MASARYK UNIVERSITY
Faculty of Science Department of Geography
Roman BOHOVIC
MODELLING EVAPOTRANSPIRATION AT DIFFERENT SCALES BY THE MEANS OF REMOTE SENSING
Diploma thesis
Supervisor: doc. RNDr. Petr Dobrovolný, CSc. ______Brno 2009 Author: Roman Bohovic Title of the thesis: Modeling Evapotranspiration at Different Scales by the Means of Remote Sensing Title in Slovak: Modelovanie evapotranspirácie v rôznych merítkach postupmi diaľkového prieskumu Zeme Programme of studies : Geographical Cartography and Geoinformatics Supervisor: doc. RNDr. Petr Dobrovolný, CSc. Year: 2009
Annotation Khorezm province (Uzbekistan) lies in Amu Darya delta and is afflicted with the problems caused by the Aral Sea desiccation. Agriculture is the major source of livelihood, but it is fully dependent on the irrigation. Therefore, sustainable water management and sensible use of the water are crucial. The main aim of this study was to provide high-resolution evapotranspiration (ET) data. This was achieved by the modelling of remotely sensed ASTER images. For that, Surface Energy Balance Algorithm for Land (SEBAL) with METRIC modification was applied. The modelling process proved suitability of the data and the method for this purpose. As an output of this study, dataset (7 scenes) of the actual ET for the years 2004 – 2007 was created. In addition, maps of the ET and basic analyses were done upon modelled data at the second part of diploma thesis.
Anotácia Chorazimská oblasť (Uzbekistan) sa nachádza v delte rieky Amudarja a je postihnutá problémami spôsobenými vysychaním Aralského mora. Poľnohospodárstvo je hlavným zdrojom obživy v regióne. Keďže je úplne závislé na zavlažovaní, citlivé zaobchádzanie a trvalo udržateľné hospodárenie s vodou sú veľmi dôležité. Hlavným cieľom tejto štúdie bolo vytvoriť podrobné dáta o evapotranspirácii (ET). To bolo dosiahnuté modelovaním družicových snímkov zo snímača ASTER s použitím metódy SEBAL (Surface Energy Balance Algorithm for Land) s modifikáciou METRIC. Zvolený postup aj vstupné dáta sa ukázali pre účel tejto diplomovej práce ako vhodné. Výsledkom je 7 dátových vrstiev aktuálnej ET v období rokov 2004 – 2007. V závere práce boli na základe vytvorených dát urobené jednoduché analýzy a dáta boli kartograficky vizualizované do výslednej mapy evapotranspirácie.
Keywords: evapotranspiration ○ SEBAL model ○ ASTER ○ Uzbekistan remote sensing ○ raster data modelling ○ GIS analyses Kľúčové slová: evapotranspirácia ○ model SEBAL ○ ASTER ○ Uzbekistan diaľkový prieskum Zeme ○ modelovanie rastrových dát ○ GIS analýza THANKS TO
Dr. Christopher Conrad ○ doc. RNDr. Petr Dobrovolný, Csc. ○ Dr. John Lamers Lucia Šikulincová Avo Tuahrem ○ Berousek ○ Gunter ○ Dano ○ Zuza ○ Lenka ○ Osman ○ Mardon 19 years of education rodičom
I KNOW WHAT FOR
I claim, that I elaborated this diploma thesis under the supervision of my supervisor (doc. Dobrovolný) and consultant (Dr. Conrad) and with some kind of help from the people mentioned above.
May 15th, 2009 Brno ______signature
TABLE OF CONTENTS PREFACE...... 7 1 GENERAL INTRODUCTION...... 9 1.1 Aim of the study...... 9 1.2 The study region: Khorezm Oblast...... 9 1.2.1 Physical geography of the study area...... 11 1.2.2 Socio-economical conditions...... 14 1.2.3 Land cover and agriculture...... 15 1.3 Irrigation and drainage system...... 18 1.4 Evapotranspiration...... 20 2 MODELLING EVAPOTRANSPIRATION...... 22 2.1 Input data...... 22 2.1.1 ASTER data...... 22 2.1.2 Meteorological data...... 26 2.1.3 Land cover and land use data...... 27 2.2 SEBAL and METRIC models...... 28 2.2.1 Physical principals of SEBAL...... 28 2.3 Modelling ET ...... 30 2.3.1 The 1st phase of the model...... 31 2.3.2 The 2nd phase of the model...... 33 2.3.3 The 3rd phase of the model...... 36 2.3.4 The 4th phase of the model...... 40 2.4 ASTER – MODIS result comparison...... 42 2.5 Results and discussion...... 45 3 GEOPROCESSING ANALYSES...... 48 3.1 Patterns of ET distribution...... 48 3.2 Relative differences in the actual ET...... 51 3.3 Discussion...... 52 CONCLUSION...... 54 REFERENCES...... 56 APPENDICES...... 59 ABBREVIATIONS...... 62
6 PREFACE
Uzbekistan is not a particularly popular tourist destination. Its location, strict visa policy and complicated administrative regulations for residence make the visit considerably difficult, especially for Europeans.
Rather by chance, I got involved the Erasmus Mundus External Cooperation Window Lot 9 programme. The aim of this project is to bring European and Central Asian universities together, by way of a mobility scheme accepted by the European Commission (EMEC9, 2009). In February 2008 I arrived to Urgench in Khorezm for one term as an exchange student on the Urgench State University Named after Al-Khorazmy (Al-Xorazmiy Nomli Urganch Davlat Universiteti), see it on the Figure 1.
Figure 1: Entrance to the Urgench State University Named after Al-Khorazmy.
Khorezm province, with Urgench as a capital, is one of the remotest parts of Uzbekistan, landlocked in the Central Asia deserts. Neither Uzbeks nor the foreigners commonly visit this area (except for the Khiva UNESCO world heritage site). In spite of its famous history (it has a reputation once being a centre of intelligence and education) it is very isolated region nowadays. The fame of the Khorezm oasis on the Silk Road has faded since the medieval times. The Khiva Khanate used to be on the crossroads, where not only trade, but also culture and ideas from the east and the west used to mingle.
The present situation in the country is rather difficult and complicated. Poor economical and social conditions are now accented by the global economic crisis. According to the International Crisis Group reports (2009), an authoritarian regime in the country is very severe particularly in this region, moreover accompanied by serious
7 violation of human rights. The human development index of Uzbekistan is 0.702 (in 2005), which is at the 113th place in the world (ADB, 2009). Local conditions in the Khorezm oblast seem to be even worse, due to its social and spatial isolation from the other populated regions.
Besides social problems, Khorezm is afflicted with environmental difficulties as well. It lies in the Aral Sea basin. The lake used to have a moderate effect on the local climate, attenuating extreme frosts during the winter and high temperatures in summer. Because of the ecological disaster in Aral Sea, this positive effect on the local conditions is disappearing. This leads to the lake desiccation, and consequently gives rise to other problems as well (sand storms, salinization, soil degradation, diseases, etc.).
The major problem affecting this area is the water shortage and droughts. As the agriculture is the major source of livelihood, being fully dependent on the irrigation, sustainable water management and sensible use of the river water are crucial for this province. However, there is a lack of awareness of environmental, social and economical impacts of water and energy use. Regardless exploitation of water, perceived as a free abundant resource, and the lack of economical incentives are some of the main reasons of unsustainable water use in region (EEA, 2007).
The environmental, social and economic problems in Khorezm are referred to in the German-Uzbek research project “Economic and Ecological Restructuring of Land and Water Use in the Khorezm Region (Uzbekistan): A pilot Project in Development Research” with a base office in Urgench. It is a long-term (from 2002 to 2011) research project that attempts to offer a sustainable solutions to the Aral Sea water management through a holistic approach, combining technology, policy and institutional options developed in cooperation with local and international stakeholders (ZEF, 2009). Among other things, there has been build a GIS laboratory, within the project office, since the outset of the project in 2002.
Thanks to the project coordinator Dr. John Lamers, I was enabled to do an internship in the GIS laboratory and participated in several field works (e.g. installation of the meteorological station) during my stay in Urgench. Within the framework of my praxis, I started cooperation with Dr. Christoper Conrad (from University of Würzburg) and got into the modelling of evapotranspiration, using the SEBAL (Surface Energy Balance Algorithm for Land). This was followed by ensuing cooperation back in Europe, and also gave rise to the topic of this thesis. During my two week internship at Remote Sensing Unit at the University of Würzburg, Germany in November 2008 and March 2009 I collected useful data and information thanks to the consultations and support of the staff. The final result of my work is an object of this study, written under the supervision of ass. prof. Petr Dobrovolny.
8 1 GENERAL INTRODUCTION
1.1 Aim of the study
This study was carried out within the framework of the UNESCO/ZEF research project. It has an overall objective to increase the economic efficiency of agriculture, while improving the natural ecosystem and its services (ZEF, 2009). As the water is the crucial resource for province moving power – the agriculture, the most of the research deals with this aspect.
In the arid climate with high temperatures, low air humidity and extensive irrigation system, the evapotranspiration is an important component of water cycle. Together with infiltration and leaching, evapotranspiration is the main cause of the water loss. For this reason, it is important to have reliable information about the evapotranspiration. There have not been any ground measurements of evapotranspiration performed till summer 2008, when the Eddy covariance tower started operating in the area. However, this provides only point measurements and it is not available for calculations prior to its installation.
Remotely sensed satellite images represent an optimal opportunity to obtain continuous data for large areas. In the field of evapotranspiration in Khorezm, there has been conceived a crucial work by Conrad et al. (2007), who modelled evapotranspiration data for the whole Khorezm province during the year 2004 with 1 km resolution. This offers a good outline of temporal changes of the ET, as well as its spatial patterns within the region. For a study on a small scale, especially one carried out on the field level, the resolution is, however, not sufficient. Yet, modelling of the evapotranspiration based on the remotely sensed data has been proved as appropriate.
The primary goal of this diploma thesis was to follow the previous work in order to create dataset with a resolution suitable for an analysis on the local scale. Secondly it attempts to provide an opportunity of covering longer time period, as well as an option to compare different conditions among different years.
1.2 The study region: Khorezm Oblast
Uzbekistan lies in the centre of five Central Asian republics (see upper map at Figure 2), that were formed in 1991 (Uzbekistan in the 31st of August) after the Soviet union collapse. Tashkent – the capital – lies in the east part of the republic. It is an inland country, without any access to the see (as Aral see is actually a lake), spread on the area of 448 900 km2. On the northern and western part of the country, there is a long border
9 with Kazakhstan, followed by a border with Turkmenistan in the south. Kyrgyzstan and Tajikistan form eastern border mingled in the Ferghana valley. There is a short border with Afghanistan near Termez in the south. Population of Uzbekistan comprises 27,4 million inhabitants (2007) with 1,4% annual growth and population density 64 persons per km2. From that 36,7 % (2006) is urban population (ADB, 2008).
Administratively Uzbekistan consists of 12 provinces (called “oblast”) and Republic of Karakalpakstan, that has proclaimed autonomy. All provinces differ a lot in size, population and environmental conditions. Oblast's are divided into rayons.
Khorezm oblast with its area of 6050 km2 (Visvar, Kim, 2006) is one of the smallest provinces and lies in a middle west part of the country. It borders with Turkmenistan in the south and west. River Amu Darya forms the border with Republic of Karakalpakstan in the north and east part of the region (see map at Map of the ET in Khorezm (Part 1) in Appendix 1).
Figure 2: Elevation map (m a.s.l.) of Khorezm oblast and overlook map of Central Asian countries and location of Khorezm within Uzbekistan.
The area of interest of this study lies in Khorezm oblast (province). Satellite based modelling was made upon rectangular area defined by available satellite images. For the
10 delimited area there are full cover data available for the whole study period. Area of interest covers 1205,08 km2. Share of agricultural fields is 614,97 km2 (51%). This is depicted by orange-yellow rectangle on the Figure 2 and on the map in Appendix 1 (Part 1, upper half).
Study area covers central part of Khorezm oblast with its capital Urgench. Spurs of the Karakum Desert are reaching the southern parts of rectangle with lakes, that surrounds irrigated area as a ring. North-east part of the study area is crossed by the Amu Darya river flowing from the south-east to the north-west. Area at the right bank of the river administratively belongs to the Karakalpakstan republic with bigger town Beruni, that is out of the study range. Beruni, Amu Darya, Urgench and Karakum Desert spurs are parts of the region that can be easily identified (see Figure 11) within the are of interest due to its different spectral values (larger non-irrigated places) contrasting to the rest of the area.
For some parts of this study, where only a smaller areal extent was needed, area of Koramon WUA was chosen. It is an administrative unit of water users association in central part of Khorezm oblast, fully agriculturally used and irrigated. It is situated to the west, south-west from Urgench (depicted by brown colour on the maps at Figures 10 and 7). Koramon WUA covers area of 31,842 km2.
1.2.1 Physical geography of the study area
Khorezm province is a historical oasis on the left bank of the Amu Darya river. It is a part of its lower floodplain in the river delta to the Aral Sea (or former delta as considering Aral Sea disaster it is disputatious). It is about 225 km far from present shores of Aral Sea (Conrad et al., 2007), however, the distance is rising due to its desiccation.
From the west and south Khorezm borders with Turkmenistan, where Karakum Desert forms the border. Desert spurs are reaching province, that stays arable only thanks to the irrigation water. The biggest desert spur is to the west from Urgench.
The northern and eastern border of the province is formed naturally by the Amu Darya river, that separates two deserts. On the right bank, further from the river, where the land is not irrigated Kyzyl Kum desert proceeds into Karakalpakstan republic.
Relief is very flat with altitude around 100 m above see level. Elevation is slightly decreasing by 30 m in the river direction from south-east to north-west from approximately 115 m to the 85 m above sea level (see left map on the Figure 2) Rolling pattern in the south is caused by sand dunes in desert.
Khorezm is an ancient river delta, which formed its morphology. Nowadays there is
11 a river valley, floodplain and first over-floodplain terrace, old river channels and periphery lakes. Geologically, Khorezm is made up of quaternary deposits that form 20-100 m thick stratum. (Ibrakhimov, 2004)
Climate in Khorezm region is arid and continental, with long hot summers and very cold winter temperatures. Precipitation sums are very low. Aral Sea used to serve as regional buffer, protecting the area from severe Siberian winter winds and high summer temperatures. However, this function is now reduced due to desiccation of the lake. As a consequence, the frost-free period has shortened to 170 days a year, which is insufficient for cotton growing. (Forkutsa, 2006)
Table 1: Mean air temperature, relative humidity, precipitation and potential evaporation for decades - 70's and 90's, mean value for months at the Urgench and Khiva (only for 70's) meteorological station (source: Mukhammadiev, 1982 and Glavgidromet, 1999 both in Ibrakhimov, 2004 ).
1990-2000 Temperature °C -2.2 0.3 5.6 15.6 21.1 27.2 28.2 25.8 19.2 11.9 4.4 -0.3 -2.2 Rel. humidity % 80 73.3 63.9 52.1 49.3 44.5 47.2 50.3 53 58.2 71.6 80.2 80 Precipitation mm 13.7 9.2 12.3 14 6.6 4.3 1.1 2.1 1.7 7.9 10.2 8.3 Σ 91.4 1970-1980 Temperature °C -4.1 -2.3 5 14.6 21.8 27 28 25.8 19.3 11.6 5 -1.4 -4.1 Rel. humidity % 75 73 65.4 55 39 35 41.7 44 46 54 66 77 75 Precipitation mm 5.6 6.5 15.2 19.4 8 3.8 6 1.6 6.4 7 8.1 9.2 Σ 96.8 Pot. evaporation mm 16 21 45 102 194 254 236 208 154 90 44 19 Σ 1383
Long-term mean monthly values of selected meteorological elements are summarized in the Table 1. Mean long-term air temperature is 13.1 °C. During the winter, mean temperature for all months is below zero. January is the coldest month, with average minimum temperature -5.9 °C and mean monthly value of -2.2 °C (Ibrakhimov, 2004). Higher temperatures with mean 21.1 °C starts already in May. Maximum mean temperature in annual course occurs in July, whereas absolute maximum mean July temperature was 35.6 °C (Ibrakhimov, 2004). Evident decline of temperature starts in September. During summer period from May to September there is only 15.8 mm of rainfall on average. Total sum of precipitation is 91.4 mm per year and is concentrated to the beginning and the end of the year. There are significant differences among years in precipitation sums. The maximum rainfall of 173.4 mm was recorded in 1992, in contrast to only 34.8 mm in 1995 (Ibrakhimov, 2004). Low relative humidity – around 50 % - lasts from April to October.
In comparison to the data from the 1970's, we can notice a slight increase of mean temperature and slight decrease of precipitation amount. Rainfall distribution is more
12 spread, with 25.8 mm for 5 months counted for the vegetation season. Potential evapotranspiration for the period 1970-1980 was 1383 mm on average per year and exceeded the rainfall of 96.8 mm more than 14 times.
Figure 3: Long-term trend of the Amu Darya runoff for 1932 to 1999. The Pyandj-Vakhsh confluence is beginning of Amu Darya river, the Kerky water-monitoring station is at the middle river reach, the Tuyamuyun station is at the lower Amu Darya reach (in the Khorezm province) and the Chatly Station is at the end of the Amu Darya (adopted from UNDP, 2007).
The only natural watercourse in Khorezm province is the Amu Darya river, that separates the study area from Karakalpakstan and Kyzyl Kum desert. Amu Darya is bigger than Syr Darya – both tributaries of Aral Sea and the main water resources in Central Asia. Amu Darya is formed by confluence of the Pyandj and Vakhsh rivers on Afghan-Tajik border. The length of Amu Darya is 2540 km, of which about 1000 km lies on the Uzbek territory – along the borders with Afghanistan and Turkmenistan. According to Ibrakhimov (2004), river basin covers 309000 km2 or 1327000 km2 according to UNDP (2007), depending on sources counted. River is fed mainly by melting water from glacier/snow in Pamir and Tian-Shan mountains in Tajikistan and Kyrgyzstan. For this reason, the major run-off (77–80 %; UNDP, 2007) appears during summer vegetation period (April to September). Such a distribution is very favourable for irrigation agriculture (Ibrakhimov, 2004). Total inflow from Amu Darya watershed is 80.5 km3 (UNDP, 2007) or 70 km3 according to Ibrakhimov (2004). Figure 3 presents a long-term annual run-off at 4 water monitoring stations. Because of the aforementioned depletion (due to irrigation) downstream stations recorded lower flow rates since 1960's and 1970's, in spite of the fact, that the river origin has approximately the same run-off for the whole period.
Difficult lateral ground water flow and the prevalence of evaporation over outflow due to dry arid climate, heavy soil textures and stratigraphy of the parent materials are typical for hydrogeology. Ground water table is shallow, strongly affected by irrigation and
13 in 2 – 3 km wide stripe along river banks there is an influence of Amu Darya as well. Precipitation does not have a significant impact on ground water level. On the non- irrigated land shallow ground water tables are intensively evaporated during hot seasons, which results in salinization. (Ibrakhimov, 2004)
As a product of human activity there is a chain of artificial lakes along the periphery of the Khorezm oasis. They are made of waste waters, that are disposed here from drainage system and are therefore saline. (UNDP, 2007)
1.2.2 Socio-economical conditions
Population of Khorezm province has 1410300 inhabitants (Visvar and Kim, 2006), which makes it one of the most densely populated regions in Central Asia with approximately of 233 inhabitants per square kilometre. Most of the people live in rural settlements and they work in agriculture.
Figure 4: Historical centre of the Khiva UNESCO world heritage site.
Administrative capital of the province is Urgench with population of 136800 (Visvar and Kim, 2006). Other main settlements are Pitnyak, Arbek and centres of rayons (smaller administrative units) – Bagat, Gurlen, Koshkupyr, Khazarasp, Khiva, Khanka, Shavat, Yangiaryk, Yangibazar (YDQ, 2007). There are 9 other rayons besides Urgench rayon (whose administrative centre is Korovul). For details please see the administrative map of Khorezm with main settlements on upper part of Map of the ET in Khorezm – Part 1 in Appendix 1.
Especially notable is a town Khiva (Figure 4), former seat of the khan of the Khiva Khanate. It was a centre of one of the three pre-russian medieval states on Uzbek territory. It is a cultural sight and a historical site of UNESCO nowadays. This makes the
14 town one of hotspots of tourism in the country.
Khorezm is a very remote part of Uzbekistan, separated from the other populated regions by two deserts, about 1100 km far away from capital city Tashkent. This fact, together with different historical background (influence of Persian empire) and local dialect different from Uzbek language, makes this province rather isolated with limited trade and knowledge exchange.
There is a dense network of paved and non-paved roads within the province. The main road that leads to the Nukus (Capital of Karakalpakstan) in the north-west and to the Buchara, Samarkand and Tashkent to the east stretches on the other side of the Amu Darya river. Both hight water level and flowing ice during spring complicate to cross the river, since there are only pontoon bridges or ferries available. Railway connection along Amu Darya river that used to connect waste space of Central Asia from Kazakhstan to Termez on Afghan border is crossing Khorezm with the main station in Urgench. After Soviet Union break up the rail became rather inconvenient, as it is crossing the border with Turkmenistan several times. For the same reason, the railway on the other bank of Amu Darya is used more frequently, with station in Turtkul.
There is a developing university in Urgench, yet, the level of studies and chance to find an appropriate occupation after studies is low. Personal computers are not common and internet connection is available only in several places. According to the Asian Development Bank (2008), only 3.08 % (in 2006) of people own a computer and 0.03 % (in 2007) of people have broadband internet connection subscribed in Uzbekistan. Presumably statistics for Khorezm province would be even lower.
1.2.3 Land cover and agriculture
Since the ancient times (4th century BC) Khorezm region has been based on Amu Darya delta, including present Khorezm oblast and neighbouring parts of Turkmenistan and Karakalpakstan. There was a cultivated oasis with intensive human activity, assigning the region into one of the oldest civilizations based on irrigation agriculture. Millet, wheat, barley, watermelons, honey melons, and gourds have been grown in the oasis since historical times. (Forkutsa, 2006)
15 Figure 5: Map of the land use in the area of interest in Khorezm and detail of Koramon WUA for years 2004 – 2007.
Unfortunately, there is a lack of written sources dealing with the agriculture of old times. The first reliable data are from 1926, when 1484.8 km2 of land was arable. This is actually far less in comparison to the current arable area within the Khorezm province. To be precise, only Khorezm oblast itself (though the borders of the province nowadays do not really correspond to the historical borders) has irrigated area 2750 km2 (Ibrakhimov,
16 2004) in the present. In 1926, cotton counted only for 18% (263.08 km2), 15 % (218.34 km2) were under alfalfa and the rest (33%) was occupied mainly with grain crops. In this extent, the agriculture and water use can be considered sustainable. (Atashev et al., 1966 in Zavgorodnyaya, 2006)
Precise information concerning land use and crop pattern in recent years by Zeidler (2009) is available even for area of interest of this work (see Figure 5). The data cover an area of 1205.08 km2, of which half is irrigated. In 2004, cotton was planted on 63% of this territory, but it decreased to the 47% in 2007. On the other hand, wheat (including all wheat categories together) cultivation area increased from 19% to 35%. Pure rice field area shows the highest variability between 15% and 20% (rice sown after wheat is not included). See coulombs for area per crop type on the Figure 25. From these data typical pattern of agriculture in Khorezm is obvious.
Cotton is a traditional crop from the old times, but during the Soviet era agriculture has focused solely on cotton production, taking all the risks of mono-cultivation. This orientation towards the cotton production remains until now, though certain changes are on the move. After claiming of independence, Uzbekistan gained about 20% share on international trade with cotton during the early 1990's. In the years 2004 – 2006, export of cotton fell to only 10% of global trade (USDA, 2006). This was due to decrease of cotton production in the country from 5058 thousand metric tons in 1990 to 3715 in 2007 (ADB, 2008). We can observe the same trend in Khorezm province, with 290 thousand ton yield in 1999 (FAO/WFP, 2000).
Table 2: Water demand per crops during vegetation period in Khorezm (source: HydroModRay, 2002 in Müller, 2006 ).
Cotton Wheat. cereals Rice Fruit and veg. Fodder Other crops Water demand during irrigation period 5.6 4.5 26.2 6.3 6.7 8.4 [1000m3/ha] Since the introduction of winter wheat in the region after 1991 its importance and share on crop growing is permanently rising (from 553 thousand tons in 1990 to 6198 thousand tons in 2007 for whole Uzbekistan; ADB, 2008). This is due to state policy attempting to achieve self-sufficiency in grain production (Ibrakhimov, 2004). It is harvested in mid-July (see cropping calendar on the Figure 6) and hence it is possible to grow another crop afterwards. For its lower water demand (see Table 2) its spread is perceived as more than favourable. On the other hand, Ibrakhimov (2004) is presenting this fact as one of the reasons causing negative changes in the ground water table and salinity.
17 Figure 6: Cropping calendar of cotton, rice and winter wheat in Khorezm (adapted from Schweitzer et al. 2005, in Zeidler, 2009).
The third main crop in Khorezm is rice. Because of favourable settings for rice growing in plain deltaic area, the region was supported in rice-growing during Soviet era. This became very unsafe in connection to water shortages during recent years, as rice water demand is very hight (see Table 2). During drought period in 2000 and 2001 the rice fields has been devastated to a large extent.
1.3 Irrigation and drainage system
Khorezm province is highly dependent on agriculture. Due to arid climate, the whole arable land needs to be irrigated. Total irrigated area within Khorezm oblast is about 2750 km2 (Ibrakhimov, 2004), while only 2400 km2 is suitable for irrigation (Forkutsa, 2006). This is confirming statement of Müller (2006), that the system of plant production in Uzbekistan is operating close to or already above a naturally given capacity limit. From a global point of view it is therefore unlikely that the agricultural areas can be expanded.
In Khorezm region, the oasis on Amu Darya river has history of irrigation for hundreds of years. Archaeological research proved that the first irrigation channels were build in the middle of 2nd century BC, when farming based on natural flooding was developing (Zavgorodnyaya, 2006). Before 1930's simple water-wheal system allowed farmers to divert only small amounts of water. However, with the construction of gravity- driven water head-gates in 1940s, the water use has sharply increased (Ibrakhimov, 2004). During soviet era, large areas of desert were assigned for cotton irrigation. This boosted process of building of an extensive irrigation system in Khorezm province that is still in use without any major repairs till these days.
18 Figure 7: Map of Khorezm with irrigation channels network (to the inter-farm level) and drainage network and detail with Koramon WUA with irrigation channels (to the intra-farm level) and drainage network.
Base of the system is comprised of dense network consisting of 16233 km (16115 km according to Ibrakhimov, 2004) of irrigation channels. They are hierarchically constructed on 3 levels: the main one, inter-farm and on-farm level (see maps at the Figure 7). Through the irrigation channels water is distributed from six major inlets on Amu Darya into the region. It is driven mainly by gravity forces, supported by pumps where needed. By this mechanism, Khorezm receives annually between 3.5 – 5 km3 of water, from that 95% is for agriculture. (Conrad et al., 2007)
Along with irrigation channel network, drainage system is built. It consists of 7679 km (9254.9 km according to Ibrakhimov, 2004) of collectors (see map at the Figure 7). They are used to collect and transport drainage water out of the irrigated fields into numerous small lakes located on the periphery of the region (Ibrakhimov, 2004). Most of the drainage water is than diverted by Ozernyj collector – Družba to the Sarykamish Depression south from Aral Sea on the border with Turkmenistan, where a big saline lake
19 is growing.
Leaching is a common praxis in the region. It is the leading method for removing the soluble salts from the soil profile on a large scale and in a short-time period. Approximately 15 cm of leaching water is distributed among the agricultural fields (Ibrakhimov, 2004). Salts in the soil that are dissolved and washed away by water, subsequently caught and diverted by drainage. This procedure takes place in November or before vegetation period.
Water is often led to the fields in open, non-lined channels. Because of that, the losses caused by evaporation and groundwater discharge are estimated to be 40% or even more (Martius et al. 2004 in Conrad et al., 2007).
Figure 8: Winter wheat fields and leaching process on the fields on the left, March 2009.
1.4 Evapotranspiration
Evapotranspiration (ET) is a complex process, that refer to the interaction between the land surface and atmosphere. It consists from two distinguishable parts – from the transpiration and evaporation. Evaporation accounts for the movement of the water from the surface (the sum from soil, canopy and water bodies) to the air. Transpiration is a process within the plants, that leads to the lost of the water in the vapor state. See Figure 9 for illustration. (Wiki, 2009)
Evapotranspiration is important for cooling of the plants. Approximately 50% of the incoming energy from the sun is consumed by evapotranspiration of vegetation cover (Procházka et al., 1998).
20 Figure 9: Illustration of the evapotranspiration process (source: Wiki, 2009).
Potential evapotranspiration represents environmental demand for evapotranspiration. It is equal to the evapotranspiration rate of a short green crop, completely shading the ground, of uniform height (typically alfalfa) and with adequate water status in the soil profile (Wiki, 2009). See Table 1 for values of potential ET in Khorezm, that exceeds actual ET by 14 times.
Figure 10: Irrigated rice fields in Urgench, June 2008.
21 2 MODELLING EVAPOTRANSPIRATION
2.1 Input data
For the modelling process of the evapotranspiration in regional scale using SEBAL and METRIC models multispectral satellite data are the principal input. For this purpose ASTER images were used. Moreover, certain additional ground meteorological data are required. Basically, no land use knowledge is required for the SEBAL, yet, it is helpful for an anchor point selection. It has been used for subsequent processing and interpretations as well.
2.1.1 ASTER data
The Advanced Spaceborne Thermal Emission and Refection Radiometer (ASTER) is a high spatial resolution, multispectral imager with along-track stereo capabilities flying on a Terra satellite. It is a cooperative project of NASA, Japan's Ministry of Economy, Trade and Industry and Japan's Earth Remote Sensing Data Analysis Centre (JPL, 2004).
Terra is a part of NASA's Earth Observing System (EOS). Terra was launched in December 18, 1999 into sun-synchronous Earth orbit in 705 km altitude with inclination 98.3 degrees from the Equator. Orbit period is 98.88 minutes and orbit repeat cycle is 233, what means that temporal resolution is 16 days. It started sending data back to earth in February 2000. Terra carries five scientific instruments: ASTER, CERES, MISR, MODIS and MOPITT. (JPL, 2004)
Table 3: ASTER instrument specification (source: Abrams, 2000).
VNIR SWIR TIR Spatial resolution [m] 15 30 90 Data rate [Mbps] 62 23 4 Cross-track pointing [degrees] ± 24 ± 8,55 ± 8,55 Cross-track pointing [km] ± 318 ± 116 ± 116 Swath width [km] 60 60 60 Quantization [bits] 8 8 12 Stereo Yes No No ASTER as a hight resolution multispectral device enables a plenty of applications not only in the field of science. One of ASTER’s primary goals is to obtain a cloud-free map of the land surface, while its stereo capability can be used to generate high-resolution digital elevation models (Abrams, 2000). According to JPL (2004), it is typically used in the field of the land surface climatology, vegetation and ecosystem dynamics, volcano monitoring, hazard monitoring, hydrology, geology and soils, land surface and land cover change. The
22 range of possible applications is unlimited.
The ASTER instrument consists of three separate subsystems: Visible and Near Infrared (VNIR), the Shortwave Infrared (SWIR), and the Thermal Infrared (TIR) system. Each one operates in a different spectral range (see Table 4) using its own telescope (see Table 3). Each instrument was constructed by different Japanese company.
Table 4: ASTER spectral bands and their radiometric resolution (source: Abrams, 2000).
BAND BANDWIDTH [μm] VNIR 1 0.520 0.600 2 0.630 0.690 3 0.760 0.860 SWIR 4 1.600 1.700 5 2.145 2.185 6 2.185 2.225 7 2.235 2.285 8 2.295 2.365 9 2.360 2.430 TIR 10 8.125 8.475 11 8.475 8.825 12 8.925 9.275 13 10.250 10.950 14 10.950 11.650 The VNIR subsystem operates on visible and near infrared wavelengths from 0.52 to 0.86 μm divided to three spectral bands (see Figure 11). It has the highest resolution of 15 m. It consists of two telescopes. One is nadir-looking with a three-spectral-band detector, and the other one is backward-looking with a single-band detector. The backward-looking telescope provides an additional view of the target area in Band 3 for stereo observations. Nadir-looking detector is calibrated by two on-board halogen lamps. (JPL, 2004)
The SWIR subsystem operates on the near-infrared wavelengths from 1.6 to 2.43 μm divided to the six spectral bands. The only nadir-looking telescope provides 30 m resolution. There is parallax error of about 0.5 pixels per 900 m of elevation emerges due to the size of the detector. Still, it can be fairly corrected if elevation data are available. For the calibration lamps are used similarly to the VNIR detector. (JPL, 2004)
The TIR subsystem operates on the thermal infrared wavelengths from 8.125 to 11.65 μm divided to the five spectral bands. Nadir-looking telescope has a fixed-position with a resolution of 90 m. TIR instrument contains a scanning mirror that oscillates at about 7 Hz and each band uses 10 detectors in a staggered array with optical bandpass filters over each detector element. Because of the instrument's high data rate there are
23 some restrictions imposed so that the average data rate is manageable by the spacecraft data management system. (JPL, 2004)
Basic ASTER instrument specification can be found in the Tables 3 and 4, for more details see ASTER web page (JPL, 2004).
Figure 11: ASTER multispectral image for the area of interest with Koramon WUA marked. Extends of the scenes from the particular years are illustrated in colours.
ASTER is an on-demand instrument, which means that the data are only acquired on request in a specific area and time. Once the image is scanned it is available from the managing authority. There are several levels of data products dependent on the degree of processing. A list of standard ASTER products is in the Table 5.
24 While 0 Level data are without any calibration and referencing, in full resolution, 1A Level data are reconstructed, unprocessed instrument data at full resolution. Moreover, they are time referenced and annotated with ancillary information, that are not primarily applied to the data (such as including radiometric and geometric calibration coefficients and georeferencing parameters). Data are on 8 bits in units of digital counts or digital numbers (DN). To the 1B Level data there are already radiometric and geometric coefficients applied in order to calibrate them to radiance-at-the-sensor values. Then data are provided in units of radiance (W/m2/sr/μm). These are raw data levels. From the 2nd Level, products offer a specific physical variables. (Abrams, 2000)
For the modelling of evapotranspiration, which is described in chapter 2, 1B Level data were used (see Figure 11). They were provided by the Remote Sensing Unit at the University of Wűrzburg, Germany.
Table 5: ASTER standard products (source: JPL, 2004).
LEVEL PRODUCT NAME DESCRIPTION Image data plus radiometric and geometric coefficients. Data are 1A Radiance at sensor separated by telescope 1B Registered radiance at sensor 1A data with radiometric and geometric coefficients applied Expedited L1AE data product created from ASTER Expedited Level-0. 1AE Radiance at sensor Image data plus radiometric and geometric coefficients. Data are separated by telescope Expedited L1BE data product created from ASTER Expedited Level-1AE. 1BE Registered radiance at sensor 1AE data with radiometric and geometric coefficients applied 2 AST06 Decorrelation stretch Enhanced color composites for each telescope 2 AST04 Brightness temperature Radiance at the sensor converted to temperature 2 AST09 Surface radiance – VNIR, SWIR Radiance corrected for atmospheric effects 2 AST09T Surface radiance – TIR Radiance corrected for atmospheric effects 2 AST07 Surface reflectance – VNIR, SWIR Derived from surface radiance with topographic corrections Temperature-emissivity separation algorithm applied to atmospherically 2 AST08 Surface kinetic temperature corrected surface radiance data. Temperature-emissivity separation algorithm applied to atmospherically 2 AST05 Surface emissivity corrected surface radiance data. Classifies each pixel of polar scenes into one of eight classes: water AST13 Polar Surface and Cloud 3 cloud, ice cloud, aerosol/dust, water, land, snow/ice, slush ice, and Classifiction shadow. AST14 Digital Elevation Model DEM produced by stereo correlation of nadir and aft Band 3 data 3 AST14OTH Orthorectified 15 orthorectified L1B radiance images, in GeoTiff AST14DMO Orthorectified + DEM 15 orthorectified L1B images + DEM For the subsequent use in the model, there was atmospheric correction carried out on the TIR bands. ENVI 4.3 software was used for this purpose and its function (Basic Tools → Preprocessing → Calibration Utilities → Thermal ATM Correction) with empirical constants determined by University of Wűrzburg. Software (Basic Tools → Preprocessing → Calibration Utilities → Calculate Emissivity → Emissivity Normalization) was used to
25 compute land surface temperature (LST). All spectral bands – VNIR, SWIR, TIR and LST were stacked to one GeoTiff Image that was an input for modelling.
For the purpose of this study, data from 7 different time spells during the years 2004 – 2007 were used. They represent early (May/June) and later (July) phase of vegetation period in the Khorezm for each year. The early period for 2004 was missing. In initial phases of work and modelling, data from March 2004 were included. Because of the very early phase of the vegetation period (see Figure 6), most of the fields is bare in March. Such conditions require different settings and modelling approach. Due to this variances, mid-results from March 2004 scene did not fit in the expected values, hence, the 2004 scene was omitted from the study and further modelling. Most of the 7 scenes (except July 2006 scene) originate from two different ASTER images (covering north and south part of area of interest), that were processed and modelled separately. Only after getting result of actual ET, 13 images were merged and clipped to the final 7 scenes.
2.1.2 Meteorological data
One of the most important advantages of evapotranspiration modelling by the means of remote sensing is minimum ground data requirements. Still, at least a few basic meteorological characteristics from the area of interest are needed. These include air temperature, relative humidity, wind speed in 2 m high and net radiation, which can all be measured at meteorological station.
Figure 12: Building of meteorological station on the edge of the Karakum Desert and irrigated fields (photo: author).
Reference evapotranspiration was computed from ASCE standardized reference evapotranspiration equation (ASCE, 2000) and ground heat flux was computed from FAO- 56 (Conrad et al., 2007).
26 Being one of the modelling inputs, meteorological data are expected to be representative for the whole image. Therefore, the meteorological station needs to be within image area. The selection of station is limited by the local facilities. Reliable network of meteorological stations has been built up just recently within the framework of UNESCO/ZEF research project (see Figure 12). In this work, data from 3 stations (Table 6) were used with regard to data availability. Since at least two images per acquiring date are usually used, similarly two different meteorological stations (each per single scene) are needed. Due to the Khiva station being closed at the end of the year 2006, Shomohulum meteorological station was included.
Table 6: Meteorological stations used in this work.
METEOSTATION USED FOR YEARS OPERATING start end Khiva 2004, 2005, 2006 01.01.03 06.12.06 Shomohulum 2007 01.05.07 17.04.08 Yangibazar 2004, 2005, 2006, 2007 01.01.03 -
2.1.3 Land cover and land use data
In general, precise and up-to-date information about agricultural fields and crop patterns are rather difficult to obtain. Khorezm is a complicated province with an overall lack of reliable information and with this respect a recent work of Zeidler (2009), comprising land cover/land use map of Khorezm region for the years 2004 – 2007 is of note. These data – fields with land use information for each year between 2004 – 2007 were available in Shapefile format (see Figure 10).
Land use data were produced for irrigated areas in a part of Khorezm intended for agriculture. Land use processing was based on a two-stage bi-temporal ASTER classification, combining two pixel-based pre-classifications with SPOT segmented field boundaries and expert knowledge into an object-based rule set. Accuracy of these data was tested within the same study by ground survey and reaches values of 84%, 81% and 86% for the years 2004, 2006 and 2007 respectively (Zeidler, 2009). Suitability of data for this study is emphasized by the fact, that both have been based on the same satellite images.
Land use data are not obligatory for SEBAL/METRIC model, but they are helpful for anchor pixels selection (see Chapter 2.3.3). The principal application of land use data for studying temporal, spatial and crop distribution patterns is in a phase of further analyses.
27 2.2 SEBAL and METRIC models
Surface Energy Balance Algorithm for Land (SEBAL) is a name for an algorithm originally developed by Dr. Wim Bastiaanssen from the Netherlands (Bastiaanssen et al., 1998). It is primarily used to compute an actual evapotranspiration and the other related crop characteristics (water consumption, biomass production, 24-hour and a seasonal evapotranspiration, etc.). It is based mainly on remotely sensed data with minimum ground data requirements. During more than 15 years of development it has been used worldwide in projects with various geographical and meteorological conditions. Mapping Evapotranspiration with Internalized Calibration - METRIC is an essential improvement of SEBAL, made by Dr. Rick Allen (Allen et al., 2007) from the University of Idaho (SNA, 2009).
This thesis study deals with evapotranspiration computing by means of remote sensing data and methods in Khorezm region. It follows and complements the work of Dr. Christopher Conrad (Conrad et al., 2007). It stems from the basics of SEBAL algorithm (Bastiaanssen et al., 1998, Bastiaanssen, 2000) and METRIC model (Allen et al., 2007) as it is described in the next chapters and follows the very useful manual of Idaho implementation of SEBAL (Allen et al., 2002).
2.2.1 Physical principals of SEBAL
SEBAL model is built upon the fact, that satellites measure surface reflectance (and surface temperature) of the ground. Therefore, evapotranspiration is computed from the surface energy balance equation. This is:
λET = Rn – G – H, (1)
2 where λET is the latent heat flux [W/m ], Rn is the net radiation flux at the surface [W/ m2], G stands for the soil heat flux [W/m2], and H is the sensible heat flux to the air [W/ m2]. This is illustrated in the Figure 13. It shows that the ET is computed as a residual component after subtracting the soil and sensible heat fluxes from all available energy budget in the system (net radiation). This is the energy used for evaporation from the soil and transpiration of plants (Allen et al., 2007). Since the satellites provide information for the overpass time only, SEBAL computes an instantaneous ET flux for the image time for the whole scene (Allen et al., 2002).
28 Figure 13: Surface energy balance (source: www.sebal.us [6th April, 2009]).
All radiant energy available at the surface is the net radiation flux (Rn). It is computed as a sum of all incoming and outgoing radiant fluxes expressed by an equation: Rn = RS↓ - α RS↓ + RL↓ - RL↑ - (1-εo)RL↓ (2)
2 where RS↓ is the incoming shortwave radiation [W/m ], α is the surface albedo 2 [dimensionless], RL↓ is the incoming longwave radiation [W/m ], RL↑ is the outgoing 2 longwave radiation [W/m ], and εo is the surface thermal emissivity [dimensionless]. The basic principle of a radiation transfer (Strahler & Strahler, 2006) between the atmosphere (from the sun) and the surface is reckoned in the equation. For illustration see the Fig.14.
The shortwave incoming radiation (RS↓) from the sun is partially reflected back to the atmosphere (as the function of the surface albedo α) and to a certain extent it is absorbed by surface. Incoming longwave radiation (RL↑) is similarly partially reflected back
(as the function of surface thermal emissivity εo) and absorbed by the surface. The surface itself is a source of the outgoing longwave radiation (RL↑) which is emitted to the atmosphere (Allen et al., 2002).
Net radiation – sum of the available energy on the surface is consumed by several energy fluxes (equation 1). When the heat is directly conducted from the ground to the adjacent air, it flows from the surface to the atmosphere. This is the sensible heat flux (H). Similarly, the ground heat flux (G) conducts energy back to the surface. It is used for warming soil and vegetation (simplified to the ground in the SEBAL). The rest of the available energy is so called latent heat flux (λET) – an energy consumed by the evapotranspiration from plants and soil (Strahler & Strahler, 2006).
29 Figure 14: Shortwave and longwave radiation at surface (source: Allan et al., 2002).
In SEBAL, the net radiation is computed from satellite-measured reflectance and the surface temperature. The soil heat flux is estimated from the net radiation, the surface temperature and vegetation indices. In METRIC, the sensible heat flux is estimated from surface temperature range, surface roughness and wind speed buoyancy (Allan et al., 2007).
All the particular modelling steps are described in the following chapter.
2.3 Modelling ET
SEBAL and METRIC models are computing the results stepwise in several partial computations. According to SNA (2009) there are 25 computational steps, described and developed at Allan et al. (2002). To run these stand-alone models for different images is extremely time-consuming. To meet the purpose of this thesis it would be needed to carry out 25 models for 14 images which means 350 runs altogether. This could give rise to a lot of mistakes when filling in the data. For all these reasons, eventually, four aggregated models were implemented (see Figures 16, 17, 18 and 19) using the Model Maker tool of ERDAS IMAGINE 9.1 software. Explanation of symbols used in models is on the Figure 15.
Figure 15: Objects used in Model Maker tool of ERDAS IMAGINE 9.1 software.
30 2.3.1 The 1st phase of the model
In the 1st phase (see Figure 16) of modelling the surface albedo α, Normalized 2 Difference Vegetation Index – NDVI, net radiation flux Rn [W/m ] and soil heat flux G [W/ m2], which are required for the following parts of model, are calculated. For these semi- results albedo at the top of the atmosphere αtoa, Soil Adjusted Vegetation Index – SAVI,
Leaf Area Index – LAI and the broad band surface emissivity ε0 are consequently evaluated.
Figure 16: The 1st part of SEBAL/METRIC model.
Inputs for the 1st part of the model, specific for each image, are spectral radiance of 2 ASTER bands Lλ [W/m /sr/μm] (after the atmospheric correction described in the chapter
2.1.1), solar elevation angle β [degree] at satellite overpass and incoming shortwave RS↓ 2 2 [W/m ] and incoming longwave RL↓ [W/m ] radiations from the sun and the atmosphere respectively. Other parameters, consistent for all scenes, are required: the mean solar 2 exo-atmospheric irradiance for the each band ESUNλ [W/m /μm] and its sum, specific for
31 ASTER; alpha path radiance αpath_radiance (value 0.03 recommend by Bastiaanssen, 2000); than Stefan-Boltzmann constant (5.67 × 10-8 W/m2/K4) and the elevation above the sea level z [m] representing area of interest (100 m).
For evaluation of the consecutive computations, several equations were used. All of them were adopted from Allan et al. (2002), where detailed explanation can be found. The surface albedo needs to be known at first, it is computed as: − = toa pathradiance 2 (3) sw where atmospheric transmissivity is
= × −5× sw 0,75 2 10 z (4).
Vegetation indices are computed from the ASTERs 3rd and 2nd spectral bands: − = 3 2 NDVI (5), 3 2 × − =1,5 3 2 SAVI (6), 0,5 3 2
0,69−SAVI ln 0,59 (7). LAI =− 0,91
The net radiation is computed from the equation 2. Outgoing longwave radiation is calculated as:
= ×× 4 R L 0 LST (8) where LST is land surface temperature [K] computed in ENVI 4.3 software as mentioned before. Afterwards, incoming radiation of both of the lengths was computed outside of the model in the spreadsheet. Software OpenOffice.org 3.0 Calc was used for this purpose, input variables and obtained values are listed in the Table 7. Equations applied in calculations were following: = × × × R s G SC cos d r sw (9) and
= ×− 0,09×× 4 R L 0,85 ln sw T cold (10).
Soil heat flux is computed from known values using the equation empirically set by Bastiaanssen (2000):
= × × − 4× G LST 0,0038 0,0074 1 0,98 NDVI R N (11).
32 Table 7: Values of incoming shortwave and longwave radiations [W/m2], solar elevation angle and temperatures [K] at cold and hot pixels for 13 ASTER scenes.
2007 2006 2005 2004 1 2 3 4 5 6 7 8 9 10 11 12 13 Sign Unit 6887 6599 6601 11500 28410 28441 2476 23700 23698 2177 13689 28605 28600 Incoming Longwave radiation (Allan et al., 2002, p.23) Land surface temp. cold pix T_lst_cold K 304.2 305.9 307.1 306.9 300.6 301.9 302 303.5 303.7 301.8 304.8 306.3 305.6 Stefan-Bolzman Constant σ W/m2/K4 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 5,67.10-9 Elevation of area (average) z m 100 100 100 100 100 100 100 100 100 100 100 100 100 Atmospheric transmissivity Tau_sw 0,752 0,752 0,752 0,752 0,752 0,752 0,752 0,752 0,752 0,752 0,752 0,752 0,752 Incoming longwave radiation R_L_in W/m2 368.62 376.93 382.88 381.88 351.48 357.60 358.07 365.24 366.20 357.12 371.54 378.90 375.45
Incoming Shortwave Radiation (Allan et al., 2002, p.19) Solar constant G_sc W/m2 1367 1367 1367 1367 1367 1367 1367 1367 1367 1367 1367 1367 1367 Solar elevation angle Beta degree 66,583 66,851 66,716 66,963 67,095 67,468 64,101 65,559 66,009 65,394 65,640 63,113 63,370 Solar incidence angle degree 23.42 23.15 23.28 23.04 22.91 22.53 25.9 24.44 23.99 24.61 24.36 26.89 26.63 Solar incidence angle Theta rad 0.41 0.4 0.41 0.4 0.4 0.39 0.45 0.43 0.42 0.43 0.43 0.47 0.46 GMT time of satelite pass Time 6:52:59 6:52:59 6:53:14 6:53:23 6:52:38 6:52:47 6:59:02 6:52:29 6:52:37 6:52:29 6:52:38 6:52:48 6:52:57 Single date time Date 01.06. 01.06. 03.07. 03.07. 14.06. 14.06. 23.07. 26.05. 26.05. 13.07. 13.07. 26.07. 26.07. Day of Year DOY 152 152 184 184 165 165 204 146 146 194 194 208 208 Inverse squared rel. Eart-Sun dist.d_r 0,9714 0,9714 0,9670 0,9670 0,9685 0,9685 0,9692 0,9733 0,9733 0,9676 0,9676 0,9701 0,9701 Incoming Shortw. Radiation R_s_in W/m2 916.38 918.23 913.11 914.79 917.09 919.59 896.29 910.88 914.10 904.39 906.17 889.47 891.49
Cold pixel T_lst_cold K 304.2 305.9 307.1 306.9 300.6 301.9 302,0 303.5 303.7 301.8 304.8 306.3 305.6 Hot pixel T_lst_hot K 322 325.4 325.3 326.6 317.8 317.5 319.4 318.2 322.8 316.6 319.2 322.1 323.8
(Physical) constant not changing Input from the satelitte image need(or *.met to be file) set for each image Inputs into model need to be filled into SEBAL model 2.3.2 The 2nd phase of the model
In the second phase of the model (see Figure 17), there are initial values of
momentum roughness length zom [m], friction velocity u* [m/s] and aerodynamic rd resistance to heat transport rah [s/m] for the 3 phase of the model computed. The inputs are NDVI and surface albedo from the 1st phase, Von Karman constant k (0.41) and value
of blending hight (200 m) above weather station for wind velocity u200 [m/s].
For iterative evaluation (see chapter 2.3.3) of the aerodynamic resistance and rd sensible heat flux in the 3 phase, neutral stability for rah is assumed. Then z ln 2 = z1 (12) r ∗ ah u ×k
is used, where z2 and z1 are the heights [m] above the zero-plane displacement of the vegetation (generally 0.1 and 2 m; Allan et al., 2007). Friction velocity u* for each pixel is computed applying the logarithmic wind law for neutral atmospheric conditions (Allan et al., 2002): ⋅ ∗ k u u = x z (13), ln x zom
33 where ux is the wind speed [m/s] at height zx and zom is the momentum roughness length
[m]. Since ground wind speed data for the each pixel were unavailable, we used the wind speed at meteorological station in the blending hight of 200m. That is assumed to be identical for the whole area. Rearranged equation 13 is computed in the spreadsheet, 200 ln ∗ zom (14). u =u ⋅ 200 k
Figure 17: The 2nd part of SEBAL/METRIC model.
Friction velocity u* is from the equation 13, where measurements of wind speed at the meteorological station for ux (at 2 m) and empirical estimation (Brutsaert, 1982 in
Allan et al., 2002) for the momentum roughness length : zom = 0.12h (h is vegetation hight [m] around weather station – 0.25 m) were used. Values of u200 for satellite images are in the Tables 9, 10, 11 and 12.
Once the value of u200 identical for the whole image is known, we can calculate u* for each pixel from the equation 13 as:
34 ⋅ ∗ k u u = 200 200 ln (15). z om
Then momentum roughness length remains, however, still unknown. It can be calculated from LAI, or as Bastiaanssen (2000) suggested, from NDVI. We use modified = [ × / ] zom exp a NDVI b (16), where a and b are the regression constants from the plot of ln(zom) versus NDVI/α for two conditions representing specific vegetation types (Allan et al., 2007). Chosen crop types and the specific heights are shown in Table 8, together with the regression coefficients a and b. For determining the sample pixels land use layer was applied. Supervised selection of the samples has shown significant dependence on the selector who can have a strong influence on the data coherency.
Table 8: Crop types used as a small and tall crop for the image acquisition dates and assumed heights specific for its vegetation period (consulted with Dr. Christopher Conrad). Regression coefficients a, b are computed as parameters
of the linear regression (Y-axis=ln(zom), X-axis=NDVI/α).
Assumed types and heights [m] for the tall and the small crops Date tall crop height of tall small crop height of small 27.03.2004 wheat 0.15 soil 0.01 26.05.2005 wheat 0.9 cotton 0.1 01.06.2007 wheat 1 cotton 0.15 14.06.2006 wheat 0.9 cotton 0.25 03.07.2007 cotton 0.5 fallow 0.1 13.07.2005 cotton 0.8 fallow 0.1 23.07.2006 cotton 0.9 fallow 0.15 26.07.2004 cotton 1 fallow 0.1
Regression coefficients a.b for evaluating z_om (input for 2nd phase) No. Image Date a b 1 6887 01.06.2007 0.796 -4.169 2 6599 01.06.2007 0.892 -4.194 3 6601 03.07.2007 0.724 -4.594 4 11500 03.07.2007 0.797 -4.538 5 28410 14.06.2006 0.747 -3.613 6 28441 14.06.2006 0.627 -3.547 7 2476 23.07.2006 0.644 -4.453 8 23700 26.05.2005 0.768 -4.487 9 23698 26.05.2005 0.845 -4.58 10 2177 13.07.2005 0.740 -4.606 11 13689 13.07.2005 0.826 -4.648 12 28605 26.07.2004 0.755 -4.647 13 28600 26.07.2004 0.864 -4.729 35 2.3.3 The 3rd phase of the model
In the 3rd phase of the model (see the first iteration of it on the Figure 18) is sensible heat flux H [W/m2] computed in 8 iterations while friction velocity u* [m/s], temperature difference dT [K] and aerodynamic resistance to heat transport rah [s/m] are gradually refining.
Initial values of the momentum roughness length zom [m], friction velocity u*
[m/s], aerodynamic resistance to heat transport rah [s/m] and wind velocity in 200 m u200 [m/s] for neutral atmospheric conditions from the 2nd phase are used along with the land surface temperature LST [K] and regression coefficients a and b (as described bellow).
Sensible heat flux is a rate of the heat lost to the air by convection and conduction, dependant on the temperature difference and aerodynamic resistance to heat transport. In SEBAL and METRIC it is computed as:
= ⋅ ⋅dT H air C p (17), rah
3 where ρair is air density [kg/m ], Cp specific heat of air at constant pressure [J/kg.K] and rah is aerodynamic resistance [s/m] between two near surface heights (typically 0.1 and 2 m). Sensible heat flux H is computed as a function of estimated atmospheric roughness of each pixel (Allan et al., 2007).
There are two missing variables in the equation 17 rah and dT, which need to be solved. Aerodynamic resistance of the heat transport is strongly influenced by buoyancy within the boundary layer driven by the rate of H. The iteration process, with the input values assuming neutral atmospheric conditions, (more about different atmospheric condition – appendix 11 in Allan et al., 2002) integrates Monin-Obhukov length (see Allan et al, 2002 and 2007). Process is implemented to 8 iterations and leads to the conditions that can be considered stabile for the atmosphere.
The dT [K] represents the near surface temperature difference between two heights (0.1 and 2m). It is used in order to avoid the inaccuracy when estimating the surface temperature from the satellite. This is assumed due to plausible atmospheric attenuation or contamination of data and uncertainties in radiometric calibration of the sensor (Allan et al., 2007). For both SEBAL (Bastiaanssen, 1998) and METRIC (Allan et al., 2007) it is calculated as: = ⋅ dT b a LST (18), assuming a linear relationship between dT and land surface temperature LST.
36 Figure 18: The 3rd part of SEBAL/METRIC model.
37 This is the a fundamental assumption that has strong impact on the obtained value of H. Parameters of the linear regression a and b are computed in the spreadsheet (see Tables 9, 10, 11 and 12) using values from two anchor points (cold and hot pixels) with special properties.
In traditional SEBAL, it is assumed, that the hot pixel is completely bare and dry, with no evaporation, hence, the ET is zero. For wet pixel was chosen a water body, so that there can be assumed zero temperature difference (dT). H is expected to be zero at the wet pixel as all the energy is used for evaporation and heating of the water (G) (Allan et al., 2007).
In METRIC non-vegetated dry area, optimally bare soil near to the meteorological station – same as for SEBAL – is established as a hot pixel. In contrast to SEBAL, in METRIC is surface soil water balance taken into account (Allan et al., 2007). This was, however, omitted in this study due to local atmospheric conditions in Khorezm region (Conrad et al., 2007), since precipitation is not probable in the last days of the image acquisition. Still, in most of the images, some residual evapotranspiration was included using 0.05 evapotranspiration factor (see equation 19).
Table 9: Values of main variables for the anchor pixels from the 1st and 2nd phase of the model and input coefficients a & b for 3rd phase (year 2007).
2007 1 – 6887 01.06.2007 2 – 6599 01.06.2007 3 – 6601 03.07.2007 4 – 11500 03.07.2007 Summary Data of the Anchor Pixels Wet Pixel Dry Pixel Wet Pixel Dry Pixel Wet Pixel Dry Pixel Wet Pixel Dry Pixel Land Surface Temperature LST [K] 304.48 322.02 305.96 325.17 307.12 325.29 306.87 326.58 Net Radiation Rn [W/m2] 652,908 477.25 677,759 417.96 619,150 411.98 606,962 421.91 Soil Heat Flux G [W/m2] 127.17 134.1 104.05 129.09 99.35 128.53 92.18 210.95 Latent Heat Flux λET [W/m2] 485.49 25.55 536.59 25.55 509.26 24.25 509.26 24.25 Sensible Heat Flux H [W/m2] 40,251 317.6 37,115 263.32 10,540 259.2 5,522 186.7 Aerodynamic Roughness zom [m] 0.11 0.02 0.24 0.02 0.08 0.01 0.11 0.01 Aerodynamic Resistance rah [s/m] 50.23 61.52 44.81 61.56 64.25 79.8 61.36 79.81 Friction Velocity u* [m/s] 0.15 0.12 0.16 0.12 0.11 0.09 0.12 0.09 Etr FACTOR ETrF 0.95 0.05 1.05 0.05 1.05 0.05 1.05 0.05 Wind speed in meteo Ux [m/s] 1.28 1.03 Wind sp. at blending hight u200 [m/s] 2.68 2.17 Standardized Reference ET ETr [mm/hr] 0.75 0.71 Iteration # a b a b a b a b 1 0,99471942 -300,86160771 0,75436960 -229,15114995 1,09695276 -336,22497397 0,73583436 -225,46434510 2 0,16131396 -48,24638906 0,13634492 -40,96841420 0,15826556 -48,24763247 0,12806352 -39,08877871 3 0,51697467 -156,03821004 0,39586195 -119,95236082 0,53957565 -165,23524167 0,37158014 -113,76991806 4 0,30231333 -90,89980761 0,24662140 -74,48745313 0,30222513 -92,38468698 0,23052237 -70,49960053 5 0,39961873 -120,43200458 0,31275784 -94,63204181 0,40759545 -124,72940625 0,29154241 -89,21923442 6 0,34932606 -105,16011080 0,27988190 -84,61514714 0,35235295 -107,76946153 0,26140318 -79,97235773 7 0,37355166 -112,51856897 0,29527822 -89,30653680 0,37890085 -115,92058651 0,27532511 -84,24389706 8 0,36149764 -108,85601526 0,28786719 -87,04794483 0,36559705 -111,83555651 0,26869417 -82,20930475 Input for 3rd part of the model Meteo Data: Ux – measured; Etr – computed Values from 1st & 2nd phases of the model Sensible heat flux at the cold pixel is in METRIC computed using the equation: = − − ×⋅ H cold/ hot Rn G k ET r (19),
where ETr is standardized reference evapotranspiration of the alfalfa [mm/hr], λ is the
38 latent heat of vaporization [J/kg] and k is evapotranspiration factor. Values of ETr specific for satellite overpass were computed using the meteorological data in accordance with ASCE standards (2000), λ is obtained from equation 21.
Recommendations for the k-factor value in the cold pixel are 1.05 (5% more than reference alfalfa ET rate – Tasumi, 2003 in Allan et al., 2007). However, when k-factor is used for manual calibration of some images, even a value of 0.95 was applied in 3 cases for the cold pixel. For a similar reason, factor 0.05 was used for all the hot pixels. In two cases (scenes 8 and 10), parameters a and b from the pair image (south/north image from the same acquisition time) were used to obtain corresponding results of ET in the same time. See k-factors, reference ET and parameter values in the Tables 9, 10, 11 and 12.
Table 10: Values of main variables for the anchor pixels from the 1st and 2nd phase of the model and input coefficients a & b for 3rd phase (year 2005).
2005 8 – 23700 26.05.2005 9 – 23698 26.05.2005 10 – 2177 13.07.2005 11 – 13689 13.07.2005 Summary Data of the Anchor Pixels Wet Pixel Dry Pixel Wet Pixel Dry Pixel Wet Pixel Dry Pixel Wet Pixel Dry Pixel Land Surface Temperature LST [K] 303.48 318.16 303.74 322.83 301.81 316.1 304.85 319.21 Net Radiation Rn [W/m2] 665,717 456.21 653,385 445.3 701,269 441.17 707,843 432.67 Soil Heat Flux G [W/m2] 93.74 123.59 91.63 126.88 91.29 220.58 98.9 120.38 Latent Heat Flux λET [W/m2] 526.11 25.05 526.11 25.05 605.18 0 605.18 0 Sensible Heat Flux H [W/m2] 45,869 307.56 35,646 293.37 4,797 220.58 3,761 312.3 Aerodynamic Roughness zom [m] 0,179 0.01 0.16 0.01 0.12 0.01 0.03 0.01 Aerodynamic Resistance rah [s/m] 47.73 65.52 48.38 65.35 26.24 34.45 30.83 34.44 Friction Velocity u* [m/s] 0.15 0.11 0.15 0.11 0.28 0.21 0.24 0.21 Etr FACTOR ETrF 1.05 0.05 1.05 0.05 1.05 0 1.05 0 Wind speed in meteo Ux [m/s] 1.25 2,419 Wind sp. at blending hight u200 [m/s] 2,620 5,068 Standardized Reference ET ETr [mm/hr] 0.74 0.85 Iteration # a* b* a b a** b** a b 1 1,21904323 -367,77588060 0,91027937 -274,77331995 0,52088344 -157,08296700 0,73762880 -224,74842368 2 0,19583052 -58,54049002 0,15361948 -45,89791003 0,23006987 -69,32763933 0,29456967 -89,69684686 3 0,60405227 -181,82346183 0,45771189 -137,83565928 0,33487668 -100,95659991 0,45800875 -139,51842802 4 0,36512113 -109,60766590 0,28221388 -84,72526215 0,29834204 -89,93055865 0,39698969 -120,91733037 5 0,46827266 -140,77377281 0,35796345 -107,64632900 0,31064930 -93,64493403 0,41840938 -127,44699562 6 0,41708383 -125,30451995 0,32072505 -96,37453459 0,30648636 -92,38852863 0,41079068 -125,12446844 7 0,44076217 -132,45958427 0,33787365 -101,56584549 0,30789105 -92,81247700 0,41348443 -125,94564817 8 0,42944584 -129,03985793 0,32973791 -99,10247400 0,30741677 -92,66933529 0,41253017 -125,65474500 *a & b of image 9 used **a & b of image 11 used Input for 3rd part of the model Meteo Data: Ux – measured; Etr – computed Values from 1st & 2nd phases of the model
39 2006 5 – 28410 14.06.2006 6 – 28441 14.06.2006 7 – 2476 23.07.2006 Summary Data of the Anchor Pixels Wet Pixel Dry Pixel Wet Pixel Dry Pixel Wet Pixel Dry Pixel Land Surface Temperature LST [K] 300.62 317.77 301.92 317.47 302 319.36 Net Radiation Rn [W/m2] 689,702 437.87 693,239 449.46 660,637 501.63 Soil Heat Flux G [W/m2] 92.18 116.92 95.63 118.89 83.52 122.82 Latent Heat Flux λET [W/m2] 592.05 31.16 592.05 31.16 539.34 25.68 Sensible Heat Flux H [W/m2] 5,467 289.79 5,560 299.41 37,782 353.13 Aerodynamic Roughness zom [m] 0.07 0.04 0.06 0.04 0.16 0.02 Aerodynamic Resistance rah [s/m] 17.93 19.32 18.02 19.32 24.9 32.87 Friction Velocity u* [m/s] 0.41 0.38 0.41 0.38 0.29 0.22 Etr FACTOR ETrF 0.95 0.05 0.95 0.05 1.05 0.05 Wind speed in meteo Ux [m/s] 3.8 2.43 Wind sp. at blending hight u200 [m/s] 7,967 5,090 Standardized Reference ET ETr [mm/hr] 0.92 0.76 Iteration # a b a b a b 1 0,31936628 -95,90995123 0,36422745 -109,86886471 0,61171891 -183,79921194 2 0,19181664 -57,57220693 0,21615414 -65,16856181 0,22276955 -66,62011203 3 0,23499445 -70,55157362 0,26666067 -80,41689878 0,37349389 -112,04754059 4 0,22167727 -66,54825891 0,25095134 -75,67397602 0,31340942 -93,93000314 5 0,22588076 -67,81189827 0,25594899 -77,18287140 0,33574955 -100,66800519 6 0,22456667 -67,41685652 0,25437435 -76,70745216 0,32731691 -98,12405086 7 0,22497861 -67,54069539 0,25487185 -76,85766117 0,33047429 -99,07673974 8 0,22484959 -67,50190955 0,25471481 -76,81024666 0,32928971 -98,71925632 Input for 3rd phase Meteo Data: Ux – measured; Etr – computed Values from 1st & 2nd phases of the mod.
2004 12 – 28605 26.07.2004 13 – 28600 26.07.2004 14 – 27187 27.03.2004 Summary Data of the Anchor Pixels Wet Pixel Dry Pixel Wet Pixel Dry Pixel Wet Pixel Dry Pixel Land Surface Temperature LST [K] 306.31 322.12 305.47 323.83 295.61 306.91 Net Radiation Rn [W/m2] 642,492 442.61 645,621 421.47 583,033 491.09 Soil Heat Flux G [W/m2] 89.62 221.3 87.24 124.06 62.68 82.86 Latent Heat Flux λET [W/m2] 532.45 25.35 532.45 25.35 510.36 15.47 Sensible Heat Flux H [W/m2] 20,423 195.95 25,929 272.06 10 392.76 Aerodynamic Roughness zom [m] 0.2 0.01 0.3 0.01 0,002 0 Aerodynamic Resistance rah [s/m] 40.33 57.46 37.97 57.39 39.61 40.2 Friction Velocity u* [m/s] 0.18 0.13 0.19 0.13 0.18 0.18 Etr FACTOR ETrF 1.05 0.05 1.05 0.05 1.05 0.05 Wind speed in meteo Ux [m/s] 1.45 2.5 Wind sp. at blending hight u200 [m/s] 3,049 5.24 Standardized Reference ET ETr [mm/hr] 0.75 0.46 Iteration # a b a b a b 1 0,65760560 -200,61082077 0,79375178 -241,48920115 1,35643585 -400,57877791 2 0,16447481 -49,89728950 0,17437695 -52,71878158 0,50864019 -150,04983492 3 0,35525368 -108,20410989 0,41621482 -126,41408343 0,76254050 -225,08615615 4 0,26535319 -80,71576175 0,29411894 -89,19070330 0,68075624 -200,91341738 5 0,30072607 -91,53220718 0,34355501 -104,26183266 0,70462795 -207,96946117 6 0,28590961 -87,00080410 0,32177287 -97,62037331 0,69749781 -205,86184982 7 0,29194079 -88,84555745 0,33099870 -100,43352922 0,69961077 -206,48643783 8 0,28945856 -88,08622934 0,32702682 -99,22231104 0,69898336 -206,30097496 Input for 3rd phase Meteo Data: Ux – measured; Etr – computed Values from 1st & 2nd phases of the mod. Tab. 11&12: Values of main variables for the anchor pixels from the 1st and 2nd phase of the model and input coefficients a & b for 3rd phase (years 2006 & 2004).
2.3.4 The 4th phase of the model
The final phase of modelling is the 4th one (see Figure 19), where the latent heat 2 flux λET [W/m ] and the instantaneous evapotranspiration ETinst [mm/hour] are evaluated.
Inputs are the variables from the equation 1: the net radiation flux at the surface Rn [W/ m2] and the soil heat flux G [W/m2] from the 1st phase of the model and the sensible heat flux to the air H [W/m2] from the 3rd phase.
40 Figure 19: The 4th part of SEBAL/METRIC model.
Solution of the equation yields the value of latent heat flux. From that the instantaneous ET is calculated: = ⋅ ET ET inst 3600 (20)
λ is the latent heat of vaporization or in other words, the heat absorbed when a kilogram of water evaporates [J/kg] and 3600 is a time conversion (Allan et al., 2002).
Latent heat of vaporization is constant that we obtain, presuming that 44 kilojoules of energy is used for converting one mole of water to the vapour state and that molecular mass of the water vapour is 18.02 g/mol (Campbell and Norman, 2000). Than
=44⋅10 3×18,02⋅103 (21).
Of note is that in the last step, when instantaneous ET is computed, negative values are masked to zero. The negative values point to errors or inaccuracies that may have arose for several reasons, such as errors in the input satellite images (incorrect atmospheric correction or scanner calibration) or, predominantly, due to inappropriate anchor pixels selection (when they do not represent extreme values ideally). For this reason, negative values are masked to zero, as this is the nearest valid value as well as the most probable real one.
The instantaneous evapotranspiration for the satellite overpass is an output acquired from modelling and its value was computed for 13 images made within Khorezm region during the years 2004-2007.
41 2.4 ASTER – MODIS result comparison
There are available evapotranspiration data for the whole Khorezm province based on the MODIS (Moderate-resolution Imaging Spectroradiometer) satellite data carried out by Conrad (Conrad et al., 2007), who used SEBAL modelling algorithm with METRIC modification for 35 time-series in the 2004. Moreover, there are available data from 2005. Secondly, MODIS and ASTER data are acquired from the same Terra satellite, making the comparison more transparent and reliable.
The major difference that leads to difficulties in validating and comparing the result data of evapotranspiration from the two modellings is resolution. To be more precise, MODIS modelling is made on 1 km pixel scale, whereas ASTER input data are in 15 m resolution.
Yet, comparison of two datasets of the actual evapotranspiration was eventually performed for two images from the year 2005 (data for exactly the same date are available in the both datasets). The extent of compared area was set by Khorezm oblast border (MODIS data extent) and by the north ASTER scene. Comparison was done visually and presented on the scatter plot.
Figure 20: Scatter plot of ET values modeled from ASTER (x-axis) and MODIS (y-axis) data aggregated to 1 km pixel. (Bohovic R. and Conrad C.)
This was accomplished using ENVI 4.3 software. First of all, the data were clipped with respect to the set extent. Consequently, downscaling of ASTER based data to the 1 km pixel was performed. Both data sets were stacked into one image and then 2D graph – the scatter plot – was created, as depicted in the Figure 20. Values of ASTER based pixels are plotted on the x-axis. Values on the y-axis represent the ET values of corresponding pixels from MODIS based data. The figure hence shows that the absolute
42 congruence of data would lead to the linear dependence with line equation x = y. Furthermore, there arose several obvious divergences after the visual evaluation of the scatter plot (Figure 20).
Firstly, there appeared a shift of the data to the right, which indicates higher values of the ASTER data in lower part of the ET value range. This is, actually, a substantial shortcoming of the pixels with the low ET rate that usually represent non-agricultural land.
Figure 21: Scatter plot of ET values modelled from ASTER (x-axis) and MODIS (y-axis) data aggregated to 2 km pixel. (Bohovic R. and Conrad C.)
Figure 22: Scatter plot of ET values modelled from ASTER (x-axis) and MODIS (y-axis) data aggregated to 4 km pixel. (Bohovic R. and Conrad C.)
43 Secondly, the scatter of dots around potential liner dependence line could be considered as another incoherence. In the first graph (on the left side; May 26, 2005 image) the dispersion is rather high, while graph (right side) for July shows a better correlation. For reducing the impact of different original data scale and geometric differences, scatter plots for 2 and 4 km aggregations were made. Comparing 1 km, 2 km (Figure 21) and 4 km (Figure 22) aggregation scatter plots, it was empirically confirmed that the lower is resolution, the better is trend in the graph.
Further analysis and consultations with Dr. Christopher Conrad uncovered three possible grounds for the above mentioned variances:
• Discrepancies in the modelling process:
A bit different modules of SEBAL/METRIC algorithm was applied. For modelling of the MODIS based data, leaf area index (LAI) was used for computing surface roughness, whereas in this work (ASTER data modelling), NDVI index was used (see chapter 2.3.1) instead of that.
Apart from that, qualified manual selection was used for anchoring pixels (chapter 2.3.3), instead of automatized selection of the hot and wet pixels based on set rules as was performed in the work of Conrad et al. (2007) .
• Spatial geometry.
The spatial resolution difference was significant, hence, two data sources did not entirely correspond. There should have been some geometric co-registration carried out. Not doing so, there arises a mistake when downscaling from 15 m to 1km resolution.
• The MODIS pixel size.
The original MODIS data pixel size is actually not 1 km as supposed, due to rounding of the Earth surface. Therefore, the size corrections had to be processed and that may have been a source of additional error to the data.
With regard to other works on a similar topic, it seems that the incoherent methods and lower resolution seem to play a role in the accuracy of results. Some improvements could be likewise been achieved by optimization of parameters and model settings. This is true for both of the data sets. Cross checking of results processed from different input data helped to diminish these discrepancies, however, some additional adjustments would be needed.
44 2.5 Results and discussion
Outputs from all of the 4 modelling phases were adjusted for further interpretation and processing. First of all, 13 images were clipped according to the area of interest using ERDAS IMAGINE 9.1 software (Data Preparation → Subset Image). Subsequently, two different images (north and south part of the interest area) from the same acquisition time were merged together (Data Preparation → Mosaic Images → Mosaic Wizard; according to the given cut line). This yielded 7 final scenes of the actual evapotranspiration values for one complete and full covered area of interest in different points of time.
Table 13: Statistical characteristics for the different temporal scenes. SCENE AREA MIN MAX MEDIAN MEAN STD SUM* [km2] [mm/hr] [mm/hr] [mm/hr] [mm/hr] [mm/hr] 2004-july 1188.49 0.000004 0.920308 0.52717 0.514542 0.151254 2717914 2005-may 1183.60 0.000001 0.95591 0.37685 0.38164 0.177493 2007601 2005-july 1188.95 0.000001 1.042116 0.60214 0.589474 0.18986 3114903 2006-june 1198.82 0.000002 0.967995 0.34945 0.362412 0.174985 1930959 2006-july 1205.08 0.000001 1.389674 0.52113 0.49625 0.169932 2657880 2007-june 1160.74 0.000001 0.995504 0.27036 0.293591 0.175088 1514595 2007-july 1203.84 0.000018 0.929325 0.43294 0.43696 0.15661 2337920 * count of scene pixels multiplied by MEAN value Data layers of actual evapotranspiration are depicted on the left side of the Map of ET in Khorezm (full name: Map of instantaneous evapotranspiration in Khorezm region (Uzbekistan) during years 2004 to 2007.). As, basically the main output of this thesis, it is further presented in the Appendix 1.
At the first look, there are obvious patterns in distribution of the ET values, some of them are more clearly illustrated in the value distribution graphs. Namely, Figure 23 comprises two graphs for early (May/June) and later (July) scenes respectively. While the distribution curves for the early scenes show very good correlation, curves for the later scenes are more variable, especially the year 2006. Besides the shape, local maxima are obvious as well. Some statistical characteristics are listed in the Table 13, the mean values are labelled as triangles in the graphs.
45 Figure 23: Graphs of the actual evapotranspiration value distribution – for the early and the later vegetation season separately. A triangles with numbers depict mean values [mm/hr] for the scenes.
Both graphs and tables show, that the lowest values of the ET were achieved in 2007, with mean values of 0.29 and 0.44 mm/hr, followed by the years 2006 and 2004. In 2005 were the highest values reached for both the early and the later scene (0.38, 0.59 mm/hr). There is a difference of 3 weeks in early/later scenes date acquisition throughout the examined years. This could be a reason for considerable discrepancies, which are, however, not as evident as expected. July 2007 distribution curve has an unlike waveform with sharp maximum after the mean value (similar to the early scenes), that could be ascribed to an early acquisition date of this scene, when some wheat fields have not been harvested yet.
In general, evapotranspiration differences between the various land covers are clearly visualised. All the later (July) scenes are considerably darker (ET is higher) than the early (May/June) scenes. This implies that the ET rate is strongly influenced by the
46 vegetation, as this becomes more grown on the later scenes. Constantly low ET values (yellow colour on the Map of ET in Khorezm – Appendix 1) can be observed in the settlements – Urgench in the centre of area and Beruni in the upper right corner. This can be again ascribed to the lack of vegetation in the arid climate, since it is a matter of fact, that neither trees nor grass grows in this area except for a private gardens, watered by inhabitants from the duct. This makes the value of ET in towns still slightly positive in contrast to an airport (lighter spot on the north edge of Urgench) or desert spurs (bottom of scene), with ET values close to zero (light yellow).
On the other hand, water bodies with the hight rates of evaporation are constantly dark (hight ET values). These are drainage lakes at the bottom of the left side of the scene and Amu Darya river crossing the opposite corner of the area. This can provide us with information about water level in the river. While on the May 2005 scene high water (flooding banks of the river) is visible, June 2007 scene displays low water level (with a narrow riverbed and “islands” towering out of the water). It needs to be taken into account, that input satellite images, and consequently the ET values as well depend on the water depth and the substances it contains.
As mentioned before, Table 13, Figure 23 and, in particular, Map of ET in Khorezm (Appendix 1) offer an important deal of information. The significance of data is enhanced when combined with supplementary information about the region from the different activities. It could provoke discussion and prompt further studies reflecting on some other aspects of the water management, agriculture patterns and dynamics of the farming. This study can be especially helpful in any possible water related research, such as consumption of the water, losses of irrigation, desiccation and desertification, water flow modelling of the area and optimization of the whole water distribution, use and management system. Significant implications are feasible in many agriculture studies. From the abundance of other analyses based on the obtained data related to the fields some are more thoroughly referred to in chapter 3.
Comparison of the ET data to the antecedent work of Conrad et al. (2007), has shown that modelled values for the actual evapotranspiration are generally well tenable. Some adjustments and optimizations are still suitable, while results could be already useful.
Comparison of remote sensing based modelling results with the ground measurements would definitely bring new possibilities of verification. In this respect, Eddy covariation tower station, which starts operating in the area in the summer 2008, seem promising. Since it could bring notable reference to the future modelling as well as many new possibilities for optimization.
47 3 GEOPROCESSING ANALYSES
As stated above, evapotranspiration data, modelled and described in this study, could be very useful input data for the numerous analyses that can be done upon them. Water is one of the principal sources in the agriculture of semiarid Khorezm region and its appropriate use and management is, hence, a very important issue in many aspects of an economical and social life of the local people. Even the German-Uzbek UNESCO/ZEF research project is in the most aspects water related, which affirms the importance and wide plausible application of the data.
From all the possibilities, of note here is the analysis based on the spatial and the temporal aspects and their combination. Its combination with field boundaries and agricultural land use classification for identical spatio-temporal extent provided by Zeidler (2009) brings new insights into the study of water use and agriculture of Khorezm oblast.
Furthermore, there have been analyses carried out, with regard to spatial distribution of the crop types in general and according to rayons (administrative units), for the access to the market, distance from the water intake point and WUAs (water management units) by Zeidler (2009). Differences in the spatial distribution of evapotranspiration values taking notice of the crop types are discussed further in this chapter.
3.1 Patterns of ET distribution
The analysis was made on a field level of the main crop types in the region – cotton, rice and winter wheat. The other agricultural crop types were omitted from the further interpretations for two reasons:
• Firstly, its spatial extent is marginal in comparison to the three predominant crops (see area on the Figure 25).
• Secondly, the interpretation of less prevalent crops is much more variable and inaccurate, for it is difficult to recognize any common patterns.
The reason for transition from the pixel to the field level was that pixel related information is generally more difficult to interpret and far more ambiguous. Therefore, a link between the actual evapotranspiration values and the fields themselves was quested.
As a first step, fields in the area of interest were extracted. Afterwards, the mean values of the actual ET for cotton, rice and wheat fields were computed for each year and combined into the field representations. This was performed in ESRI ArcMap 9.2 software, using tool Arc Toolbox → Spatial Analyst Tools → Zonal → Zonal Statistics as Table for the
48 aggregation and computing of the mean value for each field. Then, spatial merging of the features (according to the crop involved) was carried out, using a Arc Toolbox → Analysis Tools → Overlay → Spatial Join function. Obtained data for 7 scenes are depicted on the right side of the Map of the ET in Khorezm (2009) (see Appendix 1). The colours differ according to crop types, whereas shades depend on the value of actual ET for the fields, dark colours imply its higher values. White space represent non-agricultural land (from the top, there are visible patterns of Amu Darya river, town Urgench and Karakum Desert in the south) or other crop types (often small gardens and orchards close to the settlements).
Fields with higher ET values can be considered well watered, with well developed vegetation canopy as a result. This pattern occurs in areas near the river, especially on its left bank in upper-left part of the image. In this case, hight values of ET point to good irrigation conditions with sufficient water supply. There are two possible explanations: either this could be dude to high proportional rate of rice fields (which have hight ET values in general) or thanks to good water access, which could be a result of the advantageous position close to the river.
Furthermore, the social context of family/friends/neighbours relationships should not be underestimated, since favourable relationships could also have significant influence on WUA management and hence a good water supply in areas mentioned above.
To analyse relative differences between the crop types, box graphs in the upper right corner of Appendix 1 can be conducive. Colour boxes depict the range +/- STD (standard deviation) around the mean value (small square in the middle). Boxes demark the value range (from the minimal value to the maximum) of ET related to the crops. In a similar manner, graphs for each single crop are visualized in the Figure 24. There are some significant patterns in vegetation phases of the crops (see Figure 6) that are noteworthy.
To summarize, the highest value of ET on the early scene pertains to wheat, recorded already in the late vegetation phase before the harvest that takes place in June. This is the reason for small variance present. On the other hand, on the later scenes, wheat ET has low values and high variance. This could be caused by some remaining vegetation on harvested fields, or by fields remaining unharvested.
Rice has high values of the actual ET on both the early and later scene. Especially high deviation is present on the early scenes, because of the different growth phase of rice typical for this part of the season. Some better developed fields have already dense vegetation cover, while on the later plots plants are still small. During July, all fields with rice are already well developed causing hight ET values with small variability.
49 Figure 24: Characteristics of the ET values for the cotton, winter wheat and rice fields for the all scenes. Coloured box represents range MEAN value +/- STD; grey stripe denote ET range for category.
As for cotton, this is growing rather slowly, with a long vegetation period (see Figure 6). Due to this fact, there are only small plants in the early growth phase in May/June. This is changed considerably after a month, yet, the vegetation density (and actual ET) on July scenes is still low in comparison to rice fields.
Selected attributes of the agricultural categories are illustrated on the Figure 25, tables with the characteristics of all the categories can be found in the Appendix 4. From the graph, it is possible to identify some local/global maxima on the relative distribution plot (Figure 23). The narrow extreme of the early scenes around 0.5 mm/hour refers to the rice fields, in smaller extent to the wheat fields (as their area is small). The blunt local extreme on the June 2007 graph, is due to rice fields becoming less prevalent and the extreme becomes related to the wheat fields only. On the other hand, broad and mild
50 maximum peak between 0.1 – 0.4 mm/hr is related to cotton fields with the lower ET values in comparison to the other crops grown in June, while cotton is still in initial phase of its vegetation period (see Figure 6).
On July scenes it is more difficult to distinguish specific crop patterns, as cotton and rice are already grown up and they have similar mean ET values. The situation gets even more complicated, when after the wheat harvest some other crop is grown on the same field (usually rice).
Figure 25: Characteristics for the specific land use categories. Mean value ± standard deviation (colour box), value range
3.2 Relative differences in the actual ET
As absolute values of the actual ET depend on weather conditions and water availability, this is not constant throughout the year. Therefore simple relative comparison was processed and visualized.
Logically, better water availability is expected close to the water source, intake
51 points and near major irrigating channels. If there is not enough water to satisfy water demand in full extent, downstream farmers seem to be handicapped. This could, however, be influenced by water management represented by WUAs (Water Users Association) and their leaders. The main objective of the following analysis was to learn more about the actual operation of WUA management (not only the declared one) and find out its potential patterns and divergences.
For this purpose, fields of cotton, rice and wheat were visualized with respect to the relative ET. Results can be seen in Appendix 2 on the map Relative ET in Khorezm (full name: Actual evapotranspiration per fields – relative distribution in Khorezm region (Uzbekistan) during the years 2004 – 2007.). Fields that are underestimated – their ET value is lower than MEAN-STD of the same crop are depicted in red. On the other hand, blue marked fields have ET value higher than MEAN+STD of the same category. Further analysis brought about several interesting findings.
First of all, area on the upper left bank of Amu Darya shows alternating water conditions. While on May/June scenes this area shows good conditions with enough water (black – blue colour), it is critically underwatered on the later scenes, especially in June 2005 and 2006.
There are values surprisingly high above average on the July 2006 in the lower right edge of the area of interest. This locality has very hight ET rates, whereas the rest of the region suffers from water shortage. Similar trend is shown on July 2004 scene. We can only speculate about the reasons for such a pattern. Possibly, there is a big water dividing point and pump in this area, which gives advantage to the farmers in this area, although further from river source.
In general, there are not obvious long-term spatial patterns in relative distribution of the actual ET values. For further analysis, more knowledge about the local spatial and temporal conditions and short-term circumstances would be needed.
Relative distribution map of the ET on local level for Koramon WUA was done. Map of Relative ET in Koramon (full name: Actual evapotranspiration per fields – relative distribution the WUA Koramon during the years 2004 – 2007.) is in Appendix 3. Colour marks correspond to those in the map of Relative ET in Khorezm.
3.3 Discussion
Relating the evapotranspiration data with crop types in the monitored fields gives rise to a wide range of potential analyses. Land use data available in the same spatial and temporal scope as modelled ET is a considerable quality of this study and makes it a useful tool for water and agricultural analyses within Khorezm province research.
52 Several cases of such analyses were briefly mentioned above in this chapter. Especially monitoring and study of the crop vegetation phases is interesting and could be used for the educational purposes too.
Analyses of relative distribution of the ET based on the standard deviation did not show significant spatial or temporal patterns. However, it brings some clarity to the issue of the evapotranspiration and water availability on the local and regional levels. For a possible subsequent analysis, deeper insight into the situation is needed. For future studies, the use of standard statistical tests for quantifying the spatio-temporal patterns and to assess statistical significance would be very beneficial for objective evaluation of results.
53 CONCLUSION
High resolution multispectral ASTER satellite data, which were available for Khorezm province, allowed modelling of the actual evapotranspiration on the field level. For this purpose SEBAL algorithm with METRIC modification was used. Four models in Model Builder of ERDAS IMAGINE 9.1 software were implemented, that allowed modelling of the ET for 13 images. Eventually, 7 scenes of the actual evapotranspiration in the area of interest in the middle of Khorezm region were created. These data cover periods in May/June and July during the years 2004 – 2007.
Implementation was done with emphasis on the automation of the process. This was achieved by reducing 25-stage model into 4 phases. Nonetheless, there was still very important intervention of the advanced user needed. Of note here is the selection of hot and cold pixel, which needs expert knowledge, as this has a significant influence on the result. Taking the advantage of the remote sensing approach, there is still local, ground base knowledge very helpful when optimizing the model and the result consequently.
The modelling process proved suitability of the ASTER data for the purpose of this study. High spatial resolution of the input data enables to process the actual ET data with accuracy appropriate for the field level. This is an important improvement to the preceding data that allowed only the regional use based on the 1 km resolution. On the other hand, due to extension of the available input data, resulting values cover only the central part of Khorezm oblast. Expanding of the examined scope in the future is a matter of an access to input images, as the model itself could work in a bigger scale without need of any modification.
The most important output of this study is the above mentioned dataset of the actual evapotranspiration. This provides up-to-date, high resolution ET data for 7 different time periods in central Khorezm that have not been available before. This could have a wide range of the applications within UNESCO/ZEF project “Economic and Ecological Restructuring of Land and Water Use in the Khorezm Region (Uzbekistan)” and for other researchers involved in this project. The contribution to this research project by providing these new data is the major outcome of this study.
Actual evapotranspiration dataset modelled within this work is a basis for the Map of instantaneous evapotranspiration in Khorezm region (Uzbekistan) during years 2004 to 2007. This is the final visualization of the results, giving an overall information about the ET in Khorezm during the vegetation season. It consists of 8 parts and 15 map frames. Apart from absolute values of the actual ET in the area of interest, map show also relation between the actual ET and different crop type fields.
54 To summarize it, this study provides a simple analysis of the acquired ET data and relates them to the field boundaries. Interpretation of the crop type evapotranspiration patterns, based on the relative distribution did not show any striking revelations, yet it offers a space for further skilled analysis of this issue.
Application of the statistical tests to quantify spatial and temporal patterns are strongly suggested. Outputs from this part of the thesis are assembled and visualized in the maps Relative ET in Khorezm and Relative ET in Koramon. Together with Map of the ET in Khorezm these are the main cartographic outputs of this thesis.
Comparison of the result with the ET data modeled from the MODIS seems to require further adjustments. Nevertheless, it proves that there are available at least some modifications which can help to optimize the result. Other issue that arose, is the relation of data to the ground-based measurements, which would bring new possibilities for optimization. In this way, I’d like to bring forward the promising project of Eddy covariation tower operating in the area from the summer 2008.
55 REFERENCES
[1] ABRAMS M. (2000): The Advanced Spaceborne Thermal Emission and Refection Radiometer (ASTER): data products for the hight spatial resolution imager on NASA's Terra platform. International Journal of Remote Sensing, vol. 21, no. 5, p. 847–859. ISSN 0143-1161 [print]/ISSN 1366-5901 [on-line] [2] ALLEN R., WATERS R., TASUMI M., TREZZA R., BASTIAANSSEN W.G.M. (2002): SEBAL – Surface Energy Balance Algorithm for Land, Idaho implementation, Advanced training and users manual. Version 1.0, Idaho, USA. [3] ALLEN R., TASUMI M., TREZZA R. (2007): Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC) – Model. Journal of irrigation and drainage engineering, p. 380-394. [4] ASCE (2000): ASCE's Standardized Reference Evapotranspiration Equation. Report by ASCE Standardization of Reference Evapotranspiration Task Committee, Phoenix, USA, 11 p. [5] ADB - ASIAN DEVELOPMENT BANK (2008): Key Indicators for Asia and the Pacific 2008. http://www.adb.org/documents/books/key_indicators/2007/ [on-line edition, May 1st, 2009]. ISSN 0116-3000 [6] BASTIAANSSEN W.G.M., MENENTI M., FEDDES R.A., HOLTSLAG A.A.M. (1998): A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213, p. 198-212. [7] BASTIAANSSEN W.G.M. (2000): SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229, p. 87-100. [8] CAMPBELL G.S., NORMAN J.M. (2000): An Introduction to Environmental Biophysics. Springer-Verlag, Second Edition, New York, USA. ISBN 0-387-94937-2 [9] CONRAD C., DECH S.W., HAFEEZ M., LAMERS J., MARTIUS C., STRUNZ G. (2007): Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products. Irrigation and Drainage Systems, DOI 10.1007/s10795-007-9029-z.
56 [10] EEA – EUROPEAN ENVIRONMENT AGENCY (2007): Sustainable consumption and production in South East Europe and Eastern Europe, Caucasus and Central Asia; Joint UNEP-EEA report on the opportunities and lessons learned. United Nations Environment programme, Geneve, Switzerland; EEA Report No 3/2007, European Environment Agency, Copenhagen, Denmark, 184 p. ISBN 978-92-9167-965-2 [11] EMEC9 (2009): Erasmus Mundus EC 9. Official website, http://www.erasmusmundusec9 .eu/ [on-line, May 8th, 2009] [12] FAO/WFP (2000): FAO/WFP Crop and Food Supply Assessment Mission to the Karakalpakstan and Khorezm Regions of Uzbekistan. FAO Global information and early warning system on food and agriculture, World food programme, Special report Dec 19th, 2000. http://www.fao.org/docrep/004/x9188e/x9188e00.htm [on-line, May 1st, 2009] [13] FORKUTSA I. (2006): Modeling water and salt dynamics under irrigated cotton with shallow groundwater in the Khorezm region of Uzbekistan. Dissertation. ZEF Series in Ecology and Development, No. 37, 158 p. [14] IBRAKHIMOV M. (2004): Spatial and temporal dynamics of groundwater table and salinity in Khorezm (Aral Sea Basin), Uzbekistan. Dissertation. ZEF Series in Ecology and Development, No. 23. 175p. [15] INTERNATIONAL CRISIS GROUP (2009): Annual Report 2009, Review 2008, Plans for 2009. http://www.crisisgroup.org/home/index.cfm?l=1&id=4343 [available on-line, May 8th, 2009] [16] JPL – JET PROPULSION LABORATORY (2004): ASTER, Advanced Spaceborne Thermal Emission and Refection Radiometer. California Institute of Technology for the NASA. http://asterweb.jpl.nasa.gov/ [on-line, April 18th, 2009] [17] MÜLLER M. (2006): A General Equilibrium Approach to Modeling Water and Land Use Reforms in Uzbekistan. Dissertation, Bonn, Germany. [18] PROCHÁZKA S., MACHÁČKOVÁ I., KREKULE J., ŠEBÁNEK J., GLOSER J., HAVEL L., NÁTR L., PRÁŠIL I., SLADKÝ Z., ŠANTRŮČEK J., TESAŘOVÁ M. et VYSKOT B. (1998): Fyziologie rostlin. Academia, Praha, Czech republic, p. 484. ISBN 8020005862 [19] SNA - SEBAL NORTH AMERICA (2009). http://www.sebal.us/ [on-line, 6th April, 2009]. [20] STRAHLER ALAN & STRAHLER ARTHUR (2006): Introducing Physical
57 Geography. John Wiley & Sons, Fourth Edition, USA. ISBN 0-471-67950-X [21] UNDP (2007): WATER Critical Resource for Uzbekistan’s Future. United Nations Development Programme Uzbekistan, Tashkent. [22] USDA (2006): Briefing Rooms, Cotton: Trade. United States Department of Agriculture, Economic Research Service, updated date July 14th 2006, http://www.ers.usda.gov/briefing/cotton/trade.htm [on-line, May 1st, 2009]. [23] VISVAR R. A., KIM E. E. (2006): Республика Узбекистан. Государственний комитет Республики Узбекистан по земельным ресурсам, геодезии, картографии и государственному кадастру. Tashkent, Uzbekistan. [24] WIKI (2009): Evapotranspiration. Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/wiki/Evapotranspiration, http://en.wikipedia.org/wiki/File:Surface_water_cycle.svg [on-line, May 13th, 2009] [25] YDQ – YERGEODEZKADASTR DAVLAT QO'MITASI (2007): Xorazm Viloyati, Masshtab 1:400 000. O'zbekiston respublikasi yer resurslari, geodeziya, kartografiya va davlat kadastri davlat qo'mitasi, Tashkent. ISBN 978-9943-15-077-5 [26] ZAVGORODNYAYA D. (2006): Water Users Association in the Republic of Uzbekistan: Theory and Practice. Dissertation, Bonn, Germany.
[27] ZEF (2009): Economic and Ecological Restructuring of Land and Water Use in the Khorezm Region (Uzbekistan): A pilot Project in Development Research. Official website, http://www.zef.de/khorezm.0.html [on-line, May 8th, 2009] [28] ZEIDLER J. (2009): Field-based agricultural land-use classication and analysis in Khorezm, Uzbekistan for the years 2004 to 2007. Master thesis, Bayreut, Germany.
58 APPENDICES
Appendix 1: Map of the instantaneous evapotranspiration in the Khorezm region (Uzbekistan) during the years 2004 to 2007. (Map of ET in Khorezm)
Appendix 2: Actual evapotranspiration per fields – relative distribution in Khorezm region (Uzbekistan) during the years 2004 – 2007. (Relative ET in Khorezm)
Appendix 3: Actual evapotranspiration per fields – relative distribution in the WUA Koramon during the years 2004 – 2007. (Relative ET in Koramon)
Appendix 4: Statistical characteristics of actual evapotranspiration for the early and later scenes per different land cover categories.
59 Appendix 4: Statistical characteristics of actual evapotranspiration for the early and later scenes per different land cover categories.
SCENE COUNT AREA MIN MAX MEAN STD SUM* [km] [mm/hr] [mm/hr] [mm/hr] [mm/hr] 2005 May Undefined 8 1800 0,072722 0.343737 0.233420 0.092145 2 Cotton 1439003 323776000 0,000008 0.828975 0.263963 0.142431 379843 Wheat – Rice 406768 91522800 0,019608 0.842675 0.585435 0.111777 238136 Wheat 52624 11840400 0,009873 0.785206 0.616896 0.111156 32464 Wheat – other 118903 26753200 0,016640 0.787421 0.535497 0.113583 63672 Rice 533486 120034000 0,000007 0.917801 0.439421 0.167146 234425 Fallow 23696 5331600 0,000001 0.777716 0.370176 0.226663 8772 Other 38155 8584880 0,000031 0.782191 0.415302 0.192693 15846 2006 June Undefined 50815 11433400 0,000220 0.907618 0.395574 0.191403 20101 Cotton 1156684 260254000 0,000004 0.913647 0.247859 0.125495 286694 Wheat – Rice 314984 70871400 0,022885 0.838348 0.513381 0.094921 161707 Wheat 172678 38852600 0,032188 0.829446 0.474181 0.086075 81881 Wheat – other 256829 57786500 0,034370 0.872718 0.484697 0.093507 124484 Rice 548122 123327000 0,000014 0.938672 0.503450 0.216653 275952 Fallow 45417 10218800 0,000021 0.781629 0.274210 0.130536 12454 Other 80431 18097000 0,000844 0.821499 0.296441 0.148486 23843 2007 June Undefined 8 1800 0,029166 0.320784 0.166218 0.110722 1 Cotton 1190561 267876000 0,000000 0.916003 0.171990 0.131210 204764 Wheat – Rice 222307 50019100 0,000004 0.855413 0.476830 0.168196 106003 Wheat 61412 13817700 0,002771 0.829798 0.522114 0.136396 32064 Wheat – other 646473 145456000 0,000028 0.837002 0.399678 0.154266 258381 Rice 311168 70012800 0,000017 0.901299 0.296143 0.187928 92150 Fallow 32763 7371680 0,000003 0.744886 0.155638 0.131515 5099 Other 108730 24464300 0,000007 0.879065 0.263710 0.183228 28673 * count of scene pixels multiplied by MEAN value
60 SCENE COUNT AREA MIN MAX MEAN STD SUM* [km] [mm/hr] [mm/hr] [mm/hr] [mm/hr] 2004 July Undefined 97 21825 0.210110 0.760381 0.642119 0.099932 62 Cotton 1648148 370833000 0.000368 0.865645 0.542385 0.106544 893932 Wheat – Rice 126564 28476900 0.039069 0.879709 0.664349 0.085680 84083 Wheat 159676 35927100 0.000004 0.745879 0.318929 0.159700 50925 Wheat – other 210932 47459700 0.008300 0.791930 0.550188 0.093986 116052 Rice 387135 87105400 0.082315 0.885985 0.689606 0.075639 266971 Fallow 49535 11145400 0.000014 0.764659 0.275974 0.137635 13670 Other 45440 10224000 0.044414 0.843903 0.580648 0.113227 26385 2005 July Undefined 8 1800 0.660235 0.790962 0.742901 0.043420 6 Cotton 1454276 327212000 0.000003 0.979210 0.619287 0.155272 900615 Wheat – Rice 406774 91524200 0.010881 1.018080 0.664217 0.149442 270186 Wheat 50278 11312600 0.000012 0.895229 0.269219 0.166940 13536 Wheat – other 118847 26740600 0.002307 0.929975 0.573159 0.140352 68118 Rice 535646 120520000 0.000056 1.033340 0.779782 0.123929 417687 Fallow 22973 5168930 0.000002 0.779391 0.227226 0.130553 5220 Other 38549 8673530 0.004473 0.927869 0.622526 0.158664 23998 2006 July Undefined 50850 11441300 0.173181 1.389670 0.642014 0.127840 32646 Cotton 1159974 260994000 0.000000 0.999802 0.529943 0.118744 614720 Wheat – Rice 314985 70871600 0.000000 0.904199 0.645042 0.073300 203179 Wheat 172678 38852600 0.000000 1.039430 0.446774 0.144881 77148 Wheat – other 256829 57786500 0.036311 0.983749 0.546791 0.097244 140432 Rice 549591 123658000 0.037891 0.915729 0.664945 0.072320 365448 Fallow 46896 10551600 0.000000 1.016010 0.315889 0.175666 14814 Other 80662 18149000 0.000000 0.837609 0.601528 0.078009 48521 2007 July Undefined 8 1800 0.260784 0.401754 0.297918 0.042082 2 Cotton 1240389 279088000 0.000457 0.833949 0.393395 0.136345 487963 Wheat – Rice 222446 50050400 0.067525 0.873992 0.501385 0.161415 111531 Wheat 61277 13787300 0.000084 0.741909 0.261246 0.104596 16008 Wheat – other 648217 145849000 0.000254 0.836439 0.464524 0.124736 301113 Rice 322750 72618800 0.027597 0.879886 0.643496 0.115922 207688 Fallow 37076 8342100 0.000092 0.698216 0.233580 0.097128 8660 Other 112145 25232600 0.001852 0.814303 0.507835 0.139770 56951 * count of scene pixels multiplied by MEAN value
61 ABBREVIATIONS ASTER – The Advanced Spaceborne Thermal Emission and Refection Radiometer CERES – Clouds and the Earth's Radiant Energy System EOS – Earth Observing System of NASA ET – Evapotranspiration GIS – Geographical Information System LAI – Leaf Area Index LST – Land Surface Temperature METRIC – Mapping Evapotranspiration with Internalized Calibration MISR – Multi-angle Imaging Spectro-Radiometer MODIS – Moderate-resolution Imaging Spectroradiometer MOPITT – Measurements of Pollution in the Troposphere NASA – National Aeronautics and Space Administration NDVI – Normalized Difference Vegetation Index SAVI – Soil Adjusted Vegetation Index SEBAL – Surface Energy Balance Algorithm for Land STD – Standard Deviation (of the mean value) SWIR – Shortwave Infrared TIR – Thermal Infrared UNESCO – United Nations Educational, Scientific and Cultural Organization VNIR – Visible and Near Infrared WUA – Water User's Association ZEF – Zentrum für Entwicklungsforschung (The Center for Development Research)
62