UNIVERSITY OF CALGARY. Development of a Remote Sensing-Based Agriculture Monitoring Drought Index and Its. Application Over Semi-Arid Region

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1 UNIVERSITY OF CALGARY Development of a Remote Sensing-Based Agriculture Monitoring Drought Index and Its Application Over Semi-Arid Region by Khaled Mohmmad Amin Ahmad Hazaymeh A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN GEOMATICS ENGINEERING CALGARY, ALBERTA FEBRUARY, 2016 Khaled Mohmmad Amin Ahmad Hazaymeh 2016

2 Abstract Agricultural drought is a natural disaster that usually occurs when the available water content goes below the optimal needs of the proper growth of plants during its growing season. It has enormous impacts on economic, environmental, and social sectors. In this study, our overall objective was to develop a fully remote sensing-based method for monitoring agricultural drought conditions and evaluate its performance over a semi-arid heterogeneous rainfed agricultural dominant landscape in Jordan. In general, remote sensing data having both high spatial and temporal resolutions would be required for evaluating agricultural drought conditions, as usually agriculture land cover would be relatively heterogeneous and small in size, while drought could occur during critical short time periods i.e., few days or weeks during the growing season. However, due to different technical and cost issues such high spatio-temporal remote sensing data are still unavailable. Thus, we opted to develop a spatio-temporal image-fusion model (STI-FM) to generate synthetic Landsat-8 like data with 30 m spatial and 8 day temporal resolutions upon combining regular Landsat-8 (having 30 m spatial with 16 day temporal resolutions) with moderate-resolution imaging spectroradiometer (MODIS)-based 8-day composite data having m spatial resolutions. Then, we used these fused data in developing the agricultural drought monitoring index (ADI) as a combination of three uncorrelated remote sensing-based agricultural drought related variables [i.e., normalized difference water index (NDWI), visible and shortwave drought index (VSDI), and land surface temperature (LST)]. Results showed that the proposed STI-FM was able to produce synthetic Landsat-8 data with strong accuracy (i.e., r 2 were in the range 0.71 to 0.90). The evaluation of agricultural drought conditions over the study area using the proposed remote sensing-based agricultural drought index showed high agreements such as 85% overall accuracy and 78% Kappa-values, when compared to ground based 8-day ii

3 standardised precipitation index (SPI) values. These strong results demonstrated that the proposed methods would be great in monitoring agricultural drought conditions at agricultural field scale (i.e., high spatial resolution) and short time periods (i.e., high temporal resolution). iii

4 Preface The outcomes of this research have been published and/or presented as listed below: Journal publications: 1. Hazaymeh, K.; and Hassan, Q.K. Fusion of MODIS and Landsat-8 surface temperature images: A new approach. PloS One 2015, 10, e :1-e : Hazaymeh, K.; and Hassan, Q.K. Spatio-temporal image fusion model for enhancing temporal resolution of Landsat-8 surface reflectance images using MODIS images. Journal of Applied Remote Sensing 2015, 9, Hazaymeh, K.; and Hassan, Q.K.; and He, J. On the development of remote sensing agricultural drought monitoring index and its implementation over Jordan. Geomatics, Natural Hazards and Risk (under review). Conference presentations 1. Hazaymeh, K.; and Hassan, Q.K.; and Rahaman, K.R. Satellite-based spatio-temporal data fusion: current status and its implications, Spatial Knowledge and Information, Banff AB. Canada, February Hazaymeh, K., and Hassan, Q.K. State of art review on agricultural drought monitoring: A remote sensing prospective, Spatial Knowledge and Information, Banff AB. Canada, February iv

5 Acknowledgements First, I would like to thank my supervisor, Dr. Quazi Hassan for his fundamental support to the completion of this thesis. His insights, encouragement and ability to keep me on track were helpful throughout the duration of this work. He has always provided me with useful advices and comments on how to best improve my work. Thank you for your patience and encouragement. Also, I would like to thank my supervisory committee members Prof. Rod Blais and Dr. Jianxun He, for their important inputs and suggestions provided to improve this work. Second, I appreciate the generosity of Yarmouk University in Jordan for providing me a four years scholarship to conduct my study. Also, many thanks to the University of Calgary, Schulich School of Engineering, and Department of Geomatics Engineering for providing other financial supports and coverages. Many thanks to NASA, USGS, and Mr. Ali Hayajneh from the Jordanian Ministry of Water and Irrigation for providing the required data for my research free of charge. Third, I am grateful to my parents and my wife for their support and encouragement. Without their constant encouragement it would not have been possible to finish this work. I would like to thank my brothers and sisters as will for their support. Finally, I thank all of my Earth Observation for Environment Laboratory group members especially Dr. Mostafa Mosleh and the staff of Geomatics Dept. for their support. v

6 Dedication To: my parents; my wife, Manar; my son, Mohammad; my brothers and sisters; and the remote sensing community. vi

7 Table of Contents Abstract... ii Preface... iv Dedication... vi Table of Contents... vii List of Tables... ix List of Figures and Illustrations...x List of Abbreviations... xiii CHAPTER ONE: INTRODUCTION Background Problem statement Research Objectives Thesis Structure...8 CHAPTER TWO: LITERATURE REVIEW Importance of monitoring agricultural drought Agricultural drought impacts around the world Agricultural drought effects on crops Current agricultural drought monitoring methods In-situ based agricultural drought monitoring methods Remote sensing based agricultural drought monitoring methods Spectral reflectance of vegetation to variations in water content Spectral reflectance of soils to variations in water content Remote sensing methods for agricultural drought monitoring Advantages of remote sensing in agricultural drought monitoring Synergic remote sensing/in-situ based methods Issues in the methods of agricultural drought monitoring Satellite image fusion Satellite based spatio-temporal data fusion methods Spatio-temporal adaptive fusion methods Unmixing-based spatio-temporal fusion methods Sparse representation-based spatio-temporal fusion methods Issues in the satellite based spatio-temporal data fusion methods Review of the most commonly used remote sensing platforms in agricultural drought monitoring Landsat characteristics MODIS characteristics...47 CHAPTER THREE: STUDY AREA AND DATA PRE-PROCESSING General description of the study area Data used Remote Sensing Data Ground-based precipitation data Data pre-processing MODIS data pre-processing...57 vii

8 Constraints in relation to the 8-day composite MODIS data Landsat-8 data pre-processing Converting DN-values into surface reflectance Converting DN-values into surface temperature Processing of ground-based precipitation data and its use in calculating SPI Processing of ground-based precipitation data Calculating SPI Processing of other geographical data...65 CHAPTER FOUR: METHODS Developing spatio-temporal image fusion model (STI-FM) for enhancing the temporal resolution of Landsat-8 land surface temperature (LST) images Validating the synthetic Landsat-8 LST images Evaluating the spatio-temporal image fusion model (STI-FM) with its required modifications for generating high spatio-temporal resolution Landsat-8 surface reflectance images Establishing relationships between MODIS images acquired at two different times Generating the synthetic Landsat-8 surface reflectance images at time two and its validation Developing a remote sensing-based method for monitoring agricultural drought conditions and its evaluation Calculating drought-related variables and determining uncorrelated ones Developing a remote sensing-based agricultural drought index Evaluating remote sensing-based agricultural drought index using SPI...76 CHAPTER FIVE: RESULTS AND DISCUSSION Evaluation of synthetic Landsat-8 LST images Relationship between two MODIS LST images Evaluating the synthetic Landsat-8 LST images Evaluation of STI-FM using surface reflectance images Evaluation of the relationships between MODIS images acquired at two different times Evaluation of the synthetic Landsat-8 surface reflectance images Evaluation of remote sensing-based agricultural drought index (ADI) Principal component analysis Evaluating the results of remote sensing-based agricultural drought index...93 CHAPTER SIX: CONCLUSIONS & RECOMMENDATIONS Concluding remarks Contribution to science Recommendations for future work REFERENCES APPENDICES viii

9 List of Tables Table 1.1: Description of the major operational satellite remote sensing instruments used in environmental studies... 6 Table 2.1: Most commonly used in-situ based agricultural drought monitoring indices Table 2.2: Description of the most commonly used vegetation greenness related variables in agricultural drought monitoring Table 2.3: Most commonly used remote sensing based meteorological related variables in agricultural drought monitoring Table 2.5: Description of the most commonly used vegetation wetness related variables in agricultural drought monitoring Table 2.4: Description of the most commonly used surface wetness condition related variables in agricultural drought monitoring Table 2.6: Description of the most commonly used synergic remote sensing/in-situ based agricultural drought monitoring indices Table 2.7: MODIS spectral bands and its major use Table 3.1: Description of Landsat-8 and MODIS data used in this research Table 3.2: Description of the 19 agro-climate stations used in this research Table 5.1: Statistical comparisons between actual and synthetic Landsat-8 LST images. The LST values are given in Kelvin Table 5.2: Eigenvalues, percentage of variance, and cumulative variance explained by each principal component for four Landsat-8 images acquired during the growing season Table 5.3: Degree of correlation/loading between each variable and each PC Table 5.4: Agreements between remote sensing-derived agricultural drought index classes and corresponding point-based SPI-values Table 5.5: Examples of confusion matrices between remote sensing-derived agricultural drought index (ADI) and SPI classes, i.e., (a) ADI vs. SPI-4, and (b) ADI vs. SPI ix

10 List of Figures and Illustrations Figure 1.1: Schematic diagram illustrating the drought process, and the relationship between meteorological, agricultural, and hydrological drought. Uncorrelated of the time scale economic, social and environmental impacts are shown at the bottom of the chart indicating that they can occur at any stage during drought occurrences. (NDMC 2006)... 2 Figure 1.2: Schematic diagram illustrating the structure of the thesis, [SPI: standardized precipitation index; STI-FM: spatio-temporal image fusion model, ADI: agricultural drought index; LST: land surface temperature... 9 Figure 2.1: World water stress distribution map (World Resources Institute, 2015) Figure 2.2: Example of laboratory-measured reflectance of single Magnolia leaf to different levels of relative water content within the spectral range 0.4µm to 2.6µm (Jensen 2007) Figure 2.3: Example of laboratory-measured spectral reflectance of soil with different levels of soil moisture content (SMC) (Fabre et al. 2015) Figure 2.4: (a) triangular and (b) trapezoidal forms based on a relationship between LST and vegetation greenness variables (Li et al with modifications) Figure 2.5: Conceptual schematic diagram of the spatio-temporal fusion techniques; (a) using two input coarse spatial resolution images and one fine spatial resolution image; (b) using three input coarse spatial resolution images and two fine spatial resolution images.. 37 Figure 2.6: The historical/future time line of Landsat missions Figure 3.1: (a) Topographic map of Jordan; (b) map of Jordan illustrating long-term average annual precipitation isohyets distribution; (c) Major land use/cover map in the study area and locations of the agro-climate stations used in this study Figure 3.2: Long-term average monthly rainfall/precipitation distribution in millimeters in the study area from 1984 to Figure 3.3: Comparison between the average annual rainfall in millimeters in the study area from 1984 to 2015, and the long-term average rainfall represented by the dashed line at approximately mm Figure 3.4: Schematic diagram illustrating the ways of calculating 8-day SPI-values around the Landsat-8 actual/synthetic image dates. For example, SPI-1 represents the SPI-value of the total precipitation during 7 days before the image date plus the amount of that date comparing to the long-term precipitation of the same dates. SPI-5 represents the SPI value of the total precipitation during 3 days before the image date plus the amount of that date and 4 days after that date comparing to the long-term precipitation of the same dates x

11 Figure 4.1: Schematic diagram of the methodology for generating synthetic Landsat-8 LST images Figure 4.2: Schematic diagram of the proposed spatio-temporal image-fusion model (STI- FM) for enhancing the temporal resolution of Landsat-8 surface reflectance images Figure 4.3: Conceptual relationships between the two MODIS images at two different times Figure 5.1: Relation between two MODIS LST images acquired at time 1 and time 2 [i.e., M(t1) and M(t2)] Figure 5.2: 8-day average: (i) air temperature at Marka climate station; (ii) MODIS-derived 8-day LST at Marka station; and (iii) MODIS-derived study area-specific average LST; during the period 18 June 2013 to 12 August Figure 5.3: Comparative example between actual (a) and synthetic (b) Landsat-8 LST images for 4 July 2013). The panels [(a1), (b1)], [(a2), (b2)], [(a3), (b3)], and [(a4), (b4)] shows enlarged views over forest, water body, agricultural lands, and urban area respectively for both actual and synthetic images Figure 5.4: Panels (a), (b), (c), (d), and (e) represent histograms of actual (black solid line) and synthetic (gray dashed line) images for the whole study area, forests, water body, agricultural fields, and urban area, respectively Figure 5.5: Spatial profiles of the pixels along ~ 50 km northwest southeast transect travelling through various land cover types for actual LST image (i.e., the black solid line in Figure 5.3a) and synthetic LST image (i.e., the gray dashed line in Figure 5.3b) Figure 5.6: Scatter plots of the relation between actual and synthetic Landsat-8 LST image for (a) 4 July 2013, (b) 20 July 2013, and (c) 5 August The dotted and continue lines represent 1:1 and regression line respectively Figure 5.7: Relation between 8-day composite of MODIS surface reflectance images acquired at time 1 [i.e., M(t1)] and time 2 [i.e., M(t2)] for the spectral bands of red [panels (a)-(d)], NIR [(e)-(h)], and SWIR2.13µm [(i)-(l)] during the period 2 June to 12 August Figure 5.8: Example comparison between pseudo-colour composites images by putting the NIR, red and SWIR spectral bands in the red, green, and blue colour-planes of the computer respectively for actual and synthetic Landsat-8 images during 18 June Figure 5.9: Relation between the actual Landsat-8 surface reflectance image and its corresponding synthetic Landsat-8 surface reflectance images for the red panels [(a)- (d)], NIR [(e)-(h)], SWIR2.2µm [(i)-(l)] spectral bands. The dotted and continue lines represent 1:1 and regression line respectively Figure 5.10: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on actual xi

12 Landsat-8 data acquired on 13 Feb coinciding with the period of vegetation germination. Note that the white colored areas represented non-agricultural lands Figure 5.11: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on synthetic Landsat-8 data generated on 10 Apr at the peak period of the vegetation growth. Note that the white colored areas represented non-agricultural lands Figure 5.12: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on synthetic Landsat-8 data generated on 16 Feb coinciding with the period of vegetation germination. Note that the white colored areas represented non-agricultural lands Figure 5.13: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on actual Landsat-8 data generated on 21 Apr coinciding with the period of vegetation germination. Note that the white colored areas represented non-agricultural lands xii

13 List of Abbreviations Abbreviation Definition ADI Agricultural Drought Index ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer AVHRR Advanced Very High Resolution Radiometer CMI Crop Moisture Index CWSI Crop Water Stress Index ESI Evaporative Stress Index ESTARFM Enhanced STARFM ET Evapotranspiration FAO Food and Agriculture Organization GOES Geostationary Operational Environmental Satellites HDI Hybrid Drought Index ISDI Integrated Surface Drought Index L(t1) Landsat At Time 1 LST Land Surface Temperature M(t1) MODIS At Time 1 M(t2) MODIS At Time 2 M(t3) MODIS At Time 3 MERIS MEdium Resolution Imaging Spectrometer mestarfm Modified ESTARFM MID Multi-Index Drought MODIS Moderate-resolution Imaging Spectroradiometer MPDI Modified Perpendicular Drought Index MS Multi Spectral NDII Normalized Difference Infrared Index NDTI Normalized Difference Temperature Index NDVI Normalized Difference Vegetation Index NDWI Normalized Difference Water Index NIR Near Infrared xiii

14 Pan PCA PDI PSDI SADFAT SMDI SPI SPOT SPSTFM STARFM STAVFM SWIR synth-l TCI TIR TVDI TVX USDM VCI VegDRI VegOut VHC VIs VTCI WDI WMO Panchromatic Principal Component Analysis Perpendicular Drought Index Palmer Drought Severity Index Spatio-Temporal Adaptive Data Fusion Algorithm For Temperature Soil Moisture Drought Index Standardized Precipitation Index Satellite Pour l'observation de la Terre Sparse Representation-Based Spatio-Temporal Reflectance Fusion Model Spatial And Temporal Adaptive Reflectance Fusion Model Spatial And Temporal Adaptive Vegetation Index Fusion Model Shortwave Infrared Synthetic Landsat Images Temperature Condition Index Thermal Infrared Temperature-Vegetation Dryness Index Temperature-Vegetation Contextual US Drought Monitor Vegetation Condition Index Vegetation Drought Response Index Vegetation Outlook index Vegetation Health Index Vegetation Indices Vegetation Temperature Condition Index Water Deficit Index World Meteorological Organization xiv

15 Chapter One: Introduction 1.1 Background Drought is generally defined as a deficiency of water over an extended period of time causing problems to some activities, groups, or environmental sectors (Mishra and Singh 2010). It can be broadly classified into four categories, such as (Wilhite et al. 2007): Meteorological drought: a deficiency of precipitation comparing to average conditions over a specific period of time (e.g., weeks, months, or years). Agricultural drought: occurs when the available water content goes below the optimal needs of the proper growth of plants during its growing season resulting in growth stress and yield reduction. Hydrological drought: the reduction of the availability of natural and/or artificial surface or ground water resources. Socio-economic drought: occurs when human activities are affected by one or more of the previous three types of drought. These types of drought are interactive with each other (see Figure 1.1); however, our focus in this research was concentrated on agricultural drought because it is one of the most important issues in terms of economic, food security, and social stability in most of the countries, especially in developing countries including Jordan which is the selected study area in our research. 1

16 Figure 1.1: Schematic diagram illustrating the drought process, and the relationship between meteorological, agricultural, and hydrological drought. Uncorrelated of the time scale economic, social and environmental impacts are shown at the bottom of the chart indicating that they can occur at any stage during drought occurrences. (NDMC 2006) 2

17 Generally, agricultural drought is led by two factors: (i) short-term precipitation shortage that reduces soil moisture levels, and/or (ii) temperature increase that causes increase in evapotranspiration levels above water supply. These two factors often occur in semi-arid environments such as those of Jordan - our study area - (Al-Bakri et al. 2011). It has enormous impacts on economic, environmental, and social sectors, such as reducing crop yield and rangeland productivity; increasing livestock mortality rates; reducing farmers income; increasing food prices; and increasing unemployment, crime and migration rates, etc. (Keshavarz and Karami 2014). The severity of these impacts depends on the timing, intensity, spatial extent and duration of drought during the growing season (Mishra and Singh, 2010). For example, if drought occurred occasionally over long time period, plants might be able to reach maturity before the drought caused severe impacts. On the other hand, if it occurred frequently over a short time period, it might severely impact the plants growth and production. Therefore, understanding the causes and consequences of agricultural droughts are very important for food production, planning and management. This requires the comprehension of drought events historically and eventually during its occurrences in the affected regions (De-Pauw 2005; Hu et al. 2008). To date, many methods have been developed to investigate different drought properties, those are known as drought indices (Zargar et al. 2013). These indices are mathematical expressions based on empirical and/or physical approaches to study drought either quantitatively or qualitatively, which can be more effective than the direct use of raw data (Otun and Adewumi, 2009). These indices can be divided into three main groups based on the data source, i.e. in-situ indices, remote sensing-based indices and synergic-based indices (Niemeyer, 2008). Recently, many countries have established different frameworks for monitoring and mitigating agricultural drought impacts 3

18 on their economic, social, and environmental sectors (Guha-sapir, 2013). Currently, remote sensing satellites provide advanced products for agricultural drought monitoring that include vegetation indices, precipitation information, evapotranspiration, and soil moisture measurements. Although this provides adequate spatial coverage and continuous data, however, the trade-off between their spatial and temporal resolutions might restrict their use at agriculture fields level and during the plants growing seasons (Roy et al. 2014). However, recent advances in remote sensing data fusion of multi satellite data have assisted in mitigating these limitations (Walker et al. 2012; Anderson et al. 2011). 1.2 Problem statement Most of the currently used methods in agricultural drought monitoring are primarily based on hydro-meteorological variables (Zargar et al. 2013). Among these methods, the Palmar drought severity index (Palmar 1965), crop moisture index (Palmar 1968) and standardized precipitation index (McKee 1993) are the most frequently used ones. However, these indices are sharing three major aspects such as: (i) they are point-based hydro-climate measured variables, thus they might fail to accurately address the spatial details of agricultural drought (Kanellou et al. 2008); (ii) they can be spatially interpolated through different geospatial techniques; however, different output maps could be generated even using the same input variables (Li and Heap 2013) (iii) they were largely subject to data gaps and discontinuities. In addressing these uncertainties, remote sensing based agricultural drought monitoring methods have been used, as this data source has accounted for the spatial and temporal distribution issues. 4

19 Most of these methods were developed on the basis of spectral indices (Zargar et al., 2013). However, though these methods demonstrated capabilities for the spatio-temporal monitoring of agricultural drought conditions in the targeted areas; the trade-off between their spatial and temporal resolutions produced different uncertainties. For instance none of the operational remote sensing systems used in environmental studies is able to provide high spatial and high temporal resolution data (see Table 1.1). Note that for the practical monitoring of agricultural drought, high spatial resolution (i.e., 30 meter) and high temporal resolution (i.e., weekly) are the best requirements (Roy et al. 2014, Wu and Wilhite 2004). These particular issues would be critical in agricultural studies as most of the fields might be smaller than the employed spatial resolution of satellite images, or changes in fields could be faster than the revisit time period of satellite. Currently, Landsat-8 imagery would compensate the spatial resolution (Roy et al. 2014); however, it would be very difficult to obtain cloud-free images for the entire growing season. Also, its temporal resolution (i.e., 16 days) might restrict their application in agricultural drought monitoring. On the other hand, MODIS data would be suitable in terms of temporal resolution (daily, 8-day composite), however, their low spatial resolution (i.e., 250 m, 500 m, and 1000 m) would be the greatest challenge. In this context, further research would be required in order to enhance the spatial and temporal resolutions of the employed images in order to develop more practical and appropriate agricultural drought monitoring system for the purpose of reducing the losses and enhance the agriculture benefits and food security strategies. 5

20 Table 1.1: Description of the major operational satellite remote sensing instruments used in environmental studies Sensor Spatial Resolution (m) * Spectral Resolution Temporal Resolution (days) Radiometric Resolution (bits) Landsat-8 Pan. 15 MS. 30 TIR Pan SPOT-5 ALOS Pa. 2.5, 5 MS. 10, 20 Pan. 2.5 MS Pan Pan AVHRR MODIS MS. 250, 500 TIR Hyperion ALI Pan. 10 MS Pan ASTER MS. 15 and 30 TIR * Pan. = panchromatic band; MS = multi-spectral bands; TIR = thermal infrared band 6

21 1.3 Research Objectives The overall objective in this research is to develop an operational remote sensing based method for agricultural drought monitoring. The specific objectives are to: 1. develop a spatio-temporal image fusion model (STI-FM) for enhancing the temporal resolution (i.e., from 16 to 8 days) of Landsat-8 land surface temperature (LST) images by fusing LST images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS); and implement the developed algorithm over a heterogeneous semi-arid study area in Jordan, Middle East. 2. evaluate the spatio-temporal image fusion model (STI-FM) with its required modifications in generating high spatio-temporal resolution Landsat-8 surface reflectance images by utilizing the MODIS images; and assess its applicability over a heterogeneous agriculture dominant semi-arid region in Jordan. 3. develop a remote sensing-based index for monitoring agricultural drought conditions; and to evaluate its performance over a semi-arid heterogeneous rainfed agricultural dominant landscape in Jordan, Middle East. 7

22 1.4 Thesis Structure This thesis has been structured in six chapters. Chapter 1 provides background introduction of drought definition and types, and its disastrous consequences on affected areas with more focusing on agricultural drought. It also contains the problem statement and objectives of our research in addition to its structure. Chapter 2 presents general literature review of agricultural drought and the current operational agricultural drought monitoring methods with intensive discussion of their limitations and advantages; and a critical review of current spatio-temporal data fusion models. Chapter 3 demonstrates the major characteristics of the study area, and the required data and their pre-processing. Chapter 4 illustrates the methodologies of developing (i) two remote sensing models for enhancing the spatio-temporal resolution of Landsat-8 surface temperature and surface reflectance data using MODIS data. (ii) An operational remote sensing-based agricultural drought monitoring index. Chapter 5 presents the findings of this research. It covers the results of each analyzed component including the accuracy of the generated Landsat-8 like data and the evaluation of agricultural drought index and its implementation in the study area. Finally, Chapter 6 summarizes the research outcomes, and the recommendations for future work. This work has led to publications in peer-reviewed journals and different chapters highlight the work in much detail. 8

23 Figure 1.2: Schematic diagram illustrating the structure of the thesis, [SPI: standardized precipitation index; STI-FM: spatio-temporal image fusion model, ADI: agricultural drought index; LST: land surface temperature 9

24 Chapter Two: Literature Review In this chapter, we review and summarize the following three issues, (i) importance of monitoring agricultural drought ; (ii) the current operational agricultural drought monitoring methods and their limitations, including in-situ-based, remote sensing-based, and synergic-based agricultural drought monitoring methods, with more concentration on remote sensing part; (iii) remote sensing data fusion techniques with emphasis on the newly developed spatio-temporal data fusion techniques in particular. These reviews provide an overall understanding about the developments in these fields which encourages further research in these areas. 2.1 Importance of monitoring agricultural drought Continuous water supply throughout the growing season is required for the proper growth of agricultural crops. This can be met through irrigation, however, with the absence of irrigation facilities especially in developing countries and semi-arid regions, crops are mainly relying on the spatial and temporal distribution of precipitation, which, in turn, controls crops yield and production. Thus, effective and timely monitoring of agricultural drought during the growing season might be greatly helpful in minimizing agricultural losses. Agricultural drought monitoring is one of the three main actions in agricultural drought risk management plans, which also include drought preparedness and drought response actions. Monitoring actions include: ongoing monitoring and evaluating surface wetness conditions, precipitation amounts and patterns; and temperature in the agricultural areas during the growing 10

25 season. The ongoing monitoring includes measuring different agro-climate parameters such as precipitation, temperature, evaporation, soil moisture, etc. in near real time collection in order to develop adequate agricultural drought evaluation indicators. These evaluations are then interpreted in drought reports that objectively and accurately determine the severity, extent and duration of drought conditions. Usually, such combined information helps in providing guidance for decision makers (i.e., government ministries) and farmers to the existing situation. The drought preparedness actions focus on the efforts that increase the awareness and readiness of decision makers and farmers, especially during non-drought periods, to the proper respond to the next drought event if occurred. Lastly, drought response actions provide appropriate strategies during and immediately following a drought events to reduce drought impacts on agricultural operations. (Alberta Agriculture and Forestry, 2013) Agricultural drought impacts around the world Agricultural drought is a widespread natural hazard phenomenon (see Figure 2.1) recently, large scale intensive droughts events have been occurred and affected large areas in Europe, Africa, Asia, Australia, South America, Central America, and North America. For example, over the 1980 to 2003 growing seasons, in the United States, drought accounted for $144 billion (41.2%) of the estimated $349 billion total cost of all weather-related disasters (Ross and Lott, 2003). In Canada, the Canadian Prairies are the most drought susceptible area due to high variability of precipitation, for example, the drought event during 2001 and 2002 growing seasons resulted in an estimated loss of $3.6 billion in agricultural production (Wheaton et al. 2005). In Australia the winter cereal crop was reduced by 36% and cost around AUD$3.5 billion during the 2006 drought event (Wong 11

26 et al. 2009). During the past 30 years in Europe, several major drought events occurred. The most severe drought event in the Iberian Peninsula in 2005 caused 10% reduction in the overall European cereal yields. Since 1991, the European Union has estimated a yearly average economic impact of drought by 5.3 billion (European Communities, 2007). In Asia, the Intergovernmental Panel on Climate Change reported that most of rice, maize, and wheat production has declined in many Asian countries in the last few decades (Bates et al. 2008). For examples, around 60 million people in Central and Southwest Asia were affected by drought during growing seasons, and around 40 million hectares of agricultural areas were affected in China alone (Zhang, 2003). In India, drought has been reported at least once in every three years in the last five decades (FAO, 2002; World Bank, 2003). The West Asia - include Jordan - and North Africa region experienced several drought events in the last three decades represented by reduced food production. For example, in 1999, aggregate cereal production in the West Asia sub-region was 16% lower than in the previous year and 12% lower than the average over the previous five years. In Turkey, the grain production fell by 6% as compared to the fiveyear average. In Iraq, rainfall was 30% below average resulting in 70% failure in rain-fed agriculture crops. Similar situation faced North African countries such as, Morocco, Algeria, and Tunisia during that drought event as cereal crop was reduced by 31% comparing to the previous year s harvest (De Pauw 2005). 12

27 Figure 2.1: World water stress distribution map (World Resources Institute, 2015) Agricultural drought effects on crops Water stress affects numerous plant processes during plant growth and development. These effects can be grouped into four phenomena such as (Boken et al. 2005): 1. Germination: after sowing the fields, seeds need to be imbibed with sufficient amount of water to initiate growth. Once seeds start breaking its hard crust, they become highly susceptible to water stress. For instance, a light rain may imbibe seeds with sufficient water 13

28 to germinate, however, if no additional rain follow within next few days, seeds may not be able to emerge from soil or roots may be too small for water up-tack from deep soil. Actually, one of the most common agricultural losses in rainfed agricultural areas emerge due to re-planting crops as a result of crops germination failure with the first rain. 2. Turgor-mediated phenomenon: Once seedlings establish, water stress levels start to increase with longer time lag between successive rainfall events. This may result in smaller leaves or shorter plants which is common with mild drought conditions. Some common reactions from plants during this period are the stomatal closure or leaves rolling, though this may reduce water loss, it meanwhile reduces the uptake of carbon dioxide needed for photosynthesis process and decrease the transpiration. 3. Substrate-mediated phenomenon: when mild drought conditions develop to moderate or severe drought conditions, the process of carbon dioxide assimilation gradually declines because of increase in stomatal closure, leaf wilting, and leaf rolling. This cause reduction in the substrate available for cell division and growth, which may result in the form of leaves appearance rate reduction, branching reduction, or reproductive growth delay. Perhaps the most severe drought response during this phase is the lack of developed seed set. Once plants abort the seed, they may no longer grow and potential yield maybe lost. 4. Desiccation effects phenomenon: Even at relatively moderate drought conditions, plant leave begin to fire, turn brown, and desiccate. As drought intensifies, leaves and roots 14

29 eventually die and only viable seeds can survive, which reflect in huge agricultural yield and production losses. 2.2 Current agricultural drought monitoring methods Agricultural drought monitoring methods can be divided into three main groups according to their data source, such as in-situ based methods, remote sensing-based methods, and synergic-based methods (Niemeyer, 2008) In-situ based agricultural drought monitoring methods The in-situ based agricultural drought monitoring indices are the most accurate and historic ones among the others (Maes and Steppe 2012). They are based on ground measurements of hydroclimatic variables (including precipitation, temperature, relative humidity, and soil water content etc.) available from climatic, agricultural, and hydrologic stations; and able to provide quantitative and qualitative information over an area of interest (Kanellou et al. 2008). Some of the examples include: (i) Palmer drought severity index (PSDI) uses precipitation and temperature (Palmer 1965); (ii) crop moisture index (CMI) incorporates soil moisture, precipitation and temperature (Palmar 1968); (iii) crop water stress index (CWSI) is based on actual and potential evaporation (Jackson et al. 1981); (iv) crop specific drought index (CSDI) employs temperature, precipitation, evapotranspiration information (Meyer et al. 1993); and (iv) standardized precipitation index (SPI) uses precipitation regimes (McKee et al. 1993). Although some of these indices were initially developed for meteorological drought; however, they were effectively applied in agricultural drought monitoring in different studies because agriculture is often the first affected sector by the onset of drought due to precipitation deficiency (Quiring and Papakryiakou 2003, Paulo and 15

30 Pereira 2006, Pashiardis and Michaelides 2008). Table 2.1 shows the most commonly used in-situ based agricultural drought monitoring indices. Note that the World Meteorological Organization (WMO) recommends that all national meteorological, agricultural and hydrological services should use SPI for monitoring drought (WMO, 2012) due to its simplicity and flexibility to monitor drought at either weekly or 10 days, 1, 3, 6, 9, 12 and 24 months intervals, with four drought classes (i.e., near normal, moderate, severe and extreme droughts) (McKee et al. 1993). 16

31 Table 2.1: Most commonly used in-situ based agricultural drought monitoring indices Palmer Drought Severity Index (PSDI) Crop Moisture Index (CMI) Index Expression / Inputs * Reference Temperature, precipitation, soil moisture, evapotranspiration Temperature, precipitation, soil moisture Palmer, 1965 Palmer, 1968 Stress Degree Days (SDD) SSD= (Tc-Ta) Idso et al Crop Water Stress Index (CWSI) CWSI = 1- AET PET Jackson et al Standardized Precipitation Index (SPI) Precipitation McKee et al Crop Specific Drought Index (CSDI) Evapotranspiration Deficit Index (ETDI) Soil Moisture Drought Index (SMDI) Temperature, precipitation, evapotranspiration ETDIj = 0.5ETDIj-1 + WSAj 50 SMDIj = SD 25t+25 Meyer et al Narasimhan and Srinivasan, 2005 Narasimhan and Srinivasan, 2005 * Tc and Ta are the canopy and air temperature respectively; AET and PET are the actual and potential evapotranspiration respectively, WSA is the weekly water stress anomaly; SD is the soil water deficit; t is the time. In general, these indices usually provide very accurate estimates of agricultural drought conditions at the point locations where the input variables are acquired. However, the uneven spatial distribution of the hydro-meteorological stations across the landscape often imposes uncertainty in delineating spatial context. In order to address this, geographic information system (GIS)-based interpolation techniques (e.g., inverse distance, krigging, nearest neighbour, etc.) are usually 17

32 employed. However, these techniques often generate different outcomes despite using the same set of input variables (Li and Heap 2013) Remote sensing based agricultural drought monitoring methods In order to address the spatial context of agricultural drought in-situ based indices, one of the viable alternatives is the use of remote sensing based techniques. These techniques were based on analysing changes in the spectral and thermal properties of vegetation and soil surface due to the effects of agricultural drought (Anjum et al. 2011), which, in turn, played significant role in monitoring agricultural drought during plants growing-season (Anderson et al. 2011). It is apparent that, if remotely sensed data are used to evaluate agricultural drought, it is necessary to understand the spectral responses of vegetation and soil to variation in water content before discussing the remote sensing-based methods used for agricultural drought monitoring, Spectral reflectance of vegetation to variations in water content The response of spectral properties of vegetation to variation in water status have been analysed in different studies. It has been reported that these properties were increased with higher levels of water stress (Jensen 2007, Wang and Qu, 2009; Tang et. al. 2010). Figure 2.2 shows an example of laboratory- measured reflectance of single Magnolia leaf (Magnolia grandiflora) to different levels of relative water content 18

33 Figure 2.2: Example of laboratory-measured reflectance of single Magnolia leaf to different levels of relative water content within the spectral range 0.4µm to 2.6µm (Jensen 2007) Figure 2.2 shows an obvious overall increase in reflectance in the spectral range 0.4µm to 2.6µm with lower levels of relative water content. It showed different patterns of responses, for example in the visible spectral bands of blue (i.e., µm) and green ( µm) showed insignificant changes due to water content variations; however, the red spectral band ( µm) was more sensitive to water stress (Jensen 2007). The NIR reflectance (i.e., μm) generally showed low variations with changes of water content levels as this portion of the spectrum was mainly sensitive to leaf internal structure changes. Only when water stress reached sufficient levels that severely cause leaf dehydration, NIR reflectance would show an obvious increase (Jensen 2007). Furthermore, other factors might affect the NIR reflectance of vegetation such as leaf area index (LAI), plant types and density (Ceccato et al. 2001). Therefore, it might be concluded that the NIR reflectance was indirectly affected by water stress. The SWIR spectral bands (i.e., μm) 19

34 showed strong relationship with leaf water variation and it was described as the most sensitive band to water variation (Hunt and Rock 1989, Ceccato et al. 2001, Ghulam et al. 2008) Spectral reflectance of soils to variations in water content In regard the soil spectral characteristics, a common and dominant feature is that soils turn to be darker when they wetted due to the multiple interactions between light and soil which probably increase light absorption and, thus decrease the total light reflected. In Figure 2.3 it is observed that spectral curves increased along the entire spectral range 0.4µm to 2.6µm (Whiting et al. 2004, Fabre et al. 2015). More specifically, in Figure 2.3, the shape of the spectral curve has small increasing change in the visible spectrum (i.e., µm) indicating that the darkening effect appears to be largely uncorrelated of water absorption effect (Bedidi et al. 1992). In the NIR and SWIR spectrums, reflectance showed more percentage increase comparing to the visible spectral spectrum which indicated that soil moisture spectral sensitivity magnified with longer wavelength. For instance, there are significant changes in the reflectance that are directly related to changes in water content. In Figure 2.3, there are three obvious water absorption features centered near 1.4 µm, 1.9 µm, and 2.2 µm corresponded to the presence of water. Although some other soil properties such as texture, mineral composition, and organic matter, these properties would be considered unchanged over time for a specific location, thus soil water content would be the primary factor affecting soil spectral response (Liu et al. 2002). 20

35 Figure 2.3: Example of laboratory-measured spectral reflectance of soil with different levels of soil moisture content (SMC) (Fabre et al. 2015) Remote sensing methods for agricultural drought monitoring Generally, remote sensing methods were developed on the basis of exploiting one or more of the following criteria: 1. vegetation greenness-related variables [e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), modified perpendicular drought index (MPDI), vegetation condition index (VCI), etc.] derived as a function of visible and near infrared (NIR) spectral bands; and widely used in monitoring drought conditions over various ecosystems across the world (e.g., Tucker and Choudhury 1987, Kogan 2002, Samanta and Ganguly 2011, Shahabfar et al. 2012). In these variables the blue and NIR spectral bands were used as the less sensitive band to the variations in water content while red spectral band was used as the more sensitive band to water content vitiation. The application of these methods in agricultural drought 21

36 monitoring was made under the assumption that water stress is the only factor controlling the plants growing process. Though vegetation greenness related variables were widely used in monitoring agricultural drought, they showed an apparent time lag in responding to water stress (Gao 1996; Jackson et al. 2004; Chakraborty and Sehgal 2010); in addition, they were unable to accurately reflect the actual water condition in vegetation as different factors might affect the vegetation status, such as phenology, insects, vegetation type, temperature, and chlorophyll (Anjum et al. 2011). Table 2.2 shows description of the most commonly used vegetation greenness related variables in agricultural drought monitoring. Table 2.2: Description of the most commonly used vegetation greenness related variables in agricultural drought monitoring. Index Expression* Reference Vegetation Condition Index (VCI) NDVIi - NDVImin VCI = NDVImax - NDVImin Kogan 2002 Modified Perpendicular Drought Index (MPDI) MPDI = 1 1- fv (PDI- fv * PDIv) Ghulam et al Normalized Difference Vegetation Index (NDVI) Enhanced Vegetation Index (EVI) NDVI = ρ NIR - ρ R ρ NIR + ρ R ρ EVI= 2.5 x NIR - ρ R ρ NIR +6x ρ R +7.5x ρ B +1 Tucker and Choudhury 1987 Huete et al *ρ is the surface reflectance value of blue (B), red (R), near infrared (NIR), bands; fv is the vegetation fraction. 22

37 2. meteorological-related variables calculated using thermal spectral band-derived land surface temperature (LST) and used in assessing vegetation water stress and drought monitoring. These variables worked as an indicator for assessing vegetation water stress, evapotranspiration, and soil moisture through employing surface temperature measurements such as, apparent thermal inertia and single land surface temperature (LST)-based methods (Tang and Li 2014) such as: temperature condition index (TCI; Kogan 2002) and normalized difference temperature index (NDTI; McVicar and Jupp 1998). The application of thermal infrared measurements from space was based on the existed relationship between surface temperature and the soil and vegetation water state. In general, the assessment of agricultural drought using remote sensing-based meteorological variables was affected by the specific sensor configurations of thermal spectral bands, LST derivation algorithm, meteorological conditions, and heterogeneity of landscape (Moran 2004, Rahimzadeh-Bajgiran et al. 2012). Table 2.3 shows description of most commonly used remote sensing based meteorological related variables in agricultural drought. Table 2.3: Most commonly used remote sensing based meteorological related variables in agricultural drought monitoring Index Expression * Reference Temperature Condition Index (TCI) Normalized Difference Temperature Index (NDTI) TCI= LST max- LST LST max- LST min NDTI = T -LST T -T0 Kogan, 1995 McVicar and Jupp, 1998 * LST max and LST min are the maximum and minimum LST from all images in the dataset respectively; T and T0 are the modeled surface temperature if there is an infinite or zero surface resistance, respectively. 23

38 3. vegetation wetness-related variables [e.g., moisture stress index (MSI), normalized difference water index (NDWI), shortwave infrared water stress index (SIWSI), normalized multiband drought index (NMDI), etc.] derived from NIR and SWIR spectral bands; and used to comprehend drought conditions through the vegetation moist status (Hunt and Rock 1989, Gao et al. 1996, Fensholt and Sandholt 2003, Wang and Qu 2007). In these variables the NIR spectral band served as a moisture reference band and the SWIR spectral band was used as the moisture measuring band (Zhang et al. 2013). The use of vegetation wetness indices in agricultural drought analysis was found to be more advanced and direct vegetation moisture indicator over the other vegetation greenness variables. However, they were affected by land cover types, vegetation intensity, and land surface heterogeneity which showed considerable uncertainties in the output results (Gao 1996; Ghulam et al. 2008). Table 2.5 shows description of the most commonly used vegetation wetness related variables in agricultural drought monitoring. 24

39 Table 2.5: Description of the most commonly used vegetation wetness related variables in agricultural drought monitoring. Index Expression* Reference Normalized Difference Infrared Index (NDII) NDII = ρ NIR - ρ SWIR2 ρ NIR + ρ SWIR2 Hardisky et al Moisture Stress Index (MSI) Normalized Difference Water Index (NDWI1) MSI = ρ SWIR2 ρ NIR Hunt and Rock, 1989 NDWI = ρ NIR - ρ SWIR1 ρ NIR + ρ SWIR1 Gao et al Simple Ratio Water Index (SRWI) SRWI = ρ NIR ρ SWIR1 Zarco-Tejada et al Land Surface Water Index (LSWI) LSWI = ρ NIR - ρ SWIR2 ρ NIR + ρ SWIR2 Xiao et al Shortwave Infrared Water Stress Index (SIWSI) Normalized Multiband Drought Index (NMDI) SIWSI = ρ SWIR1,2 - ρ NIR ρ SWIR1,2 + ρ NIR Fensholt and Sandholt, 2003 NMDI = ρ NIR (ρ - ρ SWIR1 SWIR3 ) Wang and Qu 2007 ρ NIR + (ρ SWIR1 + ρ SWIR3 ) *ρ is the surface reflectance value of near infrared (NIR), and shortwave infrared (SWIR1, SWIR2, and SWIR3 centred at ~1.24, ~1.64, and ~2.14 µm) bands. 4. surface wetness conditions: since vegetation indices (VIs) and LST have different capabilities in monitoring and detecting agricultural drought, researchers have worked on combining these two variables in one drought indices assuming that this combination may provide better characterization of drought conditions. This combination was derived in two ways. The first one is based on exploiting inherent relationship between surface temperature and vegetation greenness; and some examples include temperature-vegetation dryness index (TVDI), 25

40 vegetation temperature condition index (VTCI), evaporative stress index (ESI), temperaturevegetation index (TVX), etc. (Lambin and Ehrlich 1996, Sandholt et al. 2002, Sun et al. 2012, Anderson et al. 2011). This relationship usually tested using two dimensional scatter plots which typically generated either triangular or trapezoidal shapes (Hassan et al. 2007) (see Figure 2.4). These shapes emerged due to the negative relationship between the two variables. For instance, when vegetation greenness values increase along the x-axis, the LST values decrease along the y axis due to the cooling effects of evapotranspiration indicating none water stress condition, and vice versa (Karnieli et al. 2010). Figure 2.4: (a) triangular and (b) trapezoidal forms based on a relationship between LST and vegetation greenness variables (Li et al with modifications). Referring to Figures 2.4a and 2.4b, the theoretical dry edge (i.e., water stress condition) is represented by a line connecting the no evaporation and the no transpiration points. While, the theoretical wet edge (i.e., well-watered condition) is represented by a horizontal line connecting the maximum evaporation and the maximum transpiration points. In Figure 2.4, variations along the LST axis reflects the effects of water content and topography across bare soil areas, while variations along VIs axis reflects the effects water content and vegetation cover density across the 26

41 vegetative area. The remaining area within the triangular or trapezoidal shapes represents pixels with varying vegetation cover between the bare soil and dense vegetation. The triangular and trapezoidal shapes were driven by many factors including, (i) evaporation from soil and the vegetation (Smith and Choudhury 1991); (ii) vegetation fractional cover, surface moisture status and local climate (Nemani et al. 1993); (iii) the number of pixels in the scene and the spatial resolution (Carlson et al. 1995); (iv) incident radiation variations (André et al. 1986), and (v) other specific study area characteristics (e.g., soil type, topography, land cover, spatial heterogeneity, and latitude) (Lambin and Ehrlich 1995). In the literature, numbers of different methodologies have been developed to estimate water status from satellite-derived LST-VIs scatterplots. They can be grouped into five classes such as, (i) surface temperature and simple vegetation index; (ii) surface temperature and albedo; (iii) surface-air temperature difference and vegetation index; and (iv) day-night surface temperature difference and vegetation index (Petropoulos et al. 2009). The second one is based on integrating visible, NIR, and shortwave infrared (SWIR) spectral bands using different mathematical operations (addition, subtraction, multiplication, and division); and some example include visible and shortwave infrared drought index (VSDI), four bands drought index (FBDI), moisture adjusted vegetation index (MAVI), etc. (Zhang et al. 2013, Zhu et al. 2014, Ranja et al. 2015). In this approach, the visible and NIR spectral bands were used as reference bands and the SWIR spectral bands were used as the measuring bands. Table 2.4 shows description of the most commonly used surface wetness condition related variables in agricultural drought monitoring. 27

42 Table 2.4: Description of the most commonly used surface wetness condition related variables in agricultural drought monitoring Type Index Expression * Reference Scatter plot approach (Triangle) Scatter plot approach (Trapezoid) Integrated visible, NIR, SWIR Vegetation Temperature VTCI = Condition Index (VTCI) Temperature-Vegetation TVDI = Dryness Index (TVDI) Water Deficit Index WDI = (WDI) Temperature-Vegetation TVWI = Wetness Index (TVWI) Ts NDVImax -Ts NDVI i Ts NDVImax - Ts NDVImin Ts - Ts min Ts max - Ts min (Ts - Ta) - ( LST - Ta)min (Ts - Ta)max - (Ts - Ta)min θ dry - θ s θ dry - θ wet Visible And Shortwave VSDI = 1 - [(ρ SWIR2 - ρ B ) + (ρ R - ρ B )] Drought Index (VSDI) Four bands drought ρ FBDI= SWIR1 - ρ SWIR2 index (FBDI) (ρ NIR - ρ Green )/(ρ NIR + ρ G ) Moisture adjusted MAVI = (ρ NIR - ρ R ) /(ρ NIR + ρ R + ρ SWIR1 ) vegetation index (MAVI) Wang et al Sandholt et al Moran et al Hassan et al Zhang et al Ranja et al Zhu et al *Tmax is the maximum surface temperature at the dry edge; Tmin is the minimum surface temperature at the wet edge; Ts NDVImax and Ts NDVImin are the maximum and minimum land surface temperatures of pixels which have same NDVI value in a study area, respectively, Ts NDVI i is the land surface temperature of one pixel whose NDVI value is NDVIi; Ta is the air temperature; өdry is the dry edge; өwet the wet edge;өs is the surface potential temperature. ρ is the surface reflectance value of blue (B), green (G), red (R), near infrared (NIR), and shortwave infrared (SWIR1, SWIR2, and SWIR3 centred at ~1.24, ~1.64, and ~2.14µm) bands 28

43 Due to the several problematic issues in case of implementing a single one of the above-mentioned criteria in agricultural drought monitoring, some possible solutions might include performing land cover classification and assigning a suitable index for each class (Wang et al. 2010) or applying different drought indices at different plant growing stages. However, such solutions might add additional uncertainty and complexity to the final results of agricultural drought monitoring. Therefore, a combination of variables from different approaches might provide more comprehensive assessment of drought conditions (Gu et al. 2008; Sun, et.al. 2012). For example, (i) Kogan (2002) combined VCI (i.e., vegetation greenness) and TCI (i.e., meteorological variable) to calculate vegetation health index (VHI); and (ii) Jang et al. (2006) and Gu et al. (2008) calculated normalized moisture index (NMI) and normalized difference drought index (NDDI) respectively, as a function of combining NDWI (i.e., vegetation wetness) and NDVI (i.e., vegetation greenness). It would be worthwhile to mention that the combination approach was applied in other water stress related applications like forecasting forest fire danger conditions (Akther and Hassan 2011b, Chowdhury and Hassan 2013, 2015). In these cases, they integrated: (i) LST, NMDI, and temperature-vegetation wetness index (TVWI) (Akther and Hassan 2011b); (ii) LST, NMDI, and NDVI (Chowdhury and Hassan 2013); and (ii) LST, NMDI, NDVI, and perceptible water (PW) (Chowdhury and Hassan 2015). However, in all of the previous studies, the relationships among the input variables were neglected despite the possibilities of having high correlations among input variables. In fact, such correlations might potentially generate uncertainties in model outputs (Jensen, 2007). 29

44 Advantages of remote sensing in agricultural drought monitoring 1. Spatial continuous measurements across large geographic areas. The continuous spatial coverage of remote sensing data is very important in locations where climatologic and other ground stations are sparse or not existent, in which; geo-statistical interpolation for these ground based data may cause uncertainty in mapping and monitoring agricultural drought. 2. Frequent revisit time for image acquisition. Some remote sensing systems acquire images every 1-2 days (i.e. GOES, AVHRR, and MODIS) or every ~2 weeks (i.e. Landsat). This characteristic is very important in monitoring drought conditions especially during short term drought events, such these happen during crops growing season. 3. Historical records. Some current operating satellites provide 30+ years of information (e.g., AVHRR and Landsat series), with some newer sensors (e.g., MODIS) provide daily images since These historical records are very important in understanding the previous drought events and their frequencies, severity and durations. It can be also used to quantify the socio-economic impacts of drought events and provide comparisons between the current drought conditions and the historical conditions for a specific location and time during the growing season. 30

45 2.2.3 Synergic remote sensing/in-situ based methods In most of the instances, the majority of drought studies concentrated on assessing drought using an individual drought index (Tsakiris and Vangelis, 2004; Cancelliere et al. 2006; Mavromatis, 2007) or a group of them as a comparative study (Quiring and Papakyriakou, 2003; Morid et al. 2006; Bayarjargal, et.al. 2006; Quiring, 2009). As each index has its own data type, complexity, strengthens, and weakness; they often provide different results for the same event of interest (Heim, 2002; Quiring, 2009). The derivation of drought indices from different type of observations (that include ground-based hydro-climatic data and remote sensing data) can potentially overcome the deficiencies of a single data source. However, this has been challenging due to the lack of systematic methods for the combining, implementing, and also evaluating of this phenomenon (Steinemann and Cavalcanti, 2006). Such indices include: US drought monitor (USDM; Svoboda et al. 2002), vegetation drought response index (VegDRI; Brown et al. 2008), hybrid drought index (HDI; Karamouz et al. 2009), vegetation outlook ( VegOut; Tadesse et al. 2010), integrated surface drought index (ISDI; Wu et al.2013b) and multi-index drought (MID; Sun et al. 2012). Table 2.6 shows description of the most commonly used synergic remote sensing/in-situ based agricultural drought monitoring indices. 31

46 Table 2.6: Description of the most commonly used synergic remote sensing/in-situ based agricultural drought monitoring indices Index Description * Reference US Drought Monitor (USDM) Vegetation Drought Response Index (VegDRI) Vegetation Outlook (VegOut) Integrates VHI with other drought indices such as, PDSI, SPI, PNP, and soil moisture model percentiles, daily stream flow percentiles, and many other supplementary indicators. VegDRI is a hybrid drought index that integrates satellitebased observations of vegetation conditions with climatebased drought index data and biophysical characteristics of the environment to produce 1-km spatial resolution maps that depict drought-related vegetation stress. An experimental tool that provides a series of maps depicting future outlooks of general vegetation seasonal greenness conditions based on the analysis of: climate-based drought indices (i.e., PDSI and SPI); satellite-based observations of vegetation (i.e., SSG and SOSA);biophysical characteristics of the environment (i.e., eco-region, elevation, irrigated lands, and land use/cove type); and oceanic indicators (i.e., MEI, SOI, PDO, NAO, PNA, MJO, and AMO) Svoboda et al Brown et al Tadesse et al

47 Integrated Surface Drought Index (ISDI) Integrates PDSI and the traditional climate-based drought indicators, satellite-derived vegetation indices, and other biophysical variables. ISDI can be used not only for monitoring the main drought features such as precipitation anomalies and vegetation growth conditions but also it indicates the earth surface thermal and water content properties by incorporating temperature information. Wu et al.2013b * PNP is the percent of normal precipitation; SSG is the standardized seasonal greenness; SOSA is the Start of season anomaly; MEI is the multivariate ENSO index; SOI is the southern oscillation index; PDO Pacific decadal oscillation index; NAO is the north Atlantic oscillation index; PNA Pasific north American index; MJO Madden-Julian oscillation index; and AMOis the Atlantic multi-decadal oscillation index; Issues in the methods of agricultural drought monitoring Agricultural drought requires high proficiency methods for accurate drought monitoring in terms of the spatial distribution and temporal frequency. The in-situ based monitoring methods provide high frequent data (i.e., daily measurements recorded at ground stations), however they are spatially restricted to the specific measuring locations. Currently, remote sensing satellites provide optical and thermal products in different spatial and temporal resolutions for agricultural drought monitoring. For example, some remote sensing satellites such as MODIS, AVHRR, NPP/VIIRS, and SPOT-VEG can provide high temporal/low spatial data (i.e., daily/ m). On the contrast, other satellites provide data at low temporal/high spatial resolutions such as Landsat, 33

48 ASTER, and SPOT5 (i.e., days/ m). Practically, agricultural drought monitoring requires both high spatial and high temporal data due to the small size of agricultural fields and the rapid changes in plants during its growing season (Becker-Reshef et al. 2010; Rocha et al. 2012; Atzberger 2013). For example, high spatial resolution data (i.e., 30 m) is necessary for studying agriculture at field scale (Roy et al. 2014), and high temporal resolution data (i.e., weekly) is required for monitoring rapid changes in reflected or emitted energy during plants growing season (Zhang et al. 2003, Kovalskyy et al. 2012). These changes, in some cases, may reflect specific agricultural problems such as drought (Wang and Qu 2009). However, due to technical and cost issues, none of the most currently used satellite systems in agricultural studies has the capability to provide such accompanied high resolution data (Zhang 2004; Al-Wassai and Kalyankar 2013). Therefore, it is necessary to develop multi sensor data fusion techniques that compensate these limitations and provide high quality data for such applications (Yang et al. 2010; Jiang et al. 2011, Khaleghi et al. 2013). Despite the uncertainties of the previously mentioned methods, all were found to be effective in monitoring vegetation and soil water content in a variety of studies. Actually, it is still difficult to point out the most globally reliable drought monitoring method, as the comparisons between them showed that neither one performed the best in all the cases. The selection of an appropriate drought method depends on many factors including data availability and quality, cost, temporal and spatial sensitivity, index performance in understanding drought events (impact on vegetation, agriculture, and water), the spatial scale (global, regional or national), geographical region, topography, and land use practices. 34

49 2.3 Satellite image fusion To date, different definitions of image fusion were found in the literature, one comprehensive definition in terms of the Earth observation was: the process of combining information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing (Goshtasby and Nikolov 2007). Traditionally satellite-based data fusion methods can be divided into different groups according to different criteria. For example, they can be classified according to the data source into multi-sensor or multi-source methods (Ehlers et al. 2010; Zhang 2010). Or, according to the processing level at which the fusion is taken place such as, pixel level, feature level, and decision level (Pohl and van Genderen 1998). Another important classification can be done according to the fusion methodology, such as colour related methods, statistical/numerical methods and combined methods. First group compensated the color composition of RGB, HIS or other color schemes with panchromatic band for performing data fusion (Ehlers et al. 2010). Second group performed image fusion by employing statistical or arithmetical operations such as correlation, addition, multiplication, or rationing (Roy et al. 2008). Third group integrated different fusion methods in hybrid techniques (Zhang 2008). Among the statistical/numerical methods, a new area of research has been emerged called spatiotemporal data fusion. Basically, this approach employs statistical or mathematical frameworks to produce high spatial and high temporal satellite images by integrating images from different satellite systems that have similar spectral and orbital characteristics, but with different spatial and temporal resolutions. In this section, our focus is to present a state of the art of recently developed 35

50 satellite based spatio-temporal data fusion methods in order to comprehend the current developments and its implications Satellite based spatio-temporal data fusion methods Spatio-temporal data fusion approach was introduced by Gao et al. (2006) in their effort to generate daily synthetic surface reflectance images at 30 m spatial resolution through the blending of the Landsat (having 30 m spatial resolution at 16 day intervals) and MODIS (having 500 m spatial resolution every day) surface reflectance images. Since then, several spatio-temporal data fusion methods have been developed (Roy et al. 2008; Zhu et al. 2010; Zhang et al. 2013b; Fu et al. 2013; Weng et al. 2014). These methods were evaluated by using a combination of different satellite images, such as: (i) Landsat-5/-7 and MODIS (Gao et al. 2006; Zhu et al. 2010; Fu et al. 2013) (ii) Landsat-5 and MERIS (Zurita-Milla et al. 2009); (iii) HJ-1 and MODIS (Meng et al. 2013); (iv) Landsat-5/-7 and GOES (Wu et al. 2013), and (v) ASTER and MODIS (Liu and Weng 2012). In general, the main idea of the spatiotemporal data fusion approach is to fuse low-temporal/high-spatial resolution data with hightemporal/low-spatial resolution data. This was applied to different environmental parameters and applications, such as: (i) surface reflectance (Gao et al. 2006; Zhang 2013), (ii) normalized difference vegetation index (NDVI) (Meng et al. 2013), (iii) evapotranspiration (Anderson et al. 2011), (iv) urban heat island (Huang et al. 2013), (v) public health (Liu and Weng 2012), and (vi) surface temperature (Weng et al. 2014). These spatio-temporal data fusion methods could be broadly classified into three groups, such as (i) spatio-temporal adaptive fusion methods, (ii) 36

51 unmixing-based spatio-temporal fusion methods, and (iii) sparse representation-based spatiotemporal fusion models; and briefly discussed in the following sub-sections. Figure 2.5 shows the conceptual schematic diagram for spatio-temporal fusion techniques. Figure 2.5: Conceptual schematic diagram of the spatio-temporal fusion techniques; (a) using two input coarse spatial resolution images and one fine spatial resolution image; (b) using three input coarse spatial resolution images and two fine spatial resolution images 37

52 Spatio-temporal adaptive fusion methods These methods utilized mathematical operations, such as summation, subtraction, multiplication, and rationing in order to perform the data fusion. Gao et al. (2006) introduced the spatial and temporal adaptive reflectance fusion model (STARFM) for blending surface reflectance data from MODIS and Landsat images. STARFM was an empirical fusion method that utilized the spectral and orbital similarities between Landsat and MODIS, and combined them to produce synthetic Landsat images at the temporal resolution of MODIS (i.e., daily or 8-day scale). It consisted of three main steps, such as: i. selecting spectrally similar pixels (i.e., having similar reflectance) within a moving window of interest using Landsat images; ii. iii. determining a weighting factor as a function of both Landsat and MODIS images; and generating synthetic Landsat images at time two synth-l(t2) by multiplying the weighting factor with the sum of difference between two MODIS images taken at two different times [M(t2) M(t1)] and Landsat image taken at time one [L(t1)]. Though the STARFM predicted reasonable synthetic Landsat images in comparison with reference Landsat images; there were three major limitations, such as: (i) the window size was variable depending on the study area, and needed to be adjusted at each run; (ii) the existence of spectrally similar pixels within the moving window might not be always found; and (iii) it was not applicable over heterogeneous landscapes. 38

53 In order to address the issue of heterogeneous land cover, Zhu et al. (2010) proposed the Enhanced STARFM (ESTARFM). In this case, they used two pairs of Landsat and MODIS images taken in two different dates such as (t1) and (t3) in order to predict synthetic Landsat image at an intermediate time (t2). The enhancement included the determination of a conversion coefficient for each spectrally similar pixel by performing linear regression analysis between Landsat and MODIS images [i.e., L(t1) with M(t1)] and [L(t3) with M(t3)] prior to generate the synthetic Landsat image. Although the method successfully predicted synthetic Landsat images, it was not only computationally expensive but also had the same problem of the moving window and the spectrally similar pixel issues like the original STARFM. In addressing the issue of selecting the spectrally similar pixels, Fu et al. (2013) modified ESTARFM (mestarfm). They applied two conditions to the candidate pixels, such as: (i) the candidate pixels should be less than or equal to the standard deviation threshold of the moving window, and (ii) had the same land cover type as the central pixel in the moving window. This proposed method improved the accuracy of the predicted image in comparison to that of ESTARFM-produced synthetic images. Despite, it still had other problems such as, long computation time and the inapplicability for near real time applications. In another study, Meng et al. (2013) introduced the spatial and temporal adaptive vegetation index fusion model (STAVFM); where they improved the weighting function of STARFM by defining a time window according to the temporal variation of crops (in the event the method applied over agriculture-dominant land cover types). Although the method improved the predicted normalized difference vegetation index (NDVI) images; however, failed to address other two major STARFM limitations. In other applications, researchers implemented STARFM in predicting synthetic surface temperature (Ts) (Liu and Weng 2012) and evapotranspiration (Anderson et al. 2011). However, the direct implementation of STARFM in these applications revealed severe errors due 39

54 to the STARFM limitations (i.e., heterogeneity, window size, and spectrally similar pixels). Thus, Weng et al. (2014) incorporated the annual temperature cycle and a linear spectral mixing analysis within the original STARFM model and proposed the spatio-temporal adaptive data fusion algorithm for temperature (SADFAT) mapping. Although the predicted LST images showed good agreements with actual/reference LST images, the method required adjusting the size of the moving window and the number of land cove types each time before the model implementation (Weng et al. 2014). Other researchers, such as Huang et al. (2013) and Wu et al. (2013) applied a bilateral-based filter and a variation-based filter respectively to calculate the predicted value of the LST within the moving window instead of using the original filter-based method of STARFM. However, in both of the studies, LST outliers within the moving window of interest affected the predicted images and produced uncertain synthetic LST images Unmixing-based spatio-temporal fusion methods The basis of unmixing-based fusion methods was the employment of a classification technique in generating synthetic image having both high spatial and temporal resolutions. For instance, Zurita- Milla et al. (2009) used a linear unmixing model to un-mix MERIS time-series images acquired at 300 m spatial resolution using a land use database with a 25 m spatial resolution. The approach was based on assigning the unmixed signals to the corresponding land-use class presented in the central pixel of kxk MERIS neighborhood. Though the results were promising in monitoring vegetation dynamics at Landsat-like spatial (i.e., ~25 m) and MERIS-like spectral and temporal (i.e., ~3 days) resolution; however, the accuracy of this method was highly dependent on the quality and the availability of the land use database. In another study, Zhang et al. (2013b) further 40

55 intensified the ESTARFM proposed by Zhu et al. (2010) by incorporating ISODATA classification techniques. This particular approach had four main steps, such as: i. classifying Landsat images using a patch based ISODATA classification technique and calculating the abundance of end members within a moving window; ii. iii. unmixing of the three MODIS images using those end members; predicting two synthetic images by calculating the sum of L(t1) and L(t3) with the corresponding difference images of unmixed MODIS images; and iv. generating the final synthetic image by weighting the two predicted images produced in the previous step. This method eliminated the requirement of a high spatial resolution land use map as the case in Zurita-Milla et al. (2009). However, it would have the same problematic issues of ESTARFM proposed by Zhu et al. (2010) Sparse representation-based spatio-temporal fusion methods Huang and Song (2012) introduced the concept of the sparse representation-based spatio-temporal reflectance fusion model (SPSTFM). They used MODIS images taken at 3 times [M(t1), M(t2), and M(t3)] and two Landsat images taken at 2 times [L(t1), and L(t3)] to generate synth-l(t2). The method consisted of four steps, such as: i. enhancing the three MODIS images to the equivalent spatial resolution of Landsat image (i.e., 30 m) through a sparse representation technique; 41

56 ii. building a dictionary pair using the two counterpart MODIS and Landsat images [M(t1) and L(t1); M(t3) and L(t3)] and the other MODIS image [M(t2)]; iii. iv. predicting the difference image of Landsat data using the learned dictionary pair; and reconstructing the predicted Landsat image using different weighting parameters. This method provided better results when comparing with the STARFM-based fusion methods; however, it was computationally expensive and impractical for near real time applications. Song and Huang (2013) improved the SPSTFM method by employing a sparse representation procedure that include dictionary training and spars coding, in order to enhance the spatial resolution of MODIS images (known as transition images) to the spatial resolution of Landsat image. Then two transition images taken at t1 and t2 and one Landsat image [L(t1)] were used to predict the synthetic Landsat image at time 2 (t2) by using a high pass modulation technique. This method used only three input images to predict the synthetic image. Thus, it eliminated one of the SPSTFM problems, however it required relatively longer processing time due to the dictionary training Issues in the satellite based spatio-temporal data fusion methods The review of the three different groups of spatio-temporal image fusion methods (i.e., spatiotemporal adaptive fusion methods, unmixing-based fusion methods, and sparse representationbased fusion methods) that have been used for enhancing the spatio-temporal resolutions of satellite images were having different critical technical and operational issues. Actually, it was difficult to point out the most reliable method, as the comparisons among them showed that neither one performed the best in all the cases. It was found that the appropriate method would depend on 42

57 many factors including data availability and their quality, type of application of interest, and landscape properties. In summary, it could be stated that satellite image fusion methods started to move from generic to advanced approaches considering sensor, context, application and other parameters in the process. Discussion from this section would also open up windows to conduct more studies with different methods to delineate suitability depending on their area of studies. 2.4 Review of the most commonly used remote sensing platforms in agricultural drought monitoring Among the major operational remote sensing satellite systems mentioned in Table 1.1 in section 1.2, Landsat, MODIS and Advanced Very High Resolution Radiometer (AVHRR) are the most commonly satellites used in agricultural drought monitoring. AVHRR requires difficult preprocessing procedures, therefore, we selected Landsat-8 and MODIS for this research. MODIS and Landsat satellite systems are sharing different characteristics such as: (i) data history and continuity (i.e., MODIS has provided data since 1999 until now; and Landsat missions have provided data since 1972 until now); (ii) both of their data are freely available to everyone; (iii) they are easy to download and analyse; (iv) their data are compatible with most of remote sensing image processing software; and (v) they have different pre-processed products for the direct use by end users; (iv) their configurational, orbital and spectral characteristics are similar. In this research, we adopted MODIS Terra and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) derived products (i.e., surface reflectance and surface temperature) in the development of new spatio-temporal data fusion techniques and implemented it in the development of an operational agricultural drought monitoring method. The following is a detailed description of these two satellite systems. 43

58 2.4.1 Landsat characteristics The United States Department of the Interior, NASA, and the Department of Agriculture developed and launched the first Landsat mission (i.e., Landsat-1) in July 23, The launches of Landsat-2, Landsat-3, and Landsat-4 followed in 1975, 1978, and 1982, respectively. The fifth mission was launched in 1984 and continued to deliver high quality data of the Earth surfaces until Landsat-6 failed to achieve orbit in In 1999, Landsat-7 was launched and it still provides global coverage along with Landsat-8 which was launched in Landsat-9 is planned to be launched in Figure 2.6 shows the historical/future time line of Landsat missions. Figure 2.6: The historical/future time line of Landsat missions. 44

59 The following is a detailed description of Lansat-8 characteristics (NASA 2013): Participants NASA Department of the Interior (DOI) U.S. Geological Survey (USGS) Spacecraft bus: Orbital Science Corp. Operational Land Imager Sensor: Ball Aerospace & Technologies Corp. Thermal Infrared Sensors: NASA Goddard Space Flight Center Launch Date: February 11, 2013 Vehicle: Atlas-V rocket Site: Vandenberg Air Force Base, California Spacecraft 3.14 terabit solid-state data recorder Weight: 2,071 kg (4,566 lbs) fully loaded with fuel (without instruments) Length: 3 m (9.8 ft) Diameter: 2.4 m (7.9 ft) Communications Direct Downlink with Solid State Recorders (SSR) Data rate: 384 Mbps on X-band frequency; Mbps on S-band frequency 45

60 Orbit Worldwide Reference System-2 (WRS-2) path/row system Sun-synchronous orbit at an altitude of 705 km (438 mi) 233 orbit cycle; covers the entire globe every 16 days (except for the highest polar latitudes) Inclined 98.2 Circles the Earth every 98.9 minutes Equatorial crossing time: 10:00 a.m. +/- 15 minutes Sensors - Operational Land Imager (OLI) Nine spectral bands, including a panchromatic band: o o o o o o o o o Band 1 Visible ( µm) 30 m Band 2 Visible ( µm) 30 m Band 3 Visible ( µm) 30 m Band 4 Red ( µm) 30 m Band 5 Near-Infrared ( µm) 30 m Band 6 SWIR 1( µm) 30 m Band 7 SWIR 2 ( µm) 30 m Band 8 Panchromatic (PAN) ( µm) 15 m Band 9 Cirrus ( µm) 30 m 46

61 - Thermal Infrared Sensor (TIRS) Two spectral bands: o o Band 10 TIRS 1 ( µm) 100 m Band 11 TIRS 2 ( µm) 100 m Other Characteristics Scene size: 170 km x 185 km (106 mi x 115 mi) Design Life: Minimum of 5 years Projection: Universal Transverse Mercator (UTM) MODIS characteristics The MODIS (or Moderate Resolution Imaging Spectroradiometer) instrument is aboard with Terra and Aqua satellites. They provided daily data of the Earth surface in 36 spectral bands, which was found to be helpful in understanding the global (i.e., land and ocean) dynamics, and lower atmosphere. The following is a detailed description of MODIS Terra characteristics (NASA 2011): Participants - MODIS consisted of over 70 international team of scientists divided into four discipline groups: atmosphere, calibration, land, and ocean. - NASA 47

62 - Santa Barbara Remote Sensing Launch - Terra satellite was launched on December 18, Aqua was launched on May 4, Spacecraft - Telescope: cm diam. off-axis, a focal (collimated), with intermediate field stop - Size: 1.0 x 1.6 x 1.0 m - Weight: kg - Power: W (single orbit average) Communications - Scan Rate: 20.3 rpm, cross track - Data Rate: 10.6 Mbps (peak daytime); 6.1 Mbps (orbital average) - Quantization: 12 bits Orbit Sun-synchronous orbit at an altitude of 705 km (438 mi) 10:30 a.m. descending node (Terra) 1:30 p.m. ascending node (Aqua), Near-polar, circular 48

63 Other Characteristics - Scene size: 2330 km (cross track) by 10 km (along track at nadir) - Spatial Resolution: 250 m (bands 1-2), 500 m (bands 3-7), 1000 m (bands 8-36) - Design Life: 6 years Spectral bands 36 spectral band (reflected bands: 1 19 and 26; emitted bands: 20 25, 27 36) Resolution and major use: see Table 2.7 Projection: Sinusoidal 49

64 Table 2.7: MODIS spectral bands and its major use Band No. Spectral resolution (μm) Major use Absolute Land Cover Transformation, Vegetation Chlorophyll Cloud Amount, Vegetation Land Cover Transformation Soil/Vegetation Differences Green Vegetation Leaf/Canopy Differences Snow/Cloud Differences Cloud Properties, Land Properties Chlorophyll Chlorophyll Chlorophyll Chlorophyll Sediments Atmosphere, Sediments Atmosphere, Sediments Chlorophyll Fluorescence Chlorophyll Fluorescence Aerosol Properties Aerosol Properties, Atmospheric Properties Atmospheric Properties, Cloud Properties Atmospheric Properties, Cloud Properties Atmospheric Properties, Cloud Properties Sea Surface Temperature Forest Fires & Volcanoes Cloud Temperature, Surface Temperature Cloud Temperature, Surface Temperature Cloud Fraction, Troposphere Temperature Cloud Fraction, Troposphere Temperature Cloud Fraction (Thin Cirrus), Troposphere Temperature Mid Troposphere Humidity Upper Troposphere Humidity Surface Temperature Total Ozone Forest Fires & Volcanoes, Surface Temp Forest Fires & Volcanoes, Surface Temperature Cloud Fraction, Cloud Height Cloud Fraction, Cloud Height 50

65 Chapter Three: Study Area and Data Pre-processing 3.1 General description of the study area In this study, we selected the rainfed agricultural land in the Northwestern part of Jordan - located in the Middle East - as our study area (see Figure 3.1). The area is about 3522 km 2 covering approximately 4% of the country and located between 31 51' to 33 22' North and 34 19' to 36 18' East. It includes the main populated area in Jordan and consists of six governorates. Topographically, the study area has a complex terrain with elevations vary from 600 m to 1100 m above mean sea level. Agriculturally, crop production is very climate sensitive, which is reflected in a high variability in crop production. The growing season starts in November and ends in June. Field crops (e.g., wheat, barley, and lentil), grasslands and rangeland, and orchards (i.e., olives and fruits) and evergreen forests are the dominant vegetation cover/use types, and are accounting for approximately 65.5%, 25.5%, and 9.0% of the study area, respectively (FAO 2006). Climatologically, the area is located in the semi-arid environment and it experiences a Mediterranean climate conditions, with a long dry hot summer, (i.e., average temperature ~25 o C with no precipitation during May to August); and cool and wet winter (i.e., average temperature ~ 5-7 o C); and two transition seasons (i.e., spring during March and April; and fall during September and October) (HKJ, 2013). Precipitation ranging from 250 mm to 600 mm and occurs mainly in the winter season (November to March). In the study area, the average annual long-term precipitation is about mm; and the mean annual minimum and maximum temperature is around 11 o C and 23 o C, respectively. 51

66 Figure 3.1: (a) Topographic map of Jordan; (b) map of Jordan illustrating long-term average annual precipitation isohyets distribution; (c) Major land use/cover map in the study area and locations of the agro-climate stations used in this study. 52

67 In general, Jordan is considered as one of the most drought vulnerable countries in the Middle East (Erian et al. 2013), and water shortage is the main obstacle to the development of the agricultural sector. According to the World Bank, Jordan is the third most water scarce country in the world (JME, 2006). The analysis of the long-term precipitation data showed that the temporal distribution of precipitation was highly erratic throughout the growing seasons with more concentration during January and February (see Figure 3.2 and Figure 3.3 for illustration). In the study area, several drought events have been occurred as a result of variations in precipitation distribution and density which affected agricultural and other economic sectors (Al-Salihi 2003, and, Al-Qinna et al. 2011). For example, the drought season caused an estimation of only 1% of cereals harvested in Jordan and 180,000 farmers and herders were affected which caused food insecurity for 4.75 million people (FAO/WFP, 1999). The average ratio of harvested to cultivated areas in the period of was only 68% for wