first.

The estimation of the atmospheric parameters jL↑ , jL↓ and jτ has to be done using a suitable

radiative transfer model. In this case MODTRAN 4 developed by the US Air force geophysics laboratory was used to determine the up welling radiance, the down welling radiance and the atmospheric transmittance for all the ASTER thermal bands. The MODTRAN code basically uses the sun satellite geometry and the atmospheric conditions during the satellite overpass to estimate the upwelling and downwelling radiance and the transmittance. The atmospheric parameters required by the MODTRAN are primarily the aerosol amount, the temperature and water vapour profile and the amount of ozone or other gases.


45

Since the exact atmospheric profiles were not known during the satellite overpass time, the standard atmospheres were used in the software with modifications. The standard atmospheric profiles for water vapour and ozone were scaled to match the total values estimated for the corresponding parameters. The best atmospheric profile among the given standard profiles for a particular image was selected in such a way by maintaining the scaling constants closer to unity. (between 0.9 to 1.1) Once the integrated value of the scaled profile matches with the total value estimated for the parameter the profile was used in MODTRAN as a user defined atmosphere. The weighted average for a particular band is defined as

�

�=

=

=

== 12

8

12

8

)(

)(*)(_ λ

λ

λ

λ

λ

λλ

i

i

r

XrAverageX

Where; X = Lup, Ldn or τ for each ASTER band ri(λ) = ASTER TIR sensor response λi in steps of 0.01 µm

Figure 4-6 MODTRAN tape 5 showing the basic input parameters


46

Figure 4-7 Basic steps for estimating atmospheric radiances and transmittance in MODTRAN 4

Basic input parameters to MODTRAN 4

(Tape5)

MODTRAN 4 (FORTRAN, 4V2R1.exe

Tape 6 7SC file FLX file Tape 7 Tape 8

Multiply with ASTER TIR sensor response for each band

and calculate the weighted

Lup -weighted average for each ASTER TIR

band

Ldn -weighted average for each ASTER TIR

band

τ − weighted average for each ASTER TIR

band

ASTER band response data


47

Table 4-6 Summary of the MODTRAN output for average values of Lup, Ldn and τ Image date

Parameter Band

2/14/2002 8/16/2002 9/8/2002 5/8/2003 5/31/2003 8/3/2003 4/15/2004

B10 0.842 0.526 0.509 0.652 0.609 0.625 0.752

B11 0.885 0.633 0.624 0.714 0.687 0.723 0.789

B12 0.919 0.700 0.696 0.758 0.741 0.790 0.823

B13 0.963 0.696 0.674 0.821 0.779 0.809 0.892

τ_average

B14 0.963 0.651 0.623 0.805 0.752 0.777 0.890

B10 0.475 3.013 3.127 1.820 2.091 1.977 1.019

B11 0.361 2.451 2.513 1.604 1.782 1.535 0.948

B12 0.248 2.090 2.125 1.428 1.557 1.223 0.842

B13 0.165 2.388 2.565 1.235 1.528 1.311 0.635

Lup_average (Wm-2sr-1µm-1)

B14 0.162 2.681 2.901 1.316 1.691 1.507 0.634

B10 0.792 4.573 4.719 2.999 3.383 3.252 1.587

B11 0.576 3.708 3.819 2.457 2.751 2.488 1.344

B12 0.390 3.223 3.312 2.129 2.374 2.002 1.155

B13 0.243 3.611 3.900 1.750 2.273 2.099 0.793

Ldn_average (Wm-2sr-1µm-1)

B14 0.242 4.011 4.342 1.905 2.543 2.402 0.819

4.3.6. Temperature Emissivity Separation

Having estimated atmospheric radiances and transmittance, the narrowband emissivity and the surface temperature were estimated for each of the ASTER scene used for the study. The estimation was done using the ILWIS script developed to estimate absolute surface temperature using ASTER data reported by (Wubet 2003). The script has been developed for estimating surface temperature and emissivity from ASTER thermal infrared images based on the Temperature Emissivity Separation (TES) algorithm introduced after (Gillespie 1998; Schmugge 1998). Surface temperature is independent of wavelength and can be determined even from single band radiance provided that the atmospheric characteristics and surface emissivity are known. However, emissivity of land surfaces is an unknown which has to be estimated along with the temperature. This makes the inversion of Planck equation for temperature and emissivity a difficult task as always there are (n+1) unknowns for radiances measured in n spectral channels (n emissivities and 1 surface temperature). TES algorithm mentioned above is based on an empirical relationship between the minimum emissivity (εmin) and maximum minimum difference (MMD) determined from laboratory measurements. Through an iterative procedure this relationship has been used to equalize the number of unknowns so that the set of Planck’s equations obtained for radiance of 5 thermal infrared bands can be inverted. Running the ILWIS script it was possible to obtain five emissivity maps and a surface temperature map for each ASTER image. Please refer to (Wubet 2003) for the detailed ILWIS script used for the calculation.


49

5. Remote sensing surface energy balance models

5.1. Introduction

Remote sensing is the process of inferring surface parameters from measurements of both the reflected and emitted electromagnetic radiation from the land surface. Usually the reflected radiation is in visible and near infra red (VNIR) portion while the emitted is in both the thermal infrared (TIR) and microwave portions of the spectrum. With remote sensing it is possible to obtain the spatial variability of surface parameters and also the temporal variability if observations are made repeatedly along the time axis. A major focus of remote sensing research in hydrology has been to develop approaches for estimating hydro-meteorological states and fluxes primarily soil evaporation and plant transpiration or evapotranspiration (ET).

5.2. Surface energy balance equation

It is known that the energy balance and water balance at the land surface are closely linked with each other by evapotranspiration. Since remote sensing techniques offer much scope to quantify the land surface fluxes, over the period of time methods have been developed to quantify evapotranspiration linking to the surface energy balance. The energy balance equation and in its simplest form can be written as;

LEHGRn +=− (5.01)

Where Rn is the net radiation, G is the soil heat flux, H is the sensible heat flux and, LE is the latent heat flux, with all units expressed in [Wm-2]. The sign convention is such that the fluxes are positive when the radiation is directed toward the land surface. The quantity (Rn – G) is commonly referred to as the available energy. ET and LE represent the same water vapour exchange rate across the surface–atmosphere interface, except ET is usually expressed in terms of depth of water over daily and longer time scales, namely mm/day.

Figure 5-1 Surface Energy Balance Components


50

5.3. Remote sensing techniques and energy balance models

5.3.1. Evolution of remote sensing models

The capability of remotes sensing techniques to observe land surface processes has expanded significantly during the last two decades. According to the available literature during this period several models have been developed to estimate surface energy fluxes from remotely sensed data. Methods for determining actual evapotranspiration from bare soil and vegetation at the regional scale have been reviewed by number of scientists. Pioneer work on utilizing thermal infrared observations for estimating consumptive use in agriculture was carried out by Idso et al. (1975) and Jackson et al (1977). Methods for determining actual evapotranspiration from bare surfaces and vegetative transpiration at the regional scale have been reviewed by Choudhury(1989), Baily(1990), Engman and Gurney(1991), Moran and Jackson (1991), Menenti(1993), Norman et al(1995) and more recently by Bastiaanssen et al.(1999), Roerink et al.(1999) and Jia et al (2003).

5.3.2. Primary data for the energy balance models

When estimating evapotranspiration by remotely sensed data, energy balance models basically use the relationship between the evapotranspiration and surface temperature. Usually the surface temperature is an observed variable when applying remote sensing techniques. Thermal infrared images in the wavelength range 8 to 14 µm are therefore an essential requirement for this particular method. It is known that the surface temperature is much affected due to the efficiency by which the surface transmits radiant energy or the surface emissivity. Surface emissivity depends on the composition, surface roughness, and physical parameters of the surface and generally varies with wavelength for natural surfaces. Thus to make a quantitative estimate of the surface temperature it is essential to separate the effects of temperature and emissivity in the observed radiance.

5.3.3. Expansion in remotely sensed data

Until recently, methods for estimating surface emissivity from remote sensing were empirical. With the availability of multi-spectral TIR data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) of NASA’s Earth Observing System Platform TERRA after 1999, a technique has been proposed to extract both land surface temperature and emissivity. This approach makes use of a rather robust empirical relation between the range of emissivities and the minimum value from a set of multi-channel observations better known as temperature emissivity separation or TES. As already stated in the objectives, this study mainly focuses on estimating and comparing the actual evapotranspiration using two remote sensing based energy balance models viz. SEBAL and S-SEBI using ASTER imageries. The use of ASTER data for the analysis would produce better results, since accurate surface temperature and emissivities are crucial for both the energy balance models intended to use. A detailed description of the two energy balance algorithms will be given in the next sections.


51

5.4. The Surface Energy Balance Algorithm for Land (SEBAL)

The Surface Energy Balance Algorithm for Land (SEBAL) is a parameterization of the energy balance and surface fluxes based on spectral satellite measurements. SEBAL requires visible, near-infrared and thermal infrared input data, from satellite imageries. The model comprises of number of computational steps for image processing and finally calculates the actual evapotranspiration (ETact) as well as other energy exchanges between land and atmosphere. Satellite radiances will be converted primarily into surface parameters such as hemispherical surface reflectance r0, surface temperature T0, and vegetation indices which in turn will be used to derive the surface energy fluxes. Having estimated other three components in the instantaneous surface energy balance equation, SEBAL calculates LE as the rest term. A conceptual scheme of the process after (Bastiaanssen et al. 1998) is given below.

Figure 5-2 Conceptual scheme of SEBAL showing principal components Ancillary data requirements In addition to satellite images, the SEBAL model requires the following routine weather data parameters: Wind speed Humidity Solar radiation Air Temperature

Visible Near infrared Thermal infrared

Surface albedo Vegetation index Surface temperature

Conversion

Net radiation Soil heat flux Sensible heat flux Latent heat flux

Bowen-ratio Evaporation fraction Priestley & Taylor coefficient. Surface resistance

Satellite radiance

Land surface parameterization

Surface parameters

Surface energy balance

Moisture indicator

SEBAL


52

5.4.1. Estimation of Surface parameters

The pre-processed ASTER images (7 imageries) between the periods of 14th February 2002 to 15th April 2004 have been used to derive basic inputs to the SEBAL model. In addition to the satellite images, routine weather data observed in the Hupselse KNMI weather station have been used to support the model. Image processing was carried out entirely on ILWIS 3.2 software using ILWIS script language and the complete script indicating different computational steps is given in appendix-E. Estimation of surface parameters and the land surface parameterization procedure will be discussed briefly in the next sections. Broadband surface albedo Broadband surface albedo was computed from surface reflectance values of ASTER band 1 to 9 obtained after necessary pre-processing. The narrowband to broadband conversion was done based on the following formula suggested for ASTER sensors in the algorithms for narrow-band to broad-band conversion of land surface albedo (Liang 2000).

0015.0367.0305.0551.0324.0335.0484.0 986531 −−++−+= αααααααbb (5.02)

Where; αbb = broadband surface albedo αi (i = 1 to 9)- surface reflectance of the corresponding ASTER band Surface temperature and broadband emissivity Since the surface temperature and narrowband emissivity was determined from the TES procedure it was only needed to estimate the broad band emissivity for each image. According to the studies conducted by John Hopkins University Spectral library (Ogawa 2002) and MODIS UCSB (University of California Santa Barbara) emissivity library the ASTER broad band emissivity is defined as:

�=

=− +=

14

100.143.3

ch

chchch ca εε (5.03)

Where εch is the narrow band emissivity and ach and C are constants given in Table 5-1. Table 5-1 Coefficient for converting narrowband to broadband emissivity

a10 a11 a12 a13 a14 c 0.014 0.145 0.241 0.467 0.004 0.128


53

5.4.2. Bio Physical parameter estimation

In order to estimate the bio-physical parameters required for the SEBAL algorithm, a statistical method (Parodi 2002) was adopted. The bio-physical parameters have been defined using following vegetation indices, primarily calculated using the band reflectance of Red (ASTER band 2) and NIR (ASTER band 3n). NDVI – Normalized Difference Vegetation Index WDVI – Weighted Difference Vegetation Index SAVI – Soil Adjusted Vegetation Index

Figure 5-3 Calculation process of different vegetation indices Soil line (γ) concept

Figure 5-4 Feature space plot of NIR and RED with showing the soil line

Band reflectance of RED and NIR

NDVI

L

Soil Line (γ) WDVI

SAVI


54

The feature space plot of reflectance values of NIR band vs. Red band shows a distinctive line which represents the bare soil in the image and termed as the soil line. The equation of the soil line can be expressed as

βγρρ += REDNIR (5.04)

Usually γ is close to unity and β lies in the range from -0.1 to 0.1. The soil line extends from darker soils with low Red and NIR image intensity to upper region of brighter soils with high values of Red and NIR image intensity. The iso-vegetation lines are said to be parallel to the soil line. The points which lie closer to the soil line are partially vegetated whereas the points which stay further away from the line are purely vegetated.

RNIRRNIR

NDVI+−= (5.05)

RNIRWDVI ⋅−= γ (5.06)

WDVINDVIaL ⋅⋅−= 21 (5.07)

where a = 0.5

( )( )LRNIRRNIRL

SAVI++−+= 1

(5.08)

Following bio-physical parameters were computed based upon the statistical relationships to the vegetation indices computed above. 1) Leaf Area Index (LAI)

2

1

CCSAVI

LAI−= (5.09)

where C1=0.13 and C2=0.35 (Bastiaanssen 1998)

2) Surface roughness for momentum transport (Zom)

)( 21 NDVICCExpZom += (5.10)

where C1= -5.5 and C2= 5.8 (Bastiaanssen 1998) 3) Displacement height (d)

��

��

−−=−

LAICe

hdLAIC

1

111 (5.11)

Where C1= 20.6 (Bastiaanssen 1998)


55

5.4.3. Estimation of energy balance components

Net Radiation (Rn) Net Radiation is defined as the electromagnetic balance of all incoming and outgoing fluxes reaching and leaving a flat horizontal and homogeneous surface as:

↑−↓+↑−↓= LLKKRn (5.12)

Where K means short-wave radiation (0.3 – 3µm) and L expresses the long-wave radiation (3 -

100µm). The arrows show the direction of flux entering (↓ ) or leaving (↑ ) the system. Hence the equation can also be described as :

netnetn LKR += (5.13)

Net short-wave component of the net radiation term can be given in the simplified form by:

↓−= KK net )1( α (5.14)

Where, α is the surface albedo and ↓K is the incoming shortwave radiation or global radiation which has to be measured by means of pyranometers. Net Long-wave radiation can be expressed as

insoutinnet LLLL )1( ε−−−= (5.15)

The out going long-wave radiation (Lout) is determined by Stefan-Boltzmann’s law as a function of surface temperature and emissivity, while incoming long-wave radiation (Lin) is determined from the air temperature and emissivity. Complete sets of equations are explained in FAO-56 publication (Allen et al. 1998).

4ssout TL σε= (5.16)

where εs and Ts are surface emissivity and temperature respectively. The constant c is the Stefan-Boltzmann’s constant which is equal to 5.67x 10-8 Wm-2K-4 and

4aain TL σε= (5.17)

where εa is the atmospheric emissivity and Ta is the air temperature at the measuring height. With all the parameters described above the final expression for Rn then becomes:

444 )1()1( ssaasaan TTTKR σεσεεσεα −−−+↓−= (5.18)


56

Soil Heat Flux (G) The evaluation of G is usually presented as a ratio G/Rn. The instantaneous G/Rn function depends on pixel size, location and time. Over a period of one day the integrated soil heat flux is usually considered as negligible. Different empirical studies have shown that the day time ratio G/Rn is related to the amount of vegetation present.(De Bruin et al. 1982, Jackson et al 1987). The empirical equation for G/Rn (Bastiaanssen et al. 1998) was used to estimate Soil Heat Flux .

)98.01)(62.032.0(100

)15.273( 420'

0'

0

0 NDVIrrr

TRG

n

−+−= (5.19)

Where r’

0 is the average albedo (approx. 1.1r0) when the soil heat flux is directly downward. T0 is the surface temperature in Kelvin. Sensible Heat Flux (H) The sensible heat flux (H) is the flow of energy through air as a result of the temperature gradient. Since the surface temperature during the day is usually much higher than the air temperature, the sensible heat flux is normally directed upwards during the day. During the night the situation is reversed. Close to the surface the sensible heat transport takes place mostly by diffusive processes, whereas at some distance away from the surface, turbulent transport becomes dominant. The mathematical formulation of the sensible heat flux is based on the theory of mass transport of heat and momentum between the surface and the near surface environment. The expression can be written as:

ah

apa r

TTCH

−= 0ρ (5.20)

Where: H is the Sensible heat flux (Wm-2) ρa is the density of the moist air (kgm-3) Cp is the air specific heat at constant pressure (Jkg-1K-1) rah is the aerodynamic resistance to heat transport between the reference and the surface level (sm-1) The aerodynamic resistance (rah) is expressed as follows.

�

��

−

−= h

oh

refah z

dz

kur ψ)ln(

1

*

(5.21)

k is the Von Karman’s constant and taken as 0.41 u* is friction velocity (ms-1) ψh is the Monin Obukhov stability correction zref is the upper integration limit for eddy diffusivity for heat transport (m) zoh is the surface roughness length to heat transport (m)


57

By combining the equations 5.20 and 5.21 following expression can be obtained.

�

��

−��

��

� −=− h

oh

ref

paa Z

dZ

uCkH

TT ψρ

ln*

0 (5.22)

The SEBAL use the extreme pixels of the image called dry pixel and the wet pixel to develop a relationship between surface temperature (T0) and the difference between (T0 - Ta) given in the form of;

00 TbadtTT a ×+==− (5.23)

Where ‘a’ and ‘b’ are constants and once they are determined for each and every pixel the “dt” is expressed using the surface temperature. Selection of wet pixel and dry pixel is done based on the temperature-albedo and the NDVI-albedo relationship in a particular image. Usually a pixel with low temperature and high NDVI is selected as the wet pixel and a pixel with low albedo, low NDVI and high temperature is selected as the dry pixel. For the wet pixel it is assumed that the sensible heat flux is zero and therefore according to (5.23) dt is equal to zero. For the dry pixel the condition dt = dtmax

and the sensible heat flux was put the maximum value of the difference between net radiation and soil heat flux.

ahpa r

dtCH max

max ρ= (5.24)

Figure 5-5 Solving for constants using wet and dry pixels

T0dry T0wet

dt= a +bT0

dtmin=0

dtmax

dt

T0


58

In order to determine constants ‘a’ and ‘b’ the term ‘rah’ has to be determined. Determination of ‘rah’ which is an implicit function of sensible heat flux is the most complicated issue in the whole energy balance procedure. The term ‘rah’ varies with the intensity and direction of H itself and other variables such as wind speed. Determination of ‘rah’ can only be done by solving the set of equations iteratively. Once the wet and dry pixels are determined the values of T0-wet, T0-dry, Rn-dry, G0-dry, Zom-dry and dh-dry, are obtained from the corresponding maps. The suffixes dry and wet represent the pixel value of the dry and wet pixels respectively. The values of the wind speed (Ub) at blending height and the surface roughness for heat transport (zoh) are used in the calculation.

Figure 5-6 Iterative process to calculate sensible heat flux The iterative procedure followed in order to obtain the map of sensible heat flux is given in Figure 5-6. Usually the loop was repeated more than six times to arrive at the final map of sensible heat flux. A detailed description of the theoretical background of turbulent transport and the behaviour of the atmospheric boundary layer and Monin-Obukhov Similarity Theory (MOST) can be found in literature. (Gieske 2003).

START

THEN ELSE

Calculate rah

Calculate L

T0

a & b Calculate U*

Calculate ϕh and ϕm

dt

Calculate H

IF H n+1= Hn Final H

STOP


59

Instantaneous Latent Energy Flux Having estimated all the other components in the energy balance equation, the Latent Energy flux (LE) was calculated for each pixel of the image as a 'residual' term.

HGRLE n −−= (5.25)

All the components in the equations are instantaneous values and expressed in units of Wm-2

.

Evaporative Fraction and daily evapotranspiration The ratio of latent energy to the available energy (Rn-G) is defined as the evaporative fraction (Λ).

GRLE

ninst −

=Λ (5.26)

The evaporative fraction is based on the instantaneous surface energy fluxes and is calculated as expressed in the equation given below.

GRHGR

n

ninst −

−−=Λ (5.27)

Assuming that the daily value of evaporative fraction is approximately equal to the instantaneous value, the daily value of latent energy flux (LE24) was calculated in the following manner.

When 24Λ≈Λ inst

)()( 0240242424 GRGRLE ninstn −Λ=−Λ= (5.28)

Where Rn24 is the 24 hours net radiation and the 24 hour value of soil heat flux (G0) is usually ignored in this equation for simplicity. Hence the expression for the daily evapotranspiration (ET24) can be expressed as

v

ninst

v

RLEET

λλ2424

24

Λ== (5.29)

Where the term λv is the latent heat of vaporization given by

tv310361.2501.2 −×−=λ (5.30)

When t is in 0C and λλλλv in MJkg-1

The 24 hours net radiation was determined by the following relationship given below.

242424024 110)1( swaswn KrR ττ −↓××−= (5.31)

Where; Ka24↓ is the 24-hour incoming extraterrestrial solar radiation, r0 is the surface albedo and τsw24

is the daily atmospheric transmittance.


60

5.5. Simplified Surface Energy Balance Index (S-SEBI)

Simplified Surface Energy balance Index (S-SEBI) is a recently developed, relatively simple technique to derive surface energy fluxes from remote sensing measurements (Roerink et al. 2000). The model has been already tested and validated with the in-situ flux measurements (Jin et al. 2005). Basically the model determines a reflectance dependant maximum temperature for dry conditions and reflectance dependant minimum temperature for wet conditions; thereafter the sensible and latent heat fluxes are partitioned according to the actual surface temperature. The major advantages of this particular technique over other remote sensing flux algorithms are stated as follows.

• No additional meteorological data is needed to calculate the fluxes if the surface hydrological extremes are present.

• The extreme temperatures for the wet and dry conditions vary with changing reflectance values, where the other methods try to determine a fixed temperature for wet and dry conditions for the whole image or for each land use class.

5.5.1. S-SEBI algorithm

Similar to the SEBAL method S-SEBI too has been developed to solve the surface energy balance with remote sensing techniques on a pixel by pixel basis. The basic remote sensing inputs required by S-SEBI are spectral information in the visible, near infrared and thermal infrared range to determine its constitutive parameters: surface reflectance, surface temperature and vegetation index. The model uses these input parameters to determine the energy budget at the surface. Similar to the previous explanation under SEBAL model, Net Radiation term (Rn) was calculated as the rest term of all incoming and outgoing shortwave and longwave radiation (see equation 5.18), some of which can be detected directly by remote sensing techniques. Also the soil heat flux was derived with the empirical relationship of the vegetation and surface characteristics (see equation 5.19). The main difference of S-SEBI from SEBAL lies in the calculation of sensible heat and latent heat flux components. Unlike the SEBAL procedure in S-SEBI algorithm the sensible flux and latent flux are not calculated as separate parameters, but as the evaporative fraction (Λ) a term which has been explained earlier.

Calculation of evaporative fraction (Λ)Λ)Λ)Λ) The correlation between surface temperature and reflectance of areas with constant atmospheric forcing has been investigated by researchers in the recent past and it has been observed that the relationship can be applied to determine the effective land surface properties (Bastiaanssen 1995; Menenti et al. 1989) It has been observed that at low reflectance, surface temperature is more or less constant with increasing reflectance. This phenomenon is true for water saturated surfaces like open water and irrigated lands, where all available energy is used for the evaporation process. Up a certain point the surface temperature starts rising with increasing reflectance. This is termed as “evaporation controlled” temperature. Beyond a certain threshold value of reflectance, surface temperature decreases with increasing reflectance. Here the temperature is said to be “radiation controlled”. (see Figure 5-7)


61

Figure 5-7 Schematic representation of temperature – reflectance relationship The calculation of evaporative fraction for each pixel with reflectance r0 and surface temperature T0

was carried out in the following manner.

• Determine the reflectance dependant temperature TλE, where λEmax(r0) = Rn - G and H = 0 • Determine the reflectance dependant Temperature TH , where Hmax(r0) = Rn - G and λE = 0

The evaporative fraction (Λ) for the particular pixel is calculated as the ratio of:

EH

H

TTTT

λ−−=Λ 0 (5.32)

Figure 5-8 Feature space plot of reflectance and temperature with Hmax and LEmax lines

r0

TλE

T0

TH

λEmax(r0)

Hmax(r0)

Evaporation controlled

Surface reflectance

Radiation controlled

Surf

ace

Tem

pera

ture


62

The Figure 5-8 shows the feature space plot of the surface reflectance and the surface temperature together with the mean surface temperature per reflectance unit. In the triangular shape feature space plot it is possible to recognize the increasing lower limit with a mild slope, where maximum latent heat flux λEmax(r0) is assumed and, the decreasing upper limit with a steep slope, where maximum sensible heat flux Hmax(r0) is assumed. The linear equations representing the upper and lower limits can be written as;

0rbaT HHH += (5.33)

and

0rbaT EEE λλλ += (5.34)

The regression variables a and b are site and time specific. Substituting the observed relationship for TH and TλE the evaporative fraction defined above can be expressed as:

0

00

)( rbbaaTrba

EHEH

HH

λλ −+−−+=Λ (5.36)

Once the Λ is determined, Latent Heat Flux and Sensible Heat Flux were calculated as given below.

)( GRLE n −Λ= (5.37)

))(1( GRH n −Λ−= (5.38)

Eventually the daily actual evapotranspiration was calculated from the equations 5.28 to 5.31 making the same assumptions as in SEBAL method.


63

6. Analysis of results and discussion

Although this chapter intends to compare the results obtained from ground based techniques and remote sensing based techniques, the temporal scale and the spatial scale of the results are essentially different for each group of methods. As already indicated the results of ground based methods are point estimates for longer period where as the remote sensing methods have provided results covering a large area but over a short time span. Hence firstly a comparison was made between results obtained from the different ground based methods covering a large time span. Secondly the results from remote sensing based methods were compared giving due consideration to the temporal variation as well as the spatial variation at a particular instance. Finally an overall comparison of results from both groups of methods was made and thereby attempted to answer the research questions through the discussion. The Table 6-1 shows the temporal and the spatial coverage of results obtained for different methods. Table 6-1 Different estimation methods and their temporal and spatial coverage

Group Method Temporal coverage Spatial coverage Penman-Monteith Daily from Jan. 2002 to Oct. 2004 Point estimation Modified Makkink Daily from Jan. 2002 to Oct. 2004 Point estimation Ground based Scintillation 14 days between Jan. 2002 to Oct. 2004 Path ave. estimation SEBAL 7 days between Jan. 2002 to Oct. 2004 Areal estimation Remote

sensing based S-SEBI 7 days between Jan. 2002 to Oct. 2004 Areal estimation

6.1. Comparison of results from ground based methods.

Basically three ground based methods were used to compute evapotranspiration using the meteorological data and flux measurements obtained from both Hupselse and Haarweg stations. The Penman-Monteith equation given in the FAO 56 (Allen et al. 1998) and the modified Makkink formula (De Bruin and Lablans 1998) have been used to estimate the daily grass reference evapotranspiration for the period from February 2002 to October 2004. Although the actual field measurements of scintillation method at the Hupselse were confined only to 7 days from 30th September 2004 to 6th October 2004, the extrapolated values of sensible heat flux based on the measurements done at Haarweg station on the image dates finally made, 14 days of actual evapotranspiration results available for the Hupselse catchment. The equations and the meterologicl variables used to calculate refernce evapotranspiration are given in appendix-D


64

The results obtained for grass reference evapotranspiration calculated for the Hupselse from January 2002 to October 2004 with the Penman-Monteith equation and the modified Makkink’s formula using daily data recorded at Hupselse KNMI station are presented in Figure 6-1.

Comparison of Daily ETref at Hupselse from 2002-2004

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Jan-

02

Mar

-02

May

-02

Jul-0

2

Sep

-02

Nov

-02

Jan-

03

Mar

-03

May

-03

Jul-0

3

Sep

-03

Nov

-03

Jan-

04

Mar

-04

May

-04

Jul-0

4

Sep

-04

Nov

-04

Time (Months)

ET

(mm

per

day

)

Penman-Monteith Makkink

Figure 6-1 Time series of daily reference evapotranspiration (2002-2004) The Figure 6-1 illustrates that results from both the methods are reasonably matching although the values are deviating a bit during the summer periods (June to August) and under wet conditions during winter months (November to January). It is observed that during summer period Penman-Monteith values are higher than the Makkink results. Also for the winter period the Penman-Monteith results are systematically low compared to Makkink results for the same period.

Penman-Monteith vs Makkink for June to August

y = 1.5109x - 1.0136R2 = 0.9462

0

1

2

3

4

5

0 1 2 3 4 5Makkink (mmd-1)

Pen

man

_Mon

(mm

d-1)

Figure 6-2 Comparison of reference ET for June to August


65

Penman-Monteith vs Makkink for November to January

y = 0.92x - 0.0818R2 = 0.8304

0.0

0.5

1.0

1.5

2.0

0.00 0.50 1.00 1.50 2.00

Makkink (mmd-1)

Pen

man

-Mon

(mm

d-1)

Figure 6-3 Comparison of reference ET for November to January The Figure 6-2 & 6-3 presenting the results from two methods for summer and winter periods illustrate that, during the summer the ET estimates from Penman-Monteith gives high values whereas the winter time values are low compared to Makkink results. Also the correlation between two method is better during summer time (R2=0.95) than the correlation observed for winter period (R2=0.83).

ETref - Penman-Montieth vs Makkinky = 0.22x2 + 0.54x + 0.01

R2 = 0.97

0.00

1.00

2.00

3.00

4.00

5.00

0.00 1.00 2.00 3.00 4.00

ETref -Makkink - (mmday-1)

ET r

ef -

Pen

-Mon

(m

md

ay-1

)

Figure 6-4 Comparison of reference ET for the entire period The Figure 6-4 illustrates the comparison of decadal averages of daily results obtained for reference evapotranspiration using the two methods for the entire period. A fairly high correlation was observed (R2 = 0.97) when a second order polynomial function was used to fit the trend line. The resulted quadratic equation of the trend line is given below.


66

01.054.022.0 2 ++= XXY (6.01) Where X is the decadal average value of daily ET0 by Makkink’s equation and Y is the same obtained from Penman-Monteith equation.

6.1.1. Comparison of actual evapotranspiration (AET) by scintillation method

Comparison of Sensible Heat Flux at Hupselse and Haarweg In order to make a rigorous comparison between the results of actual ET from ground based methods and the RS based methods the basic requirement was the data for a common period. In the proposed methodology it was suggested to compare the image based actual ET values with the actual ET computed from ground based scintillation data. Although the scintillation measurements were available at Hupselse from 30th September 2004 to 6th October 2004, none of the images were falling during the said period. Also no scintillation measurements were available at Hupselse on any of the image dates. On the other hand the scintillation measurements are available from the Haarweg station from January 2001 onwards. Since the Haarweg measurements are also done over grass using a LAS equipments and most of other meteorological parameters being comparable to Hupselse values the option of extrapolating Sensible Heat Flux calculated from Haarweg data to Hupselse was considered.

Comparison of Sensible Heat Flux y = 0.4288x + 4.1091R2 = 0.7641

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

-100 -50 0 50 100 150

Haarweg (SHF) Wm-2

Hup

sels

e (S

HF)

Wm

-2

Figure 6-5 Comparison of Sensible Heat Flux values at Hupselse and Haarweg


67

The Figure 6-5 represents the hourly averaged values of Sensible Heat Flux calculated for Haarweg and Hupselse stations from 30th September to 6th October in 2004. The following linear relationship could be obtained with a correlation around 87% (R2 = 0.76) for the Sensible Heat Flux values of two stations.

1091.44288.0 +×= HaarwegHupselse SHFSHF (6.02)

Where SHF is hourly averages of Sensible Heat Flux measured in units of Wm-2

Calculation of actual evapotranspiration at Hupselse Calculation of actual ET at Hupselse was carried out by applying the energy balance equation with hourly averaged flux data. During the field work period measured values were available for all the components in the energy balance equations. The data for the image dates was obtained by both direct measurements and using the relationship of equation (6.02) with values measured at Haarweg station. The Table 6-2 indicates the method of obtaining the different flux components of the energy balance equation for calculating actual ET on Image dates. Table 6-2 Acquisition of different energy flux components

Energy flux component Acquisition Net Radiation (Rn) Direct measurements Sensible Heat Flux (H) Extrapolation Soil Heat Flux (G) Direct measurements Latent Heat Flux (LE) Residual

Figure 6-6 Estimation of Actual ET at Hupselse from Scintillation method

Field work period On Image dates

Extrapolated (H) to Hupselse

Measured (H) at Haarweg

(LE) calculated at Hupselse

Measured (H) at Hupselse

Act. ET at Hupselse

Measured (Rn & G) at Hupselse

Scintillometer at Hupselse


68

Table 6-3 Daily evapotranspiration from different ground based methods

Date AET-Hupselse

Direct/Extrapolated (mm.day-1)

AET- for Haarweg SHF

(mm.day-1)

ET0-Penman-Monteith

(mm.day-1)

ET0 -Makkink (mm.day-1)

14-02-2002 0.98 0.50 1.00 1.02 16-08-2002 4.25 3.90 3.80 4.10 09-08-2002 2.96 2.68 3.20 2.70 08-05-2003 4.16 3.68 3.40 3.40 31-05-2003 5.17 5.12 4.40 4.50 03-08-2003 4.75 4.36 4.00 4.60 15-04-2004 3.63 3.17 3.10 2.90 30-09-2004 1.35 1.38 1.31 1.52 01-10-2004 0.96 1.03 0.63 0.85 02-10-2004 1.95 1.68 1.50 1.57 03-10-2004 1.63 1.57 1.15 1.50 04-10-2004 1.38 1.50 1.50 1.42 05-10-2004 0.74 0.89 0.57 0.61 06-10-2004 1.52 1.50 1.37 1.43 The Table 6-3 summarizes the daily values of actual evapotranspiration calculated as described above for the concerned period. Also the actual evapotranspiration calculated directly using the Sensible Heat Flux at Haarweg station and the reference evapotranspiration values obtained by both the Penman-Monteith and Makkink equation are shown in the adjoining columns for comparison.

ET calculated on image dates by ground based methods

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Feb-

02

Apr

-02

Jun-

02

Aug

-02

Oct

-02

Dec

-02

Feb-

03

Apr

-03

Jun-

03

Aug

-03

Oct

-03

Dec

-03

Feb-

04

Apr

-04

Month of Year

ET

(mm

day-1

)

AET extrapolated AET for Haarweg ET0- Penman-Mon ET0-Makkink

Figure 6-7 Daily ET calculated on Image dates from ground based methods (see Table 6-3)


69

ET calculated on field work dates by ground based methods

0.0

0.5

1.0

1.5

2.0

2.5

30-S

ep-0

4

01-O

ct-0

4

02-O

ct-0

4

03-O

ct-0

4

04-O

ct-0

4

05-O

ct-0

4

06-O

ct-0

4

Date

ET

(mm

day-1

)AET-measured AET for Haarweg ET0- Penman-Mon ET0-Makkink

Figure 6-8 Daily ET calculated for field work period by ground based methods (see Table 6-3) The Figure 6-7 & 6-8 show the daily averages of evapotranspiration from different ground based methods on dates with satellite images and the dates on which field measurements were carried out. Both the charts reveal that actual values obtained from scintillation method are more often in the same range of the daily estimations of reference values obtained by two meteorological methods. The actual values are appeared to be slightly higher than the reference values especially on dates falling between the crop growing period (May to September), whereas the actual values and reference values of evapotranspiration are closer to each other during rest of the period. Also the actual ET values were compared against the reference ET values obtained with each of the meteorological methods and presented in scatter plots. The comparison of actual ET against the reference ET values of Makkink method shows that there is a strong linear relationship with an excellent correlation (R2=0.98) between the results. The same strong correlation was also found with the actual ET estimates and the results of Penman-Monteith method illustrated in the next scatter plot. Again the R2 was around 0.97 slightly lower than the previous figure obtained for Makkink. Both the relationships illustrate that the actual figure of daily evapotranspiration is closer to 1.1 times the reference figure on average, as the intercept in the linear equation is negligible.


70

Figure 6-9 Actual ET from scintillation against reference ET from Makkink

Actual ET vs ET0 from Penmany = 1.1335x + 0.0287

R2 = 0.9685

0

1

2

3

4

5

6

0 1 2 3 4 5 6

ET0 by Penman (mmd-1)

AE

T by

Sci

ntill

atio

n (m

md-1

)

Figure 6-10 Actual ET from scintillation against the reference ET from Penman-Monteith

Actual ET vs ET0 from Makkinky = 1.115x - 0.0263

R2 = 0.9752

0

1

2

3

4

5

6

0 1 2 3 4 5 6

ET0 by Makkink (mmd-1)

AE

T by

Sci

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atio

n (m

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6.2. Comparison of temporal variation of RS based Actual ET (AET)

The comparison of daily values of catchment averaged AET from remote sensing methods was carried out both temporally and spatially. Initially a comparison was made between the basin scale averaged values obtained from two energy balance models against each other. Then the daily results of each method were compared with the corresponding results of scintillation method. Finally an overall comparison was made with all the results obtained using different methods employed in the study. The Table 6-4 presents the daily values of actual evapotranspiration estimated from SEBAL, S-SEBI and scintillation methods. Table 6-4 Daily actual evapotranspiration from different methods

Date Daily AET from

SEBAL (mmd-1)

Daily AET from S-SEBI (mmd-1)

Daily AET from Scintillation method

(mmd-1) 14-02-2002 0.06 0.07 0.98 16-08-2002 3.81 3.22 4.25 09-08-2002 1.83 1.54 2.96 08-05-2003 3.55 3.28 4.16 31-05-2003 3.86 3.55 5.17 03-08-2003 4.38 4.1 4.75 15-04-2004 3.12 2.08 3.63

6.2.1. Comparison of catchment averaged AET values of SEBAL and S- SEBI

Results of AET SEBI vs SEBAL y = 0.9082x - 0.1256

R2 = 0.9532

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I (m

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Figure 6-11 Comparison of Actual ET from S-SEBI against SEBAL (dates see Table 6-4) The Figure 6-11 for scatter plot of daily values of S-SEBI against the SEBAL reveals that catchment averaged daily actual evapotranspiration values obtained by remotely sensed energy balance models are strongly correlated. (R2=0.95)


72

6.2.2. Comparison of AET from Energy Balance Models and Scintillation method

Results of AET - SEBAL vs Scintillation y = 1.0456x - 0.9267

R2 = 0.9424

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Figure 6-12 Comparison of actual ET from SEBAL against the scintillation (dates see Table 6-4)

Results of AET - S-SEBI vs Scintillation y = 0.9659x - 1.0275

R2 = 0.9295

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Figure 6-13 Comparison of actual ET from S-SEBI against the scintillation (dates see Table 6-4) The Figure 6-12 & Figure 6-13 disclose that, the results of two energy balance methods can be linearly correlated to the results obtained by scintillation method. Also on both occasions the regression line of least square fit clearly shows that the scintillation results are systematically high compared to results of the remote sensing methods.


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6.2.3. Comparison of daily ET estimated from 5 different methods

In order to compare the results of all five methods employed in this analysis, a time series was prepared from January 2002 to November 2004 presenting all the estimated values. The Figure 6-14 and Figure 6-15 illustrate the daily ET values and the decadal mean values of daily ET respectively.

Daily ET at Hupselse Beek from different methods (2002-2004)

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Figure 6-14 Time series of daily evapotranspiration at Hupselse (2002-2004)

Decadal Mean of Daily ET at Hupselse Beek (2002-2004)

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Figure 6-15 Time series of decadal mean of daily evapotranspiration at Hupselse (2002-2004)


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6.3. Comparison of spatial distribution of actual evapotranspiration

6.3.1. Spatial Variation over the entire catchment

Figure 6-16 Spatial variation of daily AET on 08-05-2003 The Figure 6-16 compares the spatial distribution of daily AET over the entire catchment on 08-05-2003. It is observed from the two raster maps that the spatial distribution is more or less similar for the results of both the SEBAL and S-SEBI methods. The following statistics were obtained from the frequency distribution diagrams displayed against the respective ET maps. The statistics of actual ET variation over the catchment on different image dates are given in the Table 6-5 for both the SEBAL and S-SEBI.

Mean = 3.55 Median = 3.76 Standard deviation = 0.86

Mean = 3.28 Median = 3.47 Standard deviation = 0.84


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Table 6-5 Statistics of spatial variation of actual ET over the catchment Actual ET from SEBAL (mmd-1) Actual ET from S-SEBI (mmd-1)

Date Mean Median Stand. dev. Mean Median Stand. dev.

14-02-2002 0.06 0.04 0.06 0.07 0.05 0.06 16-08-2002 3.81 3.89 0.69 3.22 3.30 0.63 09-08-2002 1.83 1.86 0.51 1.54 1.57 0.44 08-05-2003 3.55 3.76 0.86 3.28 3.47 0.84 31-05-2003 3.86 4.00 1.26 3.55 3.62 1.04 03-08-2003 4.38 4.45 0.90 4.10 4.14 0.73 15-04-2004 3.12 3.23 0.68 2.08 2.13 0.45

Variation of SEBAL-Actual ET over the catchment

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Figure 6-17 Spatial variation of actual ET from SEBAL

Variation of S-SEBI-Actual ET over the catchment

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Figure 6-18 Spatial variation of actual ET from S-SEBI


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6.3.2. Spatial variation of actual evapotranspiration in different Land Use classes

Figure 6-19 Spatial variation of SEBAL based actual ET in different Land use classes The Figure 6-19 compares the spatial variation of daily actual ET on 08-05-2003 within the three dominant land use classes in the basin Grass, Maize and Woodlands. The statistics are as follows. Table 6-6 Statistics of daily ET distribution in different land use classes

Land Use class Mean Median Standard deviation Grass 3.78 3.97 0.69 Maize 2.97 3.03 0.87 Woodlands 4.39 4.51 0.55


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Table 6-7 Numerical values of spatial variation of daily ET in different land use classes Ave. Actual ET from SEBAL (mmd-1) Ave. Actual ET from S-SEBI (mmd-1)

Date Grass Maize Woodlands Grass Maize Woodlands

14-02-2002 0.04 0.07 0.16 0.05 0.07 0.18 16-08-2002 3.68 3.94 4.47 3.14 3.34 3.66 09-08-2002 1.70 1.95 2.37 1.44 1.64 1.97 08-05-2003 3.78 2.97 4.39 3.58 2.63 4.05 31-05-2003 4.14 3.11 5.09 3.81 2.89 4.62 03-08-2003 4.10 4.72 5.49 3.87 4.38 4.97 15-04-2004 3.08 3.11 3.54 2.06 2.01 2.58

Average Actual ET from SEBAL per Land Use class

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Figure 6-20 Spatial variation of SEBAL based ET in different land use classes

Average Actual ET from S-SEBI per Land Use class

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Figure 6-21 Spatial variation of S-SEBI based ET in different land use classes


78

6.3.3. Comparison of single crop coefficient (Kc) for Maize

Figure 6-22 Comparison of Crop coefficient for Maize in different growing stages The single crop coefficient was calculated for Maize and Woods based on the average actual evapotranspiration of grass on the same image as given below. Kc = AET map of the land use class Average AET of Grass The growing season for Maize in the study area is usually from month of May to September. Figure 6-22 gives a comparison of two Kc maps calculated for maize crop, for the beginning of the growing season (upper map) and in the latter part of the same crop season (lower map). The statistics of the two raster maps for KC are given in the Table 6-8. Table 6-8 Statistics of distribution of Kc for Maize at two different stages

Date of Image Mean Median Standard deviation 31-05-2003 0.75 0.77 0.31 03-08-2003 1.15 1.21 0.21


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Table 6-9 Average crop coefficients calculated based on actual ET of Grass SEBAL S-SEBI Average

Date KC -Maize KC-Wood KC -Maize KC-Wood KC -Maize KC-Wood

14-02-2002 1.75 4.00 1.40 3.60 1.58 3.80 16-08-2002 1.07 1.21 1.06 1.17 1.07 1.19 08-09-2002 1.15 1.39 1.14 1.37 1.14 1.38 08-05-2003 0.79 1.16 0.73 1.13 0.76 1.15 31-05-2003 0.75 1.23 0.76 1.21 0.75 1.22 03-08-2003 1.15 1.34 1.13 1.28 1.14 1.31 15-04-2004 1.01 1.15 0.98 1.25 0.99 1.20 The Table 6-9 summarizes the average crop coefficients calculated for Maize and Wood on different dated as described earlier in the chapter. Also the Table 6-10 shows a comparison of calculated figures of single crop coefficients with decade mean Kc figures related to Makkink reference crop evapotranspiration for the growing period from May to September. The corresponding figures of 10 day averaged Kc values have been extracted from a comparison study carried out in the Netherlands in 1998 (De Bruin and Lablans 1998) Table 6-10 Single crop coefficient of Maize crop

May June July August September KC -Maize 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

Decade Mean 0.5 0.7 0.8 0.9 1.0 1.2 1.3 1.3 1.2 1.2 1.2 1.2 1.2 1.2 1.2

Calculated Av. 0.8 - 0.8 - - - - - - 1.1 1.1 - 1.1 - -

Comparison of single crop coefficient Kc for Maize

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Figure 6-23 Comparison of Kc for Maize from May 2003 to October 2003


80

6.4. Discussion

The evapotranspiration results of Hupselse Beek catchment estimated by different methods have been presented for comparison. The different methods used in the study are basically grouped in to two categories viz. ground based methods and remote sensing methods. The ground based methods used meteorological data and surface fluxes measurements whereas the remote sensing methods primarily used satellite images to estimate ET. The temporal analysis was carried out with the results of ground based methods where as the results of remote sensing methods were used to analyse the spatial variation. The following paragraphs summarize the results and discuss the observations made during the detailed analysis.

6.4.1. Summary of the results from ground based methods

The results of the two meteorological data methods Penman-Monteith and modified Makkink revealed that they are strongly correlated with a quadratic relationship. Also the analysis disclosed that the results are linearly correlated for summer time with a better correlation compared to the same obtained for the winter period. The linear relationships also confirmed that during the summer period the Penman Monteith values are generally higher than the Makkink values whereas during the winter the Penman-Monteith ET0 results are lower than the values estimated by modified Makkink method. It is noted that observed results are very much in agreement with the previous findings (De Bruin and Lablans 1998) from a comparison study between Penman-Monteith and modified Makkink equation which was carried out with meteorological data from different parts of the Netherlands. One of the facts mentioned in the discussion of the above study is that although the correlation between the ET0 results of the compared methods is high, there can be deviations especially under dry conditions. The paper also has concluded that use of the Penman-Monteith method under dry conditions will lead to an overestimation of ET because of the so called ‘Bouchet’ effect. This could have been the reason for the very high daily evaporation values observed (clearly visible in Figure 6-1) in summer months from Penman-Monteith method in Hupselse as well. Also during the winter time the Penman-Monteith equation owing to the form used here underestimates the grass reference ET (sometimes negative estimates) where as the Makkink method basically governed by the global radiation always yields a positive value of reference evapotranspiration. Under these circumstances the Makkink method seems to be more realistic in estimating reference ET even in extreme weather conditions. The comparison of hourly averaged Sensible Heat Flux values at Hupselse and Haarweg has shown a fairly good correlation between the results at two stations. Hence the proposal of extrapolating Sensible Heat Flux data at Haarweg to Hupselse can be justified. Also when compared with the reference ET values obtained from the other two ground based methods the daily actual ET values of scintillation method are within a reasonable range (maximum deviation is around 0.7 mm per day). A slightly high correlation is observed between the results of scintillation method and the Makkink method than the correlation between the scintillation method and the Penman-Monteith method. However it is essential to carryout further research to confirm the fact that the observed correlation for the sensible heat flux at Hupselse and Haarweg stations is valid for any given time instant. It should be noted that the above correlation was observed for few days of measurements made during month of October which is relatively wet and cold. The sensible heat flux calculated during this


81

period is found to be very low compared to the Net Radiation observed on the corresponding period. Hence it is advisable to repeat the experiment during the summer period to observe whether the same correlation could be obtained between the stations when the sensible heat fluxes are comparatively high. Also the differences in instrument calibrations, microclimatological differences and land surface cover differences of two stations should be examined through further studies.

6.4.2. Summary of the results from remote sensing methods

Basically the results of remote sensing methods have been used to analyse both temporal and spatial variation of actual evapotranspiration in the study area. The temporal analysis was carried out using the basin averaged daily values of actual ET obtained from SEBAL and S-SEBI methods. The initial comparison between the daily results obtained from SEBAL and S-SEBI has shown that they are strongly correlated with a linear relationship. The maximum deviation in individual catchment averaged daily values is found to be around 1 mm. It is also observed that both the SEBAL and S-SEBI methods are reasonably in agreement with the scintillation method although the results of latter method seem to be systematically high. The scatter plots displaying the linear correlation between the remote sensing methods and scintillation method revealed that, although the correlation is very high (R2 > 0.9 in both occasions) the values are biased towards the scintillation method. The time series analysis created using daily results from all five methods for the period between January 2002 and November 2004, is illustrated in Figure 6-14. It is observed that the daily variation is very much in agreement with the temporal variation of incoming solar radiation of the region. This is again clearly visible from the Figure 6-15, in which the noise has been reduced by using the decadal mean values (10 day averages) of daily ET values. By an overall comparison of results from different methods it is evident that all five methods have yielded results which are reasonably matched with each other. Further it can be noted that (except for one instance) the results of scintillation method as well as the remote sensing methods are harmonized well with temporal pattern exhibited by the more frequent meteorological methods.

6.4.3. Spatial distribution of AET over the catchment

The spatial variation of actual ET over the entire catchment was analysed using both the SEBAL and S-SEBI results. Table 6-5 summarizes the statistics for different dates by different methods showing the mean, median and standard deviation of actual ET over the catchment. It is observed that the maximum standard deviation is 1.3 mm per day from SEBAL method on 31-05-2003, whereas the corresponding S-SEBI value is around 1 mm per day over the entire catchment. Also it is evident from the mean values that more often SEBAL values are very close to the ET values obtained by S-SEBI.

6.4.4. Spatial distribution within the Land Use classes

The analysis of spatial variation in actual evapotranspiration between different land use classes present in the catchment has revealed that the average ET over different land use classes are substantially different from each other. The numerical values presented in Table 6-7 shows that the woodlands has the highest average evaporation recorded for all the dates. The grass lands have exhibited the lowest value of average ET except for the images acquired in May where the Maize area


82

has exhibited the lowest figures. It is clearly visible from Figures 6-20 & 6-21 that results from both SEBAL and S-SEBI have shown exactly the same pattern as far as the spatial and temporal variations are concerned. The maximum difference of 2 mm in average daily AET was observed on 31-05-2003 between the wooded area and the Maize area. The consistency of average ET from wooded area is obvious since the vegetation is more or less uniform through out the period. The apparent fluctuations of average actual ET in the Maize area could be due to the change in crop growth stages over the period of time.

6.4.5. Crop coefficients based on AET of grass

Single crop coefficients for Maize and Woods were calculated with respect to average actual evapotranspiration rate of grass on different dates. The summary of the average Kc values for maize and Wood is given in Table 6-9. The table shows that Kc values of woodlands are fairly consistent through out the period varying between 1.2 and 1.4 on average. In contrast the average Kc values of maize are varying from 0.75 in May to 1.15 in the months of August and September. Also Table 6-8 comparing the statistics of two images acquired in months of May and August in 2003 shows that on a particular day the variation of Kc within the Maize area is minimal. In order to verify the single crop coefficients of maize calculated for different periods, they were compared with the values found with (De Bruin and Lablans 1998). The Kc values given in this paper are based on the Makkink reference evapotranspiration for the growing season from May to September for Dutch conditions .The Table 6-10 demonstrates the decade mean values of daily Kc for Maize extracted from above literature against the values calculated for the growing period from May to September. The Figure 6-23 shows that there is a reasonable match between the calculated values and the values obtained from the literature for the comparison.


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7. Conclusions and recommendations.

7.1. Conclusions

Spatially distributed actual evapotranspiration was estimated from February 2002 to April 2004 in Hupselse Beek catchment based on remotely sensed data coupled with meteorological variables and ground based surface fluxes. Both the ground based techniques as well as the remote sensing based methods have been used to estimate the evapotranspiration of the catchment, not only for mere comparison but also to analyse the spatio-temporal variation properly. One of the important features of the study is the use of high resolution TERRA- ASTER images to derive the land surface parameters required by the remote sensing based surface energy balance methods. It has been verified in past studies that the ASTER thermal images can be used to estimate surface temperature & surface emissivity accurately by applying the Temperature Emissivity Separation (TES) algorithm coupled with MODTRAN 4 to estimate atmospheric radiance corrections. Hence the application of the above algorithm to estimate temperature and emissivity using ASTER images in this study would have improved the quality of the final results based on energy balance models. The other important feature is the application of SMAC software for atmospheric corrections in visible and NIR bands. Both these features would have contributed towards better estimation of land surface parameters eliminating the simple internal calibrations usually adopted in remote sensing models when using data from other sensors such as NOAA-AVHRR, MODIS or LANDSAT series. As far as the ground based methods are concerned it can be concluded that, out of the two meteorological station methods used to calculate reference evapotranspiration, the modified Makkink equation produced better estimates than the more complex Penman Monteith equation especially under the climatic conditions the study has been carried out. Also it can be concluded that measurements of incoming solar radiation is crucial in estimating reference evapotranspiration by the modified Makkink method. However use of Penman-Monteith equation to estimate reference ET is important to carry out regional scale comparison studies since the Penman method is the generally accepted method all over the world. With respect to the results of meteorological data methods, it can also be concluded that the, application of scintillation method in the study has yielded promising results. Specially in the absence of direct scintillation measurements at Hupselse the extrapolation of Sensible Heat Flux measurements from Haarweg data has proved to be producing daily actual evapotranspiration estimates which are in close agreement with observed values. A conclusion can be drawn from the statistics of results from remote sensing methods that the two energy balance models produce reasonably close results for the spatially distributed actual evapotranspiration. Compared to the actual ET estimates of scintillation method SEBAL method seems to be estimating marginally better values than the S-SEBI method. But it is hard to conclude that SEBAL produces better estimate with respect to S-SEBI without any in situ scintillation measurements to estimate actual ET .


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The time series analysis of results from all the methods has shown that the spatially averaged actual evapotranspiration values are more often close to the grass reference evapotranspiration values. But the spatial distribution of the same has shown that the average value is far from the actual values at different locations with very high standard deviations recorded in the statistics. Hence it can be concluded that the spatially averaged values though matching with the values obtained by point based methods, do not accurately represent the spatial distribution. However according to the statistics it can be concluded that the two remote sensing methods used in the study are representing the spatial distribution more or less in a similar fashion. The comparison of spatial distribution in different land use classes has revealed that the spatially averaged evapotranspiration values in different classes are substantially different from each other. The example shown in the Figure 6-19 shows a difference of 1.5mm between the spatial means of maize and Woods. Also the standard deviation values have shown that actual evapotranspiration distribution in wooded area is fairly uniform compared to that of maize area. This could be possibly due to differences in crop growth stages of maize area at this particular time of year. Also the calculation of crop coefficients (Kc)on different dates based on relative actual evapotranspiration has shown that the average Kc in wooded area varies slightly around 1.25 where as the maize area is exhibiting a large variation from 0.75 to 1.15 on different dates. A conclusion can be made based on the above fact that the woods have a relatively high contribution to the evapotranspiration per unit area compared to the other land use types in the catchment. The results will be extremely useful when assessing the impact of land use changes on actual evapotranspiration in the catchment. The good match between the calculated Kc values for maize and the decade mean values obtained from literature for the growing period further strengthen the validity of the results from remote sensing methods.

7.2. Limitations

Non availability of adequate satellite images covering the study area with high temporal resolution is one of the main limitations encountered in applying the remote sensing methods. This was particularly due to presence of clouds in most of the images over the study area which is a typical problem for remote sensing applications. Also the difficulties in acquiring the exact profiles of atmospheric parameters like ozone and water vapour at the satellite overpass time, required for the atmospheric corrections can be mentioned as general limitations. The scintillation method has been identified as on of the most accurate methods for estimating sensible heat flux, which in turn can be used to compare the result of remote sensing methods. But the scintillometer experiment requires special technical skills and particularly the capacity to record and store huge amount of data for a long period. This is one of the reasons why in most occasions scintillation measurements are not available with standard meteorological stations. Likewise here even though most of the meteorological variables measured at Hupselse & Haarweg stations are found to be reasonably matching with each other, the seven days of scintillation observations are insufficient to derive a reliable correlation for the Sensible Heat Flux estimates between the two locations. This phenomenon has limited the reliability of using extrapolated data from Haarweg station to cross validate the remote sensing based ET results at Hupselse.


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7.3. Recommendations

The study could re-established the fact that, the application of remote sensing brings a significant contribution to asses the spatial variation of evapotranspiration over large areas for all the different land covers in a basin over the recent past. However the ground based methods are always important especially in verifying the results from remote sensing methods. Even though the extrapolated data from Haarweg was used as a solution for cross checking the remote sensing results, the reliability of ground based results needs to be improved. Continuation of scintillation measurements at Hupselse is vital in this regard. It is acknowledged that evapotranspiration is computed not for its own sake but for some other purpose and each method discussed in this study has its usefulness depending on its application. It has been emphasized that an accurate estimation of spatially distributed evapotranspiration is essential for basin scale water management. Whether it is for water resources allocation or irrigation management or impact assessment of land use changes, the remote sensing methods can be used effectively and produces superior results compared to ground based methods. Also the use of the meteorological station methods recommended by FAO has to be adopted to calculate crop water requirements especially at the planning stage of new irrigation schemes. It is obvious that the remote sensing techniques are impossible to adopt prior to the realization of a proposal. As mentioned earlier the Hupselse Beek catchment has been studied as an instrumental catchment over the years intending to formulate planning and management strategies in adjacent larger basins with similar characteristics. Hence both the ground based and remote sensing methods are recommended in the study area to estimate evapotranspiration offering much scope to the original intention. It should be noted that both SEBAL and S-SEBI are indicative type models. Hence the user induced error in results from both the models is inevitable. Application of more physically based models like Surface Energy Balance Systems (SEBS) and Two Source Energy Balance (TSEB) in the study area is important for validation of the results. Also it is certainly important to carryout a complete water balance for the catchment incorporating the available precipitation data, stream flow measurements and the ground water information in a future study which could offer an independent validation of the results.


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Appendices

Appendix A- Data collected at Hupselse from 29-09-2004 to 06-10-2004

List of Parameters

Parameter Notation Units Parameter Notation Units Incoming Solar radiation Kin Wm-2 Soil temp below 20 t1_20 0C Outgoing Solar radiation Kout Wm-2 Soil temp below 15 t2_15 0C Incoming Longwave rad Lin Wm-2 Soil temp below 10 t3_10 0C Outgoing Longwave rad Lout Wm-2 Soil temp below 5 t4_5 0C Net Radiation Rn Wm-2 Wind vel. at 4m u_high ms-1 Body Temp of NR pt100 0C Wind vel. at 2m u_low ms-1 Air Temp. at 4m thigh 0C Temp. of panel Pan_t 0C Air Temp. at 2m tlow 0C Battery voltage batvolt V Rel. Hum. At 4m RHhigh % Log of Cn

2 logCn2 V

Rel. Hum. At 2m RHlow % Demod. signal modq V Average wind vel. at 4m uavghigh ms-1 Variance of Cn

2 varCn2 V2

Wind dir. from North Winddir degree Variance of modq varmodq V2 Soil Heat Flux 1 shf1 Wm-2 Scaled Cn

2 puCn2

Soil Heat Flux 2 shf2 Wm-2 Soil Heat Flux 3 shf3 Wm-2 Table A -1

Date & time Kin Kout Lin Lout Rn pt100 thigh tlow 9/29/04 17:00 260.73 63.20 291.84 383.04 106.32 14.91 14.16 14.00 9/29/04 18:00 243.44 55.10 287.05 380.64 94.76 15.15 14.62 14.43 9/29/04 19:00 45.47 11.29 333.48 376.41 -8.75 13.83 13.68 13.36 9/29/04 20:00 -0.08 1.89 284.22 356.89 -74.65 11.50 12.09 11.35 9/29/04 21:00 -1.95 2.97 281.48 350.20 -73.64 9.74 11.19 10.52 9/29/04 22:00 -1.76 3.14 277.52 349.20 -76.58 9.20 10.50 9.73 9/29/04 23:00 -2.11 2.61 273.79 346.24 -77.18 8.39 9.44 8.61 9/30/04 0:00 -0.65 2.61 319.76 355.46 -38.97 8.63 9.52 9.11 9/30/04 1:00 -0.72 1.54 341.23 362.81 -23.84 9.48 9.89 9.66 9/30/04 2:00 -0.41 1.35 342.42 364.14 -23.48 9.79 10.10 9.90 9/30/04 3:00 -0.08 1.15 348.33 365.96 -18.86 10.06 10.30 10.12 9/30/04 4:00 -0.40 1.09 346.01 366.08 -21.56 10.18 10.44 10.25 9/30/04 5:00 -0.53 1.03 344.35 365.35 -22.56 10.09 10.33 10.11 9/30/04 6:00 -1.68 1.32 309.48 358.04 -51.56 9.58 10.00 9.74 9/30/04 7:00 -1.64 1.50 303.61 349.16 -48.68 8.19 8.75 8.20


88

Table A-1 contd. 9/30/04 8:00 2.84 2.22 313.33 350.68 -36.73 7.78 8.20 7.74 9/30/04 9:00 30.64 7.85 314.76 350.36 -12.82 7.48 8.00 7.65 9/30/04 10:00 153.47 15.40 320.29 361.00 97.36 9.86 10.41 9.87 9/30/04 11:00 361.72 63.61 285.62 383.31 200.42 15.96 14.74 13.75 9/30/04 12:00 485.05 108.93 312.58 401.01 287.69 18.87 15.64 15.19 9/30/04 13:00 456.34 97.72 305.12 402.76 260.99 19.21 16.40 16.14 9/30/04 14:00 510.02 112.17 315.21 407.37 305.68 19.63 16.69 16.52 9/30/04 15:00 368.85 80.93 324.94 403.09 209.77 18.73 16.29 16.25 9/30/04 16:00 161.62 35.07 336.83 392.13 71.24 16.57 15.53 15.38 9/30/04 17:00 144.91 33.84 358.40 393.09 76.38 16.08 15.39 15.24 9/30/04 18:00 55.91 13.73 364.12 387.91 18.39 15.22 14.93 14.79 9/30/04 19:00 17.10 6.59 369.64 383.17 -3.02 13.39 13.94 13.81 9/30/04 20:00 -0.76 2.12 371.04 379.13 -10.97 12.62 13.28 13.13 9/30/04 21:00 -0.88 2.42 370.95 377.99 -10.34 12.10 12.70 12.54 9/30/04 22:00 -0.73 1.86 371.17 377.97 -9.39 12.05 12.58 12.42 9/30/04 23:00 -0.51 1.93 371.87 378.00 -8.57 12.21 12.76 12.59 10/1/04 0:00 -0.31 1.57 368.95 376.81 -9.74 12.18 12.67 12.49 10/1/04 1:00 -0.22 1.50 366.90 375.21 -10.03 11.88 12.37 12.19 10/1/04 2:00 -1.02 1.97 366.56 375.34 -11.77 11.82 12.40 12.21 10/1/04 3:00 -0.38 1.55 368.28 376.05 -9.70 11.92 12.43 12.24 10/1/04 4:00 -0.16 1.09 367.08 375.32 -9.50 11.94 12.21 12.03 10/1/04 5:00 -0.33 1.09 362.90 373.54 -12.06 11.60 11.94 11.77 10/1/04 6:00 -0.73 0.96 358.70 373.17 -16.15 11.64 11.98 11.82 10/1/04 7:00 -1.30 1.81 338.26 364.27 -29.12 10.68 11.45 11.20 10/1/04 8:00 3.39 2.41 344.27 366.37 -21.13 10.54 11.15 10.95 10/1/04 9:00 43.47 10.81 349.26 372.55 9.37 11.03 11.41 11.24 10/1/04 10:00 122.72 23.32 329.59 378.79 50.19 12.58 12.65 12.45 10/1/04 11:00 318.25 61.93 310.92 389.31 177.93 15.71 14.67 14.36 10/1/04 12:00 347.84 74.99 326.26 402.14 196.98 17.71 16.18 16.19 10/1/04 13:00 176.34 38.76 385.40 400.82 122.16 17.08 15.96 15.91 10/1/04 14:00 112.53 25.43 384.84 398.06 73.88 16.45 15.67 15.63 10/1/04 15:00 123.92 27.82 384.70 399.21 81.59 16.67 15.91 15.86 10/1/04 16:00 72.57 16.06 383.74 395.44 44.81 16.29 15.76 15.65 10/1/04 17:00 63.24 14.27 382.43 396.05 35.35 16.13 15.68 15.61 10/1/04 18:00 69.35 16.54 365.60 395.20 23.20 15.99 15.77 15.73 10/1/04 19:00 35.25 8.04 306.53 380.46 -46.73 15.08 15.34 15.17 10/1/04 20:00 -0.84 1.44 323.65 370.55 -49.18 13.21 13.96 13.58 10/1/04 21:00 -1.30 1.88 329.63 367.66 -41.21 12.25 13.25 12.79 10/1/04 22:00 -1.30 1.52 344.34 372.95 -31.43 12.39 13.24 12.86 10/1/04 23:00 -1.26 1.50 350.48 374.88 -27.16 12.53 13.26 12.95 10/2/04 0:00 -0.49 2.29 368.17 380.52 -15.13 13.14 13.78 13.54 10/2/04 1:00 -1.32 1.80 374.90 384.11 -12.34 13.84 14.26 14.05 10/2/04 2:00 -0.85 1.25 380.48 385.80 -7.42 14.03 14.39 14.18 10/2/04 3:00 -1.15 1.48 373.94 385.49 -14.17 14.23 14.56 14.36 10/2/04 4:00 -0.58 1.17 382.36 387.16 -6.56 14.55 14.79 14.58 10/2/04 5:00 -0.95 1.51 381.67 383.51 -4.31 13.69 14.05 13.87 10/2/04 6:00 -0.71 1.53 378.45 381.04 -4.82 13.16 13.40 13.26 10/2/04 7:00 -1.92 1.82 367.66 373.22 -9.30 12.33 12.80 12.53 10/2/04 8:00 0.36 1.70 372.82 375.75 -4.27 12.14 12.44 12.20 10/2/04 9:00 17.10 5.35 376.77 379.44 9.09 12.71 12.88 12.75


89

Table A-1 contd. 10/2/04 10:00 65.04 15.74 378.46 382.22 45.54 13.16 13.21 13.07 10/2/04 11:00 218.86 51.30 377.95 387.40 158.12 14.29 13.91 13.69 10/2/04 12:00 392.85 93.64 336.28 394.34 241.15 16.38 15.24 14.92 10/2/04 13:00 516.83 118.99 310.71 399.23 309.32 17.89 16.15 15.77 10/2/04 14:00 366.10 83.30 341.20 395.59 228.41 17.52 16.16 15.87 10/2/04 15:00 346.25 81.33 342.67 394.39 213.20 17.22 16.11 15.86 10/2/04 16:00 371.82 85.59 303.97 393.09 197.11 17.62 16.34 16.14 10/2/04 17:00 251.08 56.41 299.84 385.93 108.57 16.78 16.13 15.83 10/2/04 18:00 161.18 31.08 295.79 381.45 44.43 16.02 15.97 15.67 10/2/04 19:00 50.61 11.91 289.80 374.63 -46.13 14.31 14.66 14.21 10/2/04 20:00 -1.22 2.08 287.05 362.19 -78.44 12.08 12.78 12.26 10/2/04 21:00 -1.68 1.96 305.78 365.22 -63.08 11.61 12.32 11.94 10/2/04 22:00 -1.76 1.92 303.08 364.69 -65.29 11.23 11.81 11.37 10/2/04 23:00 -2.45 2.08 283.38 364.23 -85.38 11.20 11.78 11.40 10/3/04 0:00 -2.50 1.97 281.56 363.32 -86.24 10.93 11.48 11.11 10/3/04 1:00 -1.80 1.96 296.45 362.77 -70.08 10.66 11.16 10.79 10/3/04 2:00 -1.69 1.44 306.74 365.20 -61.60 11.28 11.65 11.33 10/3/04 3:00 -2.22 1.85 287.86 362.44 -78.65 10.86 11.37 11.03 10/3/04 4:00 -2.19 1.84 288.12 361.66 -77.57 10.64 11.12 10.78 10/3/04 5:00 -2.32 1.96 280.97 359.45 -82.77 10.07 10.58 10.21 10/3/04 6:00 -2.40 1.91 280.33 356.63 -80.61 9.48 9.99 9.61 10/3/04 7:00 -1.96 2.06 284.94 354.60 -73.68 8.93 9.45 9.07 10/3/04 8:00 3.67 3.32 292.73 355.33 -62.25 8.85 9.39 9.00 10/3/04 9:00 57.80 14.98 307.31 364.43 -14.29 10.30 10.53 10.30 10/3/04 10:00 120.06 25.44 309.81 371.57 32.85 11.94 11.80 11.60 10/3/04 11:00 269.77 52.98 294.14 378.37 132.56 14.17 13.43 13.14 10/3/04 12:00 404.96 94.31 311.61 390.19 232.07 16.05 14.79 14.67 10/3/04 13:00 542.37 125.75 300.76 399.52 317.86 18.23 16.43 16.15 10/3/04 14:00 445.86 100.92 318.74 398.87 264.79 18.18 16.65 16.43 10/3/04 15:00 302.05 68.55 349.89 398.51 184.88 17.64 16.61 16.52 10/3/04 16:00 252.94 57.84 358.69 399.21 154.58 17.88 17.03 16.88 10/3/04 17:00 143.15 32.07 369.75 396.76 84.07 17.77 17.22 17.00 10/3/04 18:00 89.37 20.60 355.42 391.61 32.57 16.95 16.67 16.43 10/3/04 19:00 39.45 9.84 329.64 384.00 -24.75 15.81 15.91 15.54 10/3/04 20:00 -0.87 1.28 330.61 374.90 -46.44 14.33 14.86 14.37 10/3/04 21:00 -0.92 0.92 347.92 377.72 -31.64 14.11 14.43 13.98 10/3/04 22:00 -1.13 1.16 355.20 379.88 -26.97 14.20 14.54 14.25 10/3/04 23:00 -0.74 0.54 360.22 380.87 -21.93 14.35 14.51 14.24 10/4/04 0:00 -0.75 0.64 358.33 380.65 -23.70 14.30 14.45 14.18 10/4/04 1:00 -0.70 0.63 356.80 380.71 -25.24 14.32 14.47 14.23 10/4/04 2:00 -1.19 0.69 348.94 380.10 -33.03 14.33 14.49 14.26 10/4/04 3:00 -0.87 0.43 354.74 379.88 -26.43 14.16 14.22 13.99 10/4/04 4:00 -2.09 1.36 306.60 373.61 -70.45 13.47 13.84 13.57 10/4/04 5:00 -1.89 1.06 316.22 371.21 -57.94 12.93 13.23 12.91 10/4/04 6:00 -1.30 1.31 321.76 371.51 -52.35 12.74 13.06 12.77 10/4/04 7:00 -0.81 0.75 344.94 374.32 -30.94 13.08 13.24 13.00 10/4/04 8:00 0.62 1.06 340.86 374.36 -33.94 13.16 13.31 13.06 10/4/04 9:00 38.52 9.79 327.82 375.53 -18.97 13.32 13.42 13.17 10/4/04 10:00 132.48 30.90 329.32 384.03 46.87 14.78 14.58 14.36 10/4/04 11:00 261.42 58.67 326.68 394.40 135.03 17.18 16.43 16.22 10/4/04 12:00 401.76 95.14 318.23 403.17 221.68 19.41 18.15 18.02


90

Table A-1 contd. 10/4/04 13:00 451.09 105.43 335.01 409.82 270.84 20.56 19.21 19.14 10/4/04 14:00 299.31 69.70 358.58 408.81 179.38 20.37 19.47 19.33 10/4/04 15:00 275.11 63.38 361.44 410.99 162.16 20.50 19.80 19.63 10/4/04 16:00 280.15 63.97 352.97 413.63 155.52 21.23 20.54 20.32 10/4/04 17:00 83.51 18.65 392.46 409.93 47.39 20.43 20.08 19.77 10/4/04 18:00 73.71 16.92 377.85 408.59 26.04 20.02 19.33 19.13 10/4/04 19:00 46.15 12.70 366.87 403.88 -3.56 19.35 19.37 18.99 10/4/04 20:00 0.43 0.63 381.80 399.01 -17.40 18.41 18.57 18.20 10/4/04 21:00 -0.86 -0.23 385.83 395.12 -9.92 17.40 17.43 16.98 10/4/04 22:00 -0.78 0.05 379.20 393.09 -14.73 16.59 16.61 16.26 10/4/04 23:00 -1.11 0.21 368.70 387.45 -20.08 15.30 15.36 15.12 10/5/04 0:00 -1.66 0.58 349.26 379.33 -32.32 14.17 14.56 14.26 10/5/04 1:00 -1.36 1.29 330.12 368.24 -40.77 12.55 13.27 12.86 10/5/04 2:00 0.22 1.58 361.91 376.09 -15.54 12.71 13.37 12.97 10/5/04 3:00 -0.73 0.71 364.60 378.21 -15.05 12.99 13.38 13.03 10/5/04 4:00 -0.26 0.67 369.62 378.78 -10.09 12.90 13.21 12.91 10/5/04 5:00 0.03 0.37 379.24 382.55 -3.64 13.31 13.52 13.32 10/5/04 6:00 0.00 0.57 378.47 384.43 -6.54 13.66 13.98 13.74 10/5/04 7:00 -2.02 2.24 363.27 388.06 -29.04 15.08 15.64 15.43 10/5/04 8:00 1.47 1.16 365.76 388.85 -22.79 15.73 15.88 15.63 10/5/04 9:00 25.41 6.41 384.30 392.90 10.40 16.04 15.98 15.77 10/5/04 10:00 81.27 19.20 378.84 396.84 44.07 16.46 16.21 16.09 10/5/04 11:00 227.05 52.66 377.69 405.16 146.92 18.09 17.41 17.34 10/5/04 12:00 174.90 41.09 390.25 405.94 118.12 18.64 17.99 17.89 10/5/04 13:00 125.23 29.27 387.99 403.55 80.39 18.48 18.06 17.89 10/5/04 14:00 61.30 12.97 385.39 393.45 40.27 16.12 15.58 15.40 10/5/04 15:00 101.43 23.50 385.14 392.17 70.90 14.50 14.61 14.42 10/5/04 16:00 99.51 23.04 386.03 394.82 67.69 15.34 15.20 15.05 10/5/04 17:00 63.59 14.47 386.47 393.88 41.71 15.57 15.37 15.22 10/5/04 18:00 50.70 12.49 383.12 391.95 29.38 15.18 15.11 14.92 10/5/04 19:00 12.16 3.42 374.96 387.90 -4.20 14.83 14.79 14.61 10/5/04 20:00 0.55 0.92 362.07 384.31 -22.60 14.25 14.33 14.12 10/5/04 21:00 -0.89 0.68 361.53 383.44 -23.48 13.98 14.13 13.93 10/5/04 22:00 -0.43 0.57 368.41 383.94 -16.53 13.99 14.11 13.92 10/5/04 23:00 -0.64 0.31 367.02 382.79 -16.73 14.06 14.07 13.88 10/6/04 0:00 -1.30 0.57 344.60 377.86 -35.14 13.40 13.53 13.31 10/6/04 1:00 -1.46 0.74 337.83 373.83 -38.20 12.74 12.96 12.70 10/6/04 2:00 -1.42 1.07 331.64 372.66 -43.51 12.14 12.42 12.16 10/6/04 3:00 -2.17 1.35 308.17 368.11 -63.47 11.45 11.84 11.56 10/6/04 4:00 -1.32 1.35 323.63 369.62 -48.66 11.33 11.67 11.41 10/6/04 5:00 -1.01 0.93 335.91 371.95 -37.99 11.76 11.93 11.73 10/6/04 6:00 -1.10 0.77 336.40 372.19 -37.66 11.88 12.02 11.81 10/6/04 7:00 -1.43 0.99 326.30 370.62 -46.75 11.66 11.88 11.65 10/6/04 8:00 0.36 1.28 330.65 370.61 -40.88 11.58 11.77 11.56 10/6/04 9:00 29.13 8.04 326.36 371.40 -23.95 11.74 11.84 11.64 10/6/04 10:00 100.68 23.87 313.27 373.60 16.48 12.09 12.01 11.84 10/6/04 11:00 203.17 45.06 297.51 376.58 79.04 12.90 12.56 12.38 10/6/04 12:00 393.01 94.00 299.62 386.17 212.46 15.20 14.12 13.92 10/6/04 13:00 428.37 102.07 315.80 390.58 251.52 16.33 15.05 14.77 10/6/04 14:00 448.67 106.61 316.39 393.29 265.17 16.86 15.60 15.34 10/6/04 15:00 366.17 87.24 337.16 393.78 222.32 17.01 15.82 15.69 10/6/04 16:00 364.85 85.96 320.43 392.60 206.72 17.35 16.13 16.03 10/6/04 17:00 165.79 35.87 333.15 387.34 75.73 16.05 15.59 15.39


91

Table A-2

Date & time RHhigh RHlow uavghigh Winddir shf1 shf2 shf3

9/29/04 17:00 62.22 62.63 4.275 322.3 2.29 5.53 9.25 9/29/04 18:00 61.57 61.94 3.410 311.5 7.72 0.34 1.12 9/29/04 19:00 67.26 67.74 3.100 323.7 -19.52 -5.41 -10.32 9/29/04 20:00 73.95 75.60 1.439 316.3 -35.96 -12.52 -18.78 9/29/04 21:00 79.06 80.59 0.518 275.7 -37.48 -21.85 -28.82 9/29/04 22:00 83.42 85.13 0.665 231.0 -35.57 -25.36 -31.50 9/29/04 23:00 88.11 89.54 0.780 225.1 -35.93 -26.44 -32.09 9/30/04 0:00 90.01 90.42 0.798 272.4 -23.95 -26.07 -30.60 9/30/04 1:00 90.51 90.31 0.539 289.4 -15.64 -19.72 -21.43 9/30/04 2:00 90.87 90.53 0.596 262.2 -15.71 -15.79 -17.28 9/30/04 3:00 91.44 90.92 0.647 265.5 -14.22 -14.03 -15.74 9/30/04 4:00 91.75 91.18 0.596 264.9 -14.38 -12.74 -14.52 9/30/04 5:00 92.32 92.02 0.576 239.4 -15.04 -12.55 -14.64 9/30/04 6:00 93.13 92.80 0.681 235.0 -23.01 -13.01 -15.93 9/30/04 7:00 95.71 95.01 0.919 207.2 -31.95 -19.83 -25.31 9/30/04 8:00 96.79 96.58 1.090 205.9 -29.40 -21.31 -26.24 9/30/04 9:00 96.87 96.84 0.855 241.5 -26.81 -22.93 -27.79 9/30/04 10:00 91.87 92.76 0.763 230.8 -16.63 -20.62 -23.73 9/30/04 11:00 74.92 76.99 0.614 250.4 3.57 -15.02 -12.92 9/30/04 12:00 69.77 70.50 0.788 293.0 23.96 -3.37 22.44 9/30/04 13:00 63.79 64.62 0.825 284.6 27.19 11.72 42.63 9/30/04 14:00 60.97 61.67 0.983 267.5 43.85 21.17 44.81 9/30/04 15:00 59.88 60.28 1.069 2.5 36.66 28.44 40.67 9/30/04 16:00 63.43 63.82 1.057 247.8 6.15 22.24 20.75 9/30/04 17:00 65.09 65.58 1.035 354.8 8.94 14.89 11.47 9/30/04 18:00 69.34 69.56 0.367 144.7 -2.43 10.87 7.24 9/30/04 19:00 78.91 78.81 0.099 145.8 -9.92 4.05 0.72 9/30/04 20:00 84.24 83.84 0.160 66.9 -13.95 -1.48 -4.50 9/30/04 21:00 86.82 86.38 0.362 116.5 -14.10 -4.41 -7.40 9/30/04 22:00 88.02 87.54 0.253 79.7 -11.92 -5.34 -7.80 9/30/04 23:00 85.79 85.11 0.478 121.7 -11.45 -5.50 -7.83 10/1/04 0:00 85.64 85.10 0.373 104.8 -12.30 -5.66 -8.22 10/1/04 1:00 86.61 86.11 0.265 52.9 -13.01 -6.67 -9.67 10/1/04 2:00 85.65 85.25 0.450 91.9 -12.38 -6.97 -10.05 10/1/04 3:00 85.91 85.55 0.271 143.7 -10.84 -6.82 -9.49 10/1/04 4:00 88.51 87.95 0.906 43.8 -11.71 -6.61 -9.09 10/1/04 5:00 89.64 88.90 0.267 84.9 -13.01 -7.38 -10.52 10/1/04 6:00 89.95 89.12 0.166 48.3 -12.93 -7.32 -9.62 10/1/04 7:00 91.04 90.46 0.315 88.9 -23.35 -10.69 -15.60 10/1/04 8:00 92.13 91.45 0.147 89.5 -17.82 -12.52 -16.76 10/1/04 9:00 90.94 90.28 0.266 148.4 -7.10 -11.13 -13.34 10/1/04 10:00 86.14 85.62 0.748 66.8 -0.53 -6.29 -6.13 10/1/04 11:00 78.59 78.61 1.719 226.8 9.73 -1.65 0.33 10/1/04 12:00 76.93 75.84 1.349 216.3 26.80 6.93 21.85 10/1/04 13:00 82.39 81.39 1.188 228.0 22.89 14.83 28.30 10/1/04 14:00 84.38 83.28 1.178 210.7 13.55 14.77 19.71 10/1/04 15:00 81.83 80.83 1.226 240.7 16.26 13.68 16.98 10/1/04 16:00 82.11 81.28 1.251 241.7 3.79 12.63 13.82 10/1/04 17:00 83.41 82.55 0.934 322.6 6.36 9.15 8.97


92

Table A-2 contd. 10/1/04 18:00 84.71 83.74 0.529 51.3 5.60 6.96 6.13 10/1/04 19:00 85.87 84.22 0.390 121.7 -16.71 3.65 1.03 10/1/04 20:00 91.20 90.95 0.408 113.0 -26.85 -5.59 -12.04 10/1/04 21:00 93.47 93.48 0.328 142.5 -30.64 -13.40 -21.75 10/1/04 22:00 93.38 93.40 0.492 153.6 -22.80 -14.86 -20.86 10/1/04 23:00 93.31 92.80 0.863 164.0 -19.27 -14.70 -19.58 10/2/04 0:00 92.07 91.40 1.308 137.5 -7.86 -11.65 -13.99 10/2/04 1:00 90.68 89.87 1.509 33.1 -3.78 -7.09 -8.14 10/2/04 2:00 91.85 91.08 1.981 46.0 -2.51 -4.24 -5.00 10/2/04 3:00 91.00 90.04 2.106 221.5 -4.90 -3.09 -4.46 10/2/04 4:00 90.40 89.57 1.253 226.4 -2.08 -2.62 -3.73 10/2/04 5:00 92.09 91.18 1.033 211.7 -6.54 -2.44 -4.08 10/2/04 6:00 94.08 92.86 1.306 214.9 -10.02 -3.75 -6.62 10/2/04 7:00 94.46 93.40 1.306 218.3 -23.18 -5.56 -10.92 10/2/04 8:00 95.11 94.39 1.415 219.5 -12.18 -8.69 -14.89 10/2/04 9:00 91.77 90.42 1.542 227.7 -5.61 -6.62 -9.53 10/2/04 10:00 88.94 87.78 1.449 233.5 1.26 -3.78 -4.46 10/2/04 11:00 85.05 84.14 1.481 239.7 10.44 -0.02 1.96 10/2/04 12:00 77.16 76.78 1.694 239.6 14.57 5.14 16.97 10/2/04 13:00 68.60 68.86 1.855 240.9 22.87 11.63 32.76 10/2/04 14:00 62.75 63.15 1.948 240.9 13.47 17.28 35.31 10/2/04 15:00 60.88 61.30 2.128 244.9 13.74 13.56 21.08 10/2/04 16:00 57.78 58.23 1.898 242.3 18.49 12.77 22.76 10/2/04 17:00 53.32 54.18 1.629 241.8 -4.17 8.74 12.05 10/2/04 18:00 58.17 58.95 1.764 226.3 -18.96 2.22 -1.58 10/2/04 19:00 66.43 67.73 1.830 212.0 -26.97 -3.18 -11.07 10/2/04 20:00 75.59 76.55 1.274 198.3 -35.50 -9.60 -20.09 10/2/04 21:00 80.79 80.93 1.431 194.2 -27.22 -14.19 -22.71 10/2/04 22:00 84.43 84.78 1.587 202.9 -26.43 -14.07 -22.33 10/2/04 23:00 82.78 82.89 2.079 212.0 -28.43 -14.17 -23.22 10/3/04 0:00 82.29 82.37 2.102 216.9 -27.79 -14.86 -24.52 10/3/04 1:00 83.04 83.15 2.043 213.6 -25.23 -15.33 -24.97 10/3/04 2:00 80.11 80.08 1.928 218.8 -21.96 -14.20 -21.90 10/3/04 3:00 80.34 80.35 1.843 223.5 -26.11 -14.29 -22.92 10/3/04 4:00 81.59 81.60 1.812 222.5 -25.27 -14.94 -23.70 10/3/04 5:00 83.56 83.67 1.986 218.7 -26.83 -15.46 -24.42 10/3/04 6:00 85.33 85.47 1.821 216.1 -28.01 -16.00 -25.29 10/3/04 7:00 88.03 88.16 1.783 206.3 -27.85 -16.53 -26.01 10/3/04 8:00 89.68 89.78 1.629 209.1 -24.72 -16.82 -25.67 10/3/04 9:00 87.81 87.29 2.057 209.5 -9.83 -14.88 -21.17 10/3/04 10:00 84.37 83.75 2.379 216.6 -2.55 -9.25 -11.15 10/3/04 11:00 79.33 78.99 2.351 218.7 1.20 -5.74 -6.76 10/3/04 12:00 75.57 74.69 2.644 223.2 17.87 0.16 10.49 10/3/04 13:00 69.21 69.21 2.211 227.4 22.69 10.22 32.01 10/3/04 14:00 66.83 67.01 2.526 224.4 26.73 17.00 37.55 10/3/04 15:00 68.65 68.36 2.426 221.3 21.60 18.30 29.50 10/3/04 16:00 67.76 67.72 2.150 225.5 19.59 17.91 25.31 10/3/04 17:00 64.91 65.17 1.874 225.8 8.62 15.32 18.78 10/3/04 18:00 65.34 65.77 1.711 232.2 -0.05 9.85 9.23 10/3/04 19:00 71.75 72.72 1.240 200.1 -11.97 5.03 2.18


93

Table A-2 contd. 10/3/04 20:00 75.67 76.93 1.095 37.8 -20.96 -2.21 -8.22 10/3/04 21:00 77.39 78.66 1.357 147.5 -14.70 -6.02 -11.34 10/3/04 22:00 76.26 76.52 1.560 207.6 -11.85 -5.78 -9.88 10/3/04 23:00 76.57 76.71 1.678 295.4 -10.34 -5.13 -8.78 10/4/04 0:00 75.62 75.73 1.887 295.9 -10.90 -4.89 -8.61 10/4/04 1:00 74.81 74.66 2.130 215.7 -11.16 -5.27 -9.56 10/4/04 2:00 74.19 74.00 2.252 203.9 -12.31 -5.36 -9.66 10/4/04 3:00 74.50 74.35 2.268 202.5 -11.22 -5.72 -9.94 10/4/04 4:00 74.24 74.20 2.476 196.8 -21.41 -6.98 -13.44 10/4/04 5:00 76.21 76.51 2.077 5.5 -19.60 -8.88 -15.75 10/4/04 6:00 74.98 75.14 2.373 296.1 -18.22 -9.80 -16.79 10/4/04 7:00 73.52 73.49 2.267 239.2 -13.13 -9.36 -14.94 10/4/04 8:00 73.97 73.94 2.279 234.6 -13.43 -8.39 -13.36 10/4/04 9:00 75.01 75.02 2.427 244.9 -9.33 -7.70 -12.08 10/4/04 10:00 73.22 73.15 2.932 212.6 6.54 -4.27 -4.94 10/4/04 11:00 70.21 70.22 3.388 195.2 17.87 1.67 5.41 10/4/04 12:00 66.34 66.09 3.987 204.9 17.78 6.42 17.88 10/4/04 13:00 63.81 63.40 3.969 212.8 38.56 14.08 30.97 10/4/04 14:00 62.75 62.53 3.763 212.0 33.56 20.23 31.92 10/4/04 15:00 65.08 64.86 3.023 219.2 35.46 19.17 26.89 10/4/04 16:00 65.90 65.77 3.027 228.9 35.20 20.71 29.31 10/4/04 17:00 67.14 67.36 1.778 236.9 12.35 16.73 20.15 10/4/04 18:00 69.87 70.29 1.017 239.9 10.79 12.63 14.81 10/4/04 19:00 72.16 72.63 0.945 237.1 3.44 9.80 10.45 10/4/04 20:00 71.80 72.29 0.772 234.1 -3.51 5.51 3.58 10/4/04 21:00 77.54 78.98 0.755 69.1 -3.41 2.73 0.88 10/4/04 22:00 81.28 81.72 1.769 2.6 -4.63 1.45 -0.17 10/4/04 23:00 85.81 85.53 0.792 37.3 -8.24 -0.11 -2.37 10/5/04 0:00 89.55 89.00 0.235 71.6 -14.96 -2.54 -5.96 10/5/04 1:00 94.80 94.06 0.279 348.0 -26.50 -8.35 -14.79 10/5/04 2:00 96.59 95.75 0.146 28.1 -14.97 -11.74 -16.71 10/5/04 3:00 96.51 95.70 0.299 64.4 -12.39 -9.09 -11.49 10/5/04 4:00 97.37 96.63 0.291 53.3 -10.11 -8.10 -10.34 10/5/04 5:00 97.37 96.46 0.191 53.4 -3.86 -5.96 -6.77 10/5/04 6:00 96.22 95.38 0.694 342.9 -3.41 -3.71 -4.17 10/5/04 7:00 84.90 83.64 2.080 227.8 -3.40 -2.98 -4.21 10/5/04 8:00 82.70 81.84 1.758 238.1 -2.74 -2.53 -3.69 10/5/04 9:00 85.85 84.85 1.949 239.5 4.85 -1.24 -0.90 10/5/04 10:00 87.06 85.75 1.551 217.5 12.57 2.32 5.27 10/5/04 11:00 83.19 81.74 2.254 219.6 28.97 8.95 16.24 10/5/04 12:00 79.74 78.47 2.376 221.5 23.46 14.24 21.56 10/5/04 13:00 78.50 77.55 2.787 225.2 11.35 14.23 19.31 10/5/04 14:00 84.58 83.61 1.994 276.6 1.04 8.85 9.38 10/5/04 15:00 88.68 88.07 0.981 246.2 4.67 4.77 5.28 10/5/04 16:00 86.62 85.82 0.895 241.5 5.63 6.87 11.44 10/5/04 17:00 86.19 85.21 0.940 231.1 1.96 6.00 7.97 10/5/04 18:00 87.11 86.34 0.959 242.4 -1.50 5.16 6.35 10/5/04 19:00 88.10 87.16 1.300 232.8 -8.43 2.43 1.16 10/5/04 20:00 89.06 88.16 1.444 230.4 -11.65 -1.08 -4.29 10/5/04 21:00 90.23 89.25 1.204 232.3 -10.94 -2.81 -5.86


94

Table A-2 contd. 10/5/04 22:00 90.35 89.29 0.976 240.1 -8.72 -3.76 -6.64 10/5/04 23:00 86.50 85.45 1.521 239.3 -10.15 -3.94 -6.48 10/6/04 0:00 83.83 83.01 1.456 233.8 -15.42 -5.28 -8.81 10/6/04 1:00 82.32 81.79 1.667 216.7 -17.42 -7.05 -11.81 10/6/04 2:00 88.17 87.65 1.614 216.2 -16.51 -8.11 -13.26 10/6/04 3:00 89.44 88.85 1.737 211.7 -21.22 -8.99 -14.91 10/6/04 4:00 90.93 90.33 1.877 216.7 -16.85 -10.17 -16.30 10/6/04 5:00 90.14 89.19 2.308 222.5 -13.52 -9.56 -14.52 10/6/04 6:00 89.60 88.66 2.441 220.8 -13.34 -9.04 -13.60 10/6/04 7:00 89.71 88.86 2.191 219.8 -15.14 -8.95 -13.85 10/6/04 8:00 89.89 88.98 2.126 223.3 -14.09 -9.26 -14.34 10/6/04 9:00 88.69 87.70 2.547 223.7 -11.03 -8.81 -13.08 10/6/04 10:00 87.83 86.78 2.648 223.7 -4.92 -6.78 -9.07 10/6/04 11:00 85.65 84.59 2.911 225.8 -1.89 -4.37 -5.45 10/6/04 12:00 79.45 78.48 3.083 227.4 9.01 -0.31 6.51 10/6/04 13:00 73.48 73.12 2.944 231.9 15.26 5.22 16.88 10/6/04 14:00 70.43 70.19 3.230 228.1 27.59 9.43 20.07 10/6/04 15:00 67.41 66.96 3.140 228.2 25.54 11.79 20.91 10/6/04 16:00 64.37 63.90 3.062 228.5 25.93 12.78 18.83 10/6/04 17:00 66.23 66.16 2.079 230.9 6.80 7.89 7.63


95

Table A-3

Date & time t1_20 t2_15 t3_10 t4_5 u_high u_low Pan_T batvolt

9/29/04 17:00 15.11 15.21 15.35 15.48 4.275 3.774 15.60 12.50 9/29/04 18:00 15.15 15.24 15.31 15.34 3.410 2.939 16.58 12.45 9/29/04 19:00 15.18 15.24 15.23 15.17 3.100 2.654 15.21 12.44 9/29/04 20:00 15.17 15.19 15.10 14.90 1.439 1.018 13.42 12.47 9/29/04 21:00 15.13 15.09 14.88 14.52 0.518 0.299 11.28 12.45 9/29/04 22:00 15.05 14.93 14.60 14.12 0.665 0.453 10.19 12.44 9/29/04 23:00 14.91 14.75 14.33 13.78 0.780 0.577 9.33 12.43 9/30/04 0:00 14.78 14.56 14.08 13.49 0.798 0.498 8.96 12.42 9/30/04 1:00 14.70 14.38 13.88 13.33 0.539 0.390 9.49 12.41 9/30/04 2:00 14.61 14.25 13.79 13.32 0.596 0.354 9.80 12.41 9/30/04 3:00 14.52 14.16 13.73 13.31 0.647 0.429 10.05 12.41 9/30/04 4:00 14.44 14.10 13.69 13.31 0.596 0.413 10.27 12.40 9/30/04 5:00 14.35 14.04 13.66 13.30 0.576 0.306 10.32 12.40 9/30/04 6:00 14.27 14.00 13.62 13.27 0.681 0.434 10.15 12.40 9/30/04 7:00 14.18 13.94 13.55 13.13 0.919 0.739 9.04 12.39 9/30/04 8:00 14.10 13.86 13.41 12.90 1.090 0.979 8.46 12.38 9/30/04 9:00 14.01 13.75 13.26 12.71 0.855 0.728 7.94 12.37 9/30/04 10:00 13.92 13.63 13.11 12.56 0.763 0.481 8.99 12.36 9/30/04 11:00 13.83 13.54 13.04 12.56 0.614 0.567 12.33 12.31 9/30/04 12:00 13.81 13.51 13.13 12.96 0.788 0.750 16.22 12.33 9/30/04 13:00 13.89 13.61 13.48 13.71 0.825 0.715 18.28 12.33 9/30/04 14:00 14.03 13.84 13.93 14.42 0.983 0.795 19.53 12.34 9/30/04 15:00 14.25 14.11 14.37 15.01 1.069 1.026 20.53 12.34 9/30/04 16:00 14.36 14.39 14.73 15.34 1.057 1.065 19.54 12.33 9/30/04 17:00 14.47 14.60 14.90 15.34 1.035 1.008 18.39 12.32 9/30/04 18:00 14.57 14.73 14.98 15.32 0.367 0.367 17.56 12.30 9/30/04 19:00 14.67 14.81 14.99 15.21 0.099 0.091 16.23 12.28 9/30/04 20:00 14.73 14.84 14.95 15.04 0.160 0.128 15.06 12.27 9/30/04 21:00 14.76 14.83 14.86 14.86 0.362 0.299 14.07 12.25 9/30/04 22:00 14.76 14.80 14.77 14.71 0.253 0.205 13.55 12.24 9/30/04 23:00 14.74 14.76 14.69 14.60 0.478 0.423 13.34 12.23 10/1/04 0:00 14.72 14.72 14.62 14.51 0.373 0.310 13.20 12.22 10/1/04 1:00 14.70 14.67 14.56 14.42 0.265 0.230 12.91 12.21 10/1/04 2:00 14.67 14.63 14.49 14.34 0.450 0.407 12.73 12.20 10/1/04 3:00 14.63 14.58 14.43 14.26 0.271 0.257 12.68 12.19 10/1/04 4:00 14.60 14.54 14.37 14.21 0.906 0.802 12.64 12.18 10/1/04 5:00 14.57 14.50 14.32 14.13 0.267 0.224 12.40 12.17 10/1/04 6:00 14.53 14.45 14.26 14.06 0.166 0.122 12.36 12.17 10/1/04 7:00 14.50 14.41 14.21 13.99 0.315 0.305 11.67 12.16 10/1/04 8:00 14.46 14.36 14.11 13.83 0.147 0.140 11.34 12.15 10/1/04 9:00 14.41 14.29 14.02 13.73 0.266 0.255 11.43 12.14 10/1/04 10:00 14.36 14.23 13.97 13.74 0.748 0.715 12.62 12.13 10/1/04 11:00 14.32 14.21 13.99 13.86 1.719 1.579 14.63 12.14 10/1/04 12:00 14.31 14.23 14.11 14.17 1.349 1.164 16.88 12.14 10/1/04 13:00 14.35 14.32 14.35 14.64 1.188 0.909 18.06 12.15 10/1/04 14:00 14.44 14.48 14.61 14.95 1.178 0.984 17.88 12.14 10/1/04 15:00 14.54 14.62 14.79 15.11 1.226 1.105 17.89 12.14 10/1/04 16:00 14.64 14.74 14.92 15.24 1.251 1.014 17.75 12.13 10/1/04 17:00 14.73 14.84 15.01 15.27 0.934 0.898 17.34 12.12


96

Table A-3 contd. 10/1/04 18:00 14.80 14.91 15.06 15.28 0.529 0.503 17.09 12.11 10/1/04 19:00 14.85 14.95 15.08 15.25 0.390 0.374 16.96 12.10 10/1/04 20:00 14.89 14.97 15.03 15.07 0.408 0.304 15.06 12.10 10/1/04 21:00 14.89 14.93 14.88 14.73 0.328 0.190 13.50 12.08 10/1/04 22:00 14.85 14.84 14.67 14.40 0.492 0.421 13.04 12.07 10/1/04 23:00 14.78 14.72 14.50 14.19 0.863 0.842 12.85 12.06 10/2/04 0:00 14.70 14.62 14.37 14.07 1.308 1.318 13.11 12.06 10/2/04 1:00 14.63 14.54 14.31 14.08 1.509 1.517 13.56 12.05 10/2/04 2:00 14.58 14.49 14.29 14.13 1.981 1.979 13.94 12.05 10/2/04 3:00 14.55 14.47 14.30 14.17 2.106 1.947 14.17 12.05 10/2/04 4:00 14.54 14.47 14.31 14.20 1.253 0.844 14.43 12.04 10/2/04 5:00 14.53 14.47 14.33 14.24 1.033 0.788 14.35 12.04 10/2/04 6:00 14.52 14.46 14.33 14.23 1.306 0.895 13.97 12.03 10/2/04 7:00 14.52 14.46 14.31 14.18 1.306 0.775 13.34 12.02 10/2/04 8:00 14.50 14.43 14.25 14.05 1.415 0.864 12.78 12.02 10/2/04 9:00 14.47 14.38 14.18 13.97 1.542 1.069 12.98 12.01 10/2/04 10:00 14.43 14.34 14.14 13.97 1.449 1.095 13.50 12.12 10/2/04 11:00 14.41 14.32 14.15 14.05 1.481 1.311 14.53 12.54 10/2/04 12:00 14.40 14.34 14.23 14.27 1.694 1.499 16.33 12.52 10/2/04 13:00 14.43 14.41 14.41 14.64 1.855 1.614 17.73 12.51 10/2/04 14:00 14.50 14.54 14.67 15.06 1.948 1.735 18.60 12.49 10/2/04 15:00 14.61 14.70 14.90 15.26 2.128 1.962 18.44 12.48 10/2/04 16:00 14.72 14.83 15.02 15.34 1.898 1.715 19.00 12.46 10/2/04 17:00 14.80 14.93 15.11 15.39 1.629 1.591 18.95 12.45 10/2/04 18:00 14.87 14.98 15.12 15.25 1.764 1.153 18.17 12.43 10/2/04 19:00 14.90 14.98 15.03 15.02 1.830 1.244 16.23 12.40 10/2/04 20:00 14.89 14.93 14.88 14.74 1.274 1.166 14.13 12.38 10/2/04 21:00 14.85 14.84 14.68 14.41 1.431 1.379 12.79 12.36 10/2/04 22:00 14.78 14.72 14.49 14.16 1.587 1.431 12.07 12.34 10/2/04 23:00 14.69 14.59 14.32 13.96 2.079 1.519 11.61 12.32 10/3/04 0:00 14.60 14.47 14.16 13.76 2.102 1.283 11.21 12.31 10/3/04 1:00 14.51 14.35 14.00 13.57 2.043 1.436 10.82 12.29 10/3/04 2:00 14.41 14.23 13.85 13.41 1.928 1.236 11.02 12.28 10/3/04 3:00 14.32 14.12 13.73 13.29 1.843 1.184 10.82 12.27 10/3/04 4:00 14.23 14.02 13.61 13.15 1.812 1.092 10.64 12.25 10/3/04 5:00 14.14 13.92 13.49 13.01 1.986 1.152 10.27 12.24 10/3/04 6:00 14.06 13.82 13.37 12.87 1.821 1.141 9.82 12.23 10/3/04 7:00 13.97 13.72 13.25 12.74 1.783 1.532 9.30 12.22 10/3/04 8:00 13.88 13.62 13.14 12.61 1.629 1.290 8.99 12.20 10/3/04 9:00 13.79 13.52 13.03 12.52 2.057 1.630 9.57 12.20 10/3/04 10:00 13.72 13.45 12.99 12.56 2.379 1.539 11.22 12.19 10/3/04 11:00 13.66 13.41 13.01 12.70 2.351 1.490 12.93 12.19 10/3/04 12:00 13.64 13.42 13.11 12.97 2.644 1.726 14.99 12.19 10/3/04 13:00 13.66 13.50 13.34 13.45 2.211 1.515 16.87 12.20 10/3/04 14:00 13.74 13.67 13.68 14.03 2.526 1.738 18.22 12.20 10/3/04 15:00 13.87 13.88 14.03 14.45 2.426 1.571 18.74 12.19 10/3/04 16:00 14.02 14.09 14.29 14.71 2.150 1.470 19.07 12.19 10/3/04 17:00 14.16 14.26 14.48 14.88 1.874 1.255 18.94 12.18 10/3/04 18:00 14.29 14.40 14.60 14.90 1.711 1.188 18.23 12.17 10/3/04 19:00 14.38 14.48 14.63 14.83 1.209 1.157 17.24 12.16


97

Table A-3 contd. 10/3/04 20:00 14.43 14.52 14.59 14.66 1.095 1.019 15.63 12.15 10/3/04 21:00 14.46 14.51 14.49 14.42 1.357 1.322 14.72 12.14 10/3/04 22:00 14.45 14.46 14.38 14.26 1.560 1.553 14.54 12.13 10/3/04 23:00 14.43 14.41 14.30 14.17 1.678 1.662 14.50 12.12 10/4/04 0:00 14.40 14.37 14.24 14.10 1.887 1.901 14.41 12.12 10/4/04 1:00 14.37 14.33 14.18 14.03 2.130 2.154 14.36 12.11 10/4/04 2:00 14.34 14.28 14.12 13.95 2.252 2.311 14.38 12.10 10/4/04 3:00 14.31 14.24 14.07 13.88 2.268 2.327 14.29 12.10 10/4/04 4:00 14.28 14.20 14.01 13.80 2.476 2.542 13.81 12.09 10/4/04 5:00 14.24 14.15 13.93 13.67 2.077 2.053 13.32 12.08 10/4/04 6:00 14.20 14.09 13.83 13.54 2.373 2.404 12.86 12.07 10/4/04 7:00 14.15 14.02 13.74 13.43 2.267 2.322 12.96 12.07 10/4/04 8:00 14.10 13.96 13.67 13.37 2.279 2.332 13.06 12.06 10/4/04 9:00 14.05 13.90 13.61 13.32 2.427 2.484 13.15 12.06 10/4/04 10:00 14.00 13.86 13.58 13.34 2.932 2.935 14.05 12.05 10/4/04 11:00 13.98 13.84 13.62 13.50 3.388 3.225 16.28 12.06 10/4/04 12:00 13.98 13.89 13.76 13.83 3.987 3.358 18.30 12.06 10/4/04 13:00 14.03 13.99 14.00 14.28 3.969 2.899 20.02 12.07 10/4/04 14:00 14.13 14.17 14.32 14.78 3.763 2.776 20.87 12.07 10/4/04 15:00 14.26 14.37 14.61 15.09 3.023 1.859 21.03 12.07 10/4/04 16:00 14.41 14.55 14.84 15.36 3.027 2.159 21.75 12.07 10/4/04 17:00 14.55 14.73 15.06 15.57 1.778 1.609 21.41 12.06 10/4/04 18:00 14.68 14.87 15.18 15.60 1.017 0.887 20.91 12.31 10/4/04 19:00 14.78 14.97 15.25 15.62 0.945 0.871 20.34 12.40 10/4/04 20:00 14.86 15.04 15.28 15.56 0.772 0.709 19.36 12.41 10/4/04 21:00 14.92 15.08 15.25 15.43 0.755 0.721 18.46 12.40 10/4/04 22:00 14.95 15.08 15.21 15.33 1.769 1.513 17.70 12.38 10/4/04 23:00 14.96 15.07 15.16 15.23 0.792 0.662 16.81 12.37 10/5/04 0:00 14.96 15.05 15.09 15.11 0.235 0.152 15.83 12.35 10/5/04 1:00 14.95 15.01 14.99 14.91 0.279 0.151 14.35 12.33 10/5/04 2:00 14.91 14.93 14.82 14.60 0.146 0.171 13.63 12.32 10/5/04 3:00 14.85 14.83 14.66 14.43 0.299 0.287 13.65 12.31 10/5/04 4:00 14.78 14.74 14.56 14.34 0.291 0.266 13.47 12.30 10/5/04 5:00 14.72 14.67 14.49 14.30 0.191 0.197 13.67 12.29 10/5/04 6:00 14.68 14.62 14.46 14.32 0.694 0.533 13.90 12.28 10/5/04 7:00 14.64 14.59 14.46 14.35 2.080 1.386 14.37 12.27 10/5/04 8:00 14.62 14.58 14.45 14.36 1.758 1.613 15.13 12.27 10/5/04 9:00 14.61 14.57 14.46 14.38 1.949 1.733 15.64 12.27 10/5/04 10:00 14.60 14.57 14.49 14.48 1.551 1.042 16.52 12.27 10/5/04 11:00 14.61 14.60 14.58 14.70 2.254 1.488 17.98 12.27 10/5/04 12:00 14.64 14.68 14.76 15.06 2.376 1.512 19.00 12.27 10/5/04 13:00 14.72 14.80 14.96 15.33 2.787 2.015 19.31 12.27 10/5/04 14:00 14.80 14.92 15.11 15.41 1.994 1.790 18.11 12.26 10/5/04 15:00 14.88 15.00 15.15 15.35 0.981 0.934 16.82 12.25 10/5/04 16:00 14.93 15.04 15.16 15.35 0.895 0.840 16.86 12.25 10/5/04 17:00 14.97 15.08 15.21 15.40 0.940 0.731 16.78 12.24 10/5/04 18:00 15.00 15.11 15.23 15.41 0.959 0.877 16.58 12.23 10/5/04 19:00 15.04 15.14 15.23 15.35 1.300 1.044 15.96 12.23 10/5/04 20:00 15.06 15.14 15.19 15.22 1.444 1.087 15.34 12.22 10/5/04 21:00 15.05 15.11 15.10 15.06 1.204 0.956 14.90 12.21


98

Table A-3 contd. 10/5/04 22:00 15.03 15.06 15.01 14.93 0.976 0.952 14.62 12.21 10/5/04 23:00 15.00 15.01 14.93 14.83 1.521 1.336 14.55 12.20 10/6/04 0:00 14.97 14.96 14.85 14.71 1.456 1.182 14.14 12.20 10/6/04 1:00 14.93 14.90 14.75 14.56 1.667 1.085 13.54 12.19 10/6/04 2:00 14.88 14.82 14.64 14.40 1.614 1.031 12.95 12.18 10/6/04 3:00 14.82 14.74 14.52 14.25 1.737 1.288 12.36 12.17 10/6/04 4:00 14.75 14.65 14.40 14.08 1.877 1.167 11.86 12.16 10/6/04 5:00 14.68 14.56 14.28 13.94 2.308 1.439 11.92 12.16 10/6/04 6:00 14.61 14.47 14.18 13.84 2.441 1.502 12.02 12.15 10/6/04 7:00 14.54 14.40 14.09 13.75 2.191 1.316 11.93 12.15 10/6/04 8:00 14.48 14.32 14.01 13.66 2.126 1.339 11.81 12.14 10/6/04 9:00 14.41 14.25 13.93 13.58 2.547 1.609 11.87 12.14 10/6/04 10:00 14.43 14.19 13.87 13.54 2.648 1.685 12.28 12.14 10/6/04 11:00 14.39 14.14 13.84 13.57 2.911 1.873 12.80 12.14 10/6/04 12:00 14.35 14.12 13.87 13.70 3.083 2.065 14.49 12.14 10/6/04 13:00 14.35 14.14 13.98 13.99 2.944 2.178 15.91 12.14 10/6/04 14:00 14.38 14.22 14.17 14.30 3.230 2.242 16.89 12.15 10/6/04 15:00 14.44 14.33 14.36 14.57 3.140 2.122 17.82 12.15 10/6/04 16:00 14.51 14.45 14.53 14.76 3.062 2.093 18.75 12.15 10/6/04 17:00 14.60 14.55 14.65 14.83 2.079 1.564 17.93 12.15


99

Table A-4 Date & time logCn

2 modq varCn2 varmodq puCn

2 n

9/30/04 14:00 -1.313 -0.203 0.015 0.000 64.018 60 9/30/04 15:00 -1.770 -0.211 0.016 0.000 25.497 60 9/30/04 16:00 -2.843 -0.218 0.038 0.000 3.115 60 9/30/04 17:00 -2.864 -0.216 0.055 0.000 4.090 60 9/30/04 18:00 -2.005 -0.212 0.008 0.000 11.497 60 9/30/04 19:00 -2.245 -0.210 0.008 0.000 6.135 60 9/30/04 20:00 -1.802 -0.186 0.004 0.000 16.474 60 9/30/04 21:00 -1.814 -0.194 0.004 0.000 15.865 60 9/30/04 22:00 -2.105 -0.208 0.005 0.000 7.993 60 9/30/04 23:00 -2.102 -0.211 0.005 0.000 8.026 60 10/1/04 0:00 -2.146 -0.208 0.004 0.000 7.252 60 10/1/04 1:00 -2.076 -0.199 0.005 0.000 8.651 60 10/1/04 2:00 -2.018 -0.185 0.007 0.000 10.100 60 10/1/04 3:00 -2.051 -0.205 0.006 0.000 9.253 60 10/1/04 4:00 -2.329 -0.204 0.010 0.000 5.383 60 10/1/04 5:00 -2.304 -0.204 0.004 0.000 6.566 60 10/1/04 6:00 -2.281 -0.204 0.004 0.000 8.056 60 10/1/04 7:00 -1.702 -0.193 0.006 0.000 22.974 60 10/1/04 8:00 -1.907 -0.192 0.006 0.000 13.390 60 10/1/04 9:00 -2.802 -0.211 0.017 0.000 3.707 60 10/1/04 10:00 -2.584 -0.216 0.090 0.000 45.251 60 10/1/04 11:00 -1.865 -0.206 0.028 0.000 22.791 60 10/1/04 12:00 -1.639 -0.195 0.015 0.000 33.704 60 10/1/04 13:00 -2.051 -0.189 0.012 0.000 10.288 60 10/1/04 14:00 -2.493 -0.186 0.068 0.000 10.260 60 10/1/04 15:00 -2.827 -0.196 0.024 0.000 2.457 60 10/1/04 16:00 -3.363 -0.186 0.135 0.000 3.224 60 10/1/04 17:00 -3.434 -0.182 0.049 0.000 2.952 60 10/1/04 18:00 -3.075 -0.176 0.014 0.000 1.155 60 10/1/04 19:00 -1.937 -0.170 0.017 0.000 17.527 60 10/1/04 20:00 -1.647 -0.125 0.009 0.000 29.750 60 10/1/04 21:00 -1.465 -0.009 0.005 0.000 36.210 60 10/1/04 22:00 -1.585 -0.001 0.004 0.000 26.932 60 10/1/04 23:00 -1.522 -0.001 0.006 0.000 30.907 60 10/2/04 0:00 -1.809 -0.010 0.006 0.000 16.203 60 10/2/04 1:00 -2.144 -0.064 0.008 0.000 7.556 60 10/2/04 2:00 -2.393 -0.106 0.009 0.000 4.203 60 10/2/04 3:00 -2.240 -0.168 0.009 0.000 6.236 60 10/2/04 4:00 -2.211 -0.188 0.006 0.000 6.399 60 10/2/04 5:00 -2.179 -0.123 0.008 0.000 7.213 60 10/2/04 6:00 -2.234 -0.131 0.007 0.000 6.016 60 10/2/04 7:00 -1.709 -0.104 0.007 0.000 22.109 60 10/2/04 8:00 -2.203 -0.069 0.006 0.000 9.851 60 10/2/04 9:00 -2.520 -0.197 0.006 0.000 3.138 60 10/2/04 10:00 -2.822 -0.178 0.189 0.001 169.733 60 10/2/04 11:00 -2.600 -0.225 0.053 0.000 18.457 60 10/2/04 12:00 -1.772 -0.221 0.036 0.000 25.473 60 10/2/04 13:00 -1.700 -0.218 0.029 0.000 35.532 60 10/2/04 14:00 -2.429 -0.222 0.022 0.000 15.536 60


100

Table A-4 contd. 10/2/04 15:00 -2.429 -0.228 0.035 0.000 8.270 60 10/2/04 16:00 -2.534 -0.227 0.044 0.000 6.685 60 10/2/04 17:00 -2.187 -0.235 0.005 0.000 9.452 60 10/2/04 18:00 -1.922 -0.236 0.016 0.000 16.431 60 10/2/04 19:00 -1.430 -0.235 0.011 0.000 42.256 60 10/2/04 20:00 -1.267 -0.229 0.013 0.000 57.892 60 10/2/04 21:00 -1.393 -0.226 0.014 0.000 43.580 60 10/2/04 22:00 -1.424 -0.222 0.011 0.000 40.428 60 10/2/04 23:00 -1.348 -0.224 0.008 0.000 45.999 60 10/3/04 0:00 -1.373 -0.226 0.007 0.000 43.327 60 10/3/04 1:00 -1.437 -0.227 0.007 0.000 38.907 60 10/3/04 2:00 -1.507 -0.229 0.007 0.000 32.351 60 10/3/04 3:00 -1.417 -0.230 0.006 0.000 39.108 60 10/3/04 4:00 -1.449 -0.229 0.005 0.000 36.238 60 10/3/04 5:00 -1.409 -0.228 0.006 0.000 39.710 60 10/3/04 6:00 -1.453 -0.227 0.007 0.000 36.147 60 10/3/04 7:00 -1.496 -0.221 0.007 0.000 32.909 60 10/3/04 8:00 -1.611 -0.211 0.009 0.000 26.679 60 10/3/04 9:00 -2.601 -0.232 0.010 0.000 6.019 60 10/3/04 10:00 -2.506 -0.232 0.018 0.000 6.141 60 10/3/04 11:00 -2.270 -0.226 0.028 0.000 9.569 60 10/3/04 12:00 -1.628 -0.220 0.029 0.000 35.735 60 10/3/04 13:00 -1.323 -0.211 0.010 0.000 51.476 60 10/3/04 14:00 -1.705 -0.214 0.012 0.000 23.842 60 10/3/04 15:00 -2.460 -0.222 0.035 0.000 6.075 60 10/3/04 16:00 -2.916 -0.223 0.041 0.000 3.656 60 10/3/04 17:00 -2.231 -0.224 0.051 0.000 11.832 60 10/3/04 18:00 -1.898 -0.225 0.025 0.000 15.468 60 10/3/04 19:00 -1.648 -0.224 0.020 0.000 26.548 60 10/3/04 20:00 -1.620 -0.221 0.016 0.000 26.095 60 10/3/04 21:00 -1.445 -0.224 0.011 0.000 37.898 60 10/3/04 22:00 -1.699 -0.218 0.013 0.000 21.109 60 10/3/04 23:00 -1.787 -0.223 0.012 0.000 17.075 60 10/4/04 0:00 -1.778 -0.223 0.011 0.000 17.415 60 10/4/04 1:00 -1.792 -0.223 0.011 0.000 16.931 60 10/4/04 2:00 -1.810 -0.221 0.010 0.000 16.452 60 10/4/04 3:00 -1.924 -0.221 0.011 0.000 12.662 60 10/4/04 4:00 -1.518 -0.224 0.011 0.000 31.707 60 10/4/04 5:00 -1.496 -0.226 0.010 0.000 33.379 60 10/4/04 6:00 -1.568 -0.230 0.012 0.000 28.709 60 10/4/04 7:00 -1.816 -0.230 0.013 0.000 16.658 60 10/4/04 8:00 -1.817 -0.230 0.010 0.000 15.952 60 10/4/04 9:00 -1.873 -0.236 0.015 0.000 14.768 60 10/4/04 10:00 -2.487 -0.235 0.041 0.000 6.665 60 10/4/04 11:00 -2.602 -0.227 0.027 0.000 5.692 60 10/4/04 12:00 -2.557 -0.214 0.022 0.000 5.043 60 10/4/04 13:00 -2.394 -0.209 0.009 0.000 6.339 60 10/4/04 14:00 -2.306 -0.212 0.014 0.000 7.310 60 10/4/04 15:00 -2.556 -0.214 0.012 0.000 3.933 60 10/4/04 16:00 -2.409 -0.209 0.019 0.000 5.499 60


101

Table A-4 contd. 10/4/04 17:00 -1.857 -0.208 0.006 0.000 14.747 60 10/4/04 18:00 -1.752 -0.206 0.036 0.000 67.504 60 10/4/04 19:00 -1.593 -0.208 0.005 0.000 27.021 60 10/4/04 20:00 -1.525 -0.206 0.006 0.000 31.282 60 10/4/04 21:00 -2.043 -0.206 0.017 0.000 9.991 60 10/4/04 22:00 -2.211 -0.209 0.013 0.000 9.108 60 10/4/04 23:00 -2.193 -0.213 0.009 0.000 7.384 60 10/5/04 0:00 -2.246 -0.203 0.010 0.000 7.469 60 10/5/04 1:00 -2.130 -0.050 0.018 0.000 10.695 60 10/5/04 2:00 -1.879 -0.001 0.004 0.000 13.874 60 10/5/04 3:00 -1.701 0.000 0.004 0.000 20.214 60 10/5/04 4:00 -1.583 0.000 0.004 0.000 26.481 60 10/5/04 5:00 -1.848 -0.001 0.003 0.000 14.866 60 10/5/04 6:00 -2.087 -0.011 0.005 0.000 8.742 60 10/5/04 7:00 -1.656 -0.197 0.005 0.000 22.775 60 10/5/04 8:00 -1.800 -0.206 0.005 0.000 16.733 60 10/5/04 9:00 -2.359 -0.198 0.013 0.000 5.265 60 10/5/04 10:00 -3.296 -0.198 0.039 0.000 2.202 60 10/5/04 11:00 -2.527 -0.204 0.034 0.000 6.160 60 10/5/04 12:00 -2.703 -0.203 0.088 0.000 37.423 60 10/5/04 13:00 -2.492 -0.204 0.040 0.000 10.909 60 10/5/04 14:00 -2.865 -0.205 0.028 0.000 4.611 60 10/5/04 15:00 -2.893 -0.203 0.021 0.000 3.974 60 10/5/04 16:00 -2.914 -0.226 0.034 0.000 3.613 60 10/5/04 17:00 -3.259 -0.221 0.037 0.000 2.483 60 10/5/04 18:00 -3.230 -0.210 0.085 0.000 6.739 60 10/5/04 19:00 -2.741 -0.214 0.004 0.000 2.249 60 10/5/04 20:00 -2.363 -0.206 0.004 0.000 4.792 60 10/5/04 21:00 -2.353 -0.193 0.005 0.000 4.783 60 10/5/04 22:00 -2.391 -0.193 0.004 0.000 4.158 60 10/5/04 23:00 -2.324 -0.209 0.004 0.000 4.825 60 10/6/04 0:00 -1.971 -0.216 0.004 0.000 11.519 60 10/6/04 1:00 -1.818 -0.222 0.007 0.000 15.696 60 10/6/04 2:00 -1.985 -0.212 0.007 0.000 10.679 60 10/6/04 3:00 -1.809 -0.207 0.009 0.000 16.137 60 10/6/04 4:00 -2.008 -0.200 0.007 0.000 10.332 60 10/6/04 5:00 -2.218 -0.211 0.005 0.000 6.185 60 10/6/04 6:00 -2.223 -0.212 0.005 0.000 6.092 60 10/6/04 7:00 -2.064 -0.210 0.006 0.000 8.914 60 10/6/04 8:00 -2.177 -0.210 0.006 0.000 6.930 60 10/6/04 9:00 -2.329 -0.223 0.008 0.000 4.960 60 10/6/04 10:00 -2.939 -0.229 0.101 0.000 5.759 60 10/6/04 11:00 -2.593 -0.227 0.027 0.000 5.761 60 10/6/04 12:00 -1.781 -0.218 0.017 0.000 22.084 60 10/6/04 13:00 -1.796 -0.213 0.021 0.000 23.115 60 10/6/04 14:00 -1.656 -0.213 0.023 0.000 31.287 60 10/6/04 15:00 -2.096 -0.216 0.029 0.000 16.166 60 10/6/04 16:00 -2.059 -0.219 0.016 0.000 15.103 60 10/6/04 17:00 -2.142 -0.227 0.044 0.000 16.901 60


102

Appendix B- Wet & Dry pixel information with SEBAL constants

Coordinates of Wet Pixel

Coordinates of Dry Pixel

Temp Wet-pixel

Temp Dry-pixel

SEBAL Constants

Date

X (m) Y(m) X(m) Y(m) (K) (K) a b

2/14/2002 339,781 5,770,950 336,210 5,768,792 277.75 282.64 -282.31 1.02

8/16/2002 340,022 5,771,821 336,452 5,768,311 300.12 317.56 -274.47 0.91

9/8/2002 340,441 5,773,501 338,612 5,773,740 287.94 303.28 -240.05 0.83

5/8/2003 338,700 5,770,411 336,629 5,771,192 293.49 310.31 -203.74 0.69

5/31/2003 338,606 5,772,093 345,929 5,772,002 300.99 317.05 -287.73 0.96

8/3/2003 340,504 5,770,561 337,109 5,768,523 302.3 316.46 -393.49 1.30

4/15/2004 338,102 5,770,292 338,522 5,773,441 292.91 307.57 -253.97 0.87


103

Appendix C- S-SEBI constants obtained from reflectance- temperature plots

Date aH bH aλλλλE bλλλλE

02/14/2002 288.0 -39.5 274.7 22.5

08/16/2002 323.0 -42.5 291.8 36.5

09/08/2002 308.0 -37.5 282.5 22.5

05/08/2003 323.2 -56.0 288.5 20.0

05/31/2003 331.8 -55.5 294.3 24.5

08/03/2003 333.5 -85.5 296.5 25.0

04/15/2004 328.5 -109.0 285.0 26.5


104

Appendix D- Calculation of reference evapotranspiration (ET0)

1) FAO Penman-Monteith equation:

)34.01(

)(273

900)(408.0

2

2

0 u

eeuT

GRET

asn

++∆

−+

+−∆=

γ

γ (D-1)

ET0 = Reference evapotranspiration (mmday-1) Rn = Net Radiation (MJm-2day-1) G = Soil heat flux density (MJm-2day-1) T = Air temperature at 2m height (0C) U2 = Wind speed at 2m height (ms-1) es = Saturation vapour pressure (kPa) ea = Actual vapour pressure (kPa) es-ea = Saturation vapour pressure deficit (kPa) ∆ = Slope of vapour pressure curve (kPa 0C-1) γ = Psychrometric constant (kPa 0C-1) 2) Modified Makkink equation:

↓+∆

∆= KETv )(

65.00 γλ (D-2)

↓K = Incoming Solar radiation (MJm-2day-1)

vλ = Latent heat of vaporization (MJkg-1)


105

Appendix E- ILWIS scripts for SEBAL & S-SEBI algorithms.

Calculation of Latitude Longitude & Solar Zenith Angle Latitude{dom=VALUE.dom;vr=-180.0000:180.0000:0.00001}:=iff(Ref_b1>0,crdy(transform(mapcrd(Ref_b1),latlon)),0) Longitude{dom=VALUE.dom;vr=-180.0000:180.0000:0.00001}:=iff(Ref_b1>0,crdx(transform(mapcrd(Ref_b1),latlon)),0) Sun_HA{dom=VALUE.dom;vr=-4:4:0.00001}:=acos(-tan(Latitude*pi/180)*tan(0.2441596)) LAT{dom=VALUE.dom;vr=0:24:0.00001}:= 10.776+longitude/15-0.0205*229.18/60 Omega{dom=VALUE.dom;vr=-180:180:0.00001}:=15*(LAT-12)*pi/180 COSTEETA{dom=VALUE.dom;vr=-1:1:0.00001}:=sin(latitude*pi/180)*sin(0.2441596) +cos(latitude*pi/180)*cos(0.2441596)*cos(Omega) Calculation of Broad band albedo and biophysical parameters alb_bb.mpr{dom=value;vr=0:1.00:0.0001} :=0.484*Ref_b1+0.335*Ref_b3N-0.324*Ref_b5+0.551*Ref_b6+0.305*Ref_b8-0.367*Ref_b9-0.0015 NDVI.mpr{dom=value;vr=-1.00:1.00:0.0001} := (Ref_b3N-Ref_b2)/(Ref_b3N+Ref_b2) WDVI.mpr{dom=value;vr=-10:1:0.001} :=Ref_b3N-1.1*Ref_b2 L.mpr{dom=value;vr=0:1:0.001} = Iff((1-2*1.6*NDVI*WDVI)<0,0,1-2*1.6*NDVI*WDVI) SAVI.mpr{dom=value;vr=-1:10:0.0001} =Iff((Ref_b3N-Ref_b2)<0, 0.00001, (Ref_b3N-Ref_b2)*(1+L)/(Ref_b3N+Ref_b2+L)) LAI.mpr{dom=value;vr=0:10:0.000001} =Iff((SAVI-0.13)/0.35<0,0.00001,(SAVI-0.13)/0.35) Calculation of Surface Energy balance Components Kin_exo{dom=value;vr=0.0000:1500:0.0001} := 1367*0.975*COSTEETA Lout{dom=value;vr=0.0000:1500:0.0001} := 5.667*(10^-8)*bb_emis*(Surf_Temp)^4+(1-bb_emis)*364.4 Rnet{dom=value;vr=0.0000:1500:0.0001} := (1-alb_bb)*Kin_exo*0.667-Lout+364.4 G{dom=value;vr=0.0000:1500:0.0001} :=Rnet*(Surf_Temp-273)/(100*alb_bb)*(0.32*1.1*alb_bb+0.62*(1.1*alb_bb)^2)*(1-0.978*(NDVI)^4) fPAR.mpr{dom=value;vr=-10:10:0.001} =iff(NDVI>-0.126,(-0.161+1.275*NDVI),0) Zom.mpr{dom=value;vr=0:100:0.001} = Exp(-5.5+5.8*NDVI) disp_h.mpr{dom=value;vr=0:1:0.00000001} := 0.3*(1-(1-exp(-1*(20.6*LAI)^0.5))/(20.6*LAI)^0.5) U_b.mpr{dom=value;vr=0:100:0.0001} :=2*(ln(100-disp_h)-ln(Zom))/(ln(10-disp_h)-ln(Zom)) Calculation of Sensible Heat Calculation Iteration-1 Ustar.mpr{dom=value;vr=0:10:0.0001}:=0.41*3.2/ln((100-disp_h)/Zom) rah_0.mpr{dom=value;vr=0:200:0.0001} =ln((10-disp_h)/0.002)/(0.41*Ustar) del_t.mpr{dom=value;vr=-100:100:0.0001}:=-274.4751+0.91455118*Surf_Temp H_0.mpr{dom=value;vr=0:1000:0.01}:=Iff(del_t>0,1.12*1004.16*del_t/rah_0,0.1)


106

L_0.mpr:=-1.12*1004.16*(Ustar)^3*Surf_Temp/(0.41*9.81*H_0) Xm.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(100-disp_h)/L_0)^0.25 Xh.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(5-disp_h)/L_0)^0.25 psi_m.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xm)/2)+ln((1+Xm^2)/2)-2*atan(Xm)+pi/2 psi_h.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xh^2)/2) Iteration-2 Ustar1.mpr{dom=value;vr=0:10:0.0001}:=0.41*3.2/(ln((100-disp_h)/Zom)-psi_m) rah_1.mpr{dom=value;vr=0:200:0.0001} =(ln((5-disp_h)/0.002)-psi_h)/(0.41*Ustar1) H_1.mpr{dom=value;vr=0:1000:0.0001}:=Iff(del_t>0,1.12*1004.16*del_t/rah_1,0.1) L_0.mpr:=-1.12*1004.16*(Ustar1)^3*Surf_Temp/(0.41*9.81*H_1) Xm.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(100-disp_h)/L_0)^0.25 Xh.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(5-disp_h)/L_0)^0.25 psi_m.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xm)/2)+ln((1+Xm^2)/2)-2*atan(Xm)+pi/2 psi_h.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xh^2)/2) Iteration-3 Ustar2.mpr{dom=value;vr=0:10:0.0001}:=0.41*3.2/(ln((100-disp_h)/Zom)-psi_m) rah_2.mpr{dom=value;vr=0:200:0.0001} =(ln((5-disp_h)/0.002)-psi_h)/(0.41*Ustar2) H_2.mpr{dom=value;vr=0:1000:0.0001}:=Iff(del_t>0,1.12*1004.16*del_t/rah_2,0.1) L_0.mpr:=-1.12*1004.16*(Ustar2)^3*Surf_Temp/(0.41*9.81*H_2) Xm.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(100-disp_h)/L_0)^0.25 Xh.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(5-disp_h)/L_0)^0.25 psi_m.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xm)/2)+ln((1+Xm^2)/2)-2*atan(Xm)+pi/2 psi_h.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xh^2)/2) Iteration-4 Ustar3.mpr{dom=value;vr=0:10:0.0001}:=0.41*3.2/(ln((100-disp_h)/Zom)-psi_m) rah_3.mpr{dom=value;vr=0:200:0.0001} =(ln((5-disp_h)/0.002)-psi_h)/(0.41*Ustar3) H_3.mpr{dom=value;vr=0:1000:0.0001}:=Iff(del_t>0,1.12*1004.16*del_t/rah_3,0.1) L_0.mpr:=-1.12*1004.16*(Ustar3)^3*Surf_Temp/(0.41*9.81*H_3) Xm.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(100-disp_h)/L_0)^0.25 Xh.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(5-disp_h)/L_0)^0.25 psi_m.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xm)/2)+ln((1+Xm^2)/2)-2*atan(Xm)+pi/2 psi_h.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xh^2)/2) Iteration-5 Ustar4.mpr{dom=value;vr=0:10:0.0001}:=0.41*3.2/(ln((100-disp_h)/Zom)-psi_m) rah_4.mpr{dom=value;vr=0:200:0.0001} =(ln((5-disp_h)/0.002)-psi_h)/(0.41*Ustar4) H_4.mpr{dom=value;vr=0:1000:0.0001}:=Iff(del_t>0,1.12*1004.16*del_t/rah_4,0.1) L_0.mpr:=-1.12*1004.16*(Ustar4)^3*Surf_Temp/(0.41*9.81*H_4) Xm.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(100-disp_h)/L_0)^0.25 Xh.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(5-disp_h)/L_0)^0.25 psi_m.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xm)/2)+ln((1+Xm^2)/2)-2*atan(Xm)+pi/2 psi_h.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xh^2)/2)


107

Iteration-6 Ustar5.mpr{dom=value;vr=0:10:0.0001}:=0.41*3.2/(ln((100-disp_h)/Zom)-psi_m) rah_5.mpr{dom=value;vr=0:200:0.0001} =(ln((5-disp_h)/0.002)-psi_h)/(0.41*Ustar5) H_5.mpr{dom=value;vr=0:1000:0.0001}:=Iff(del_t>0,1.12*1004.16*del_t/rah_5,0.1) L_0.mpr:=-1.12*1004.16*(Ustar5)^3*Surf_Temp/(0.41*9.81*H_5) Xm.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(100-disp_h)/L_0)^0.25 Xh.mpr{dom=value;vr=0:10:0.0001}:=(1-16*(5-disp_h)/L_0)^0.25 psi_m.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xm)/2)+ln((1+Xm^2)/2)-2*atan(Xm)+pi/2 psi_h.mpr{dom=value;vr=0:10:0.0001}:=2*ln((1+Xh^2)/2) Calculation of Daily ET - SEBAL L_E.mpr{dom=value;vr=0:700:0.0001}:=Iff((Rnet-G-H_5)<0,0, Rnet-G-H_5) Evp_Frac.mpr{dom=value;vr=0:1:0.0001}:= L_E/(Rnet-G) K_exo_d{dom=VALUE.dom;vr=0.00:1800.00:0.0001}:=24/pi*1367*0.0036*0.975*sin(Latitude*pi/180)*sin(0.2441596)*(Sun_HA-tan(Sun_HA)) Transmit_d.mpr{dom=value;vr=0:1:0.00001}:=259.84/(11.5741*K_exo_d) K_day.mpr{dom=value;vr=0:1000:0.0001}:=11.5741*Transmit_d*K_exo_d Rnet_d.mpr{dom=value;vr=-100:1000:0.0001}:=(1-1.1*alb_bb)*K_day-68.81 E_24_SEBAL.mpr{dom=value;vr=0:10:0.01}:= Iff(Rnet_d<0,0,Evp_Frac*Rnet_d/28.588)

Calculation of Daily ET – S-SEBI E_F.mpr{dom=value.dom;vr=0:1:0.0001} :=Iff((323-42.5*alb_bb-Surf_Temp)/(323-291.8+(-42.5-36.5)*alb_bb)>1,1,Iff((323-42.5*alb_bb-Surf_Temp)/(323-291.8+(-42.5-36.5)*alb_bb)<0,0,(323-42.5*alb_bb-Surf_Temp)/(323-291.8+(-42.5-36.5)*alb_bb))) H{dom=value;vr=0.0000:1500:0.0001} := (1-E_F)*(Rnet-G) LE{dom=value;vr=0.0000:1500:0.0001} := E_F*(Rnet-G) E_24_SEBI.mpr{dom=value;vr=0:10:0.01}:= Iff(Rnet_d<0,0,E_F*Rnet_d/28.588


108

Appendix F- Comparison of actual ET (mmd-1) - SEBAL vs. S-SEBI


109


110

Appendix G- Temporal variation of daily actual ET in Grass area


111

Appendix H- Temporal variation of daily actual ET in Maize area


112

Appendix I - Temporal variation of daily actual ET in Wooded area


113

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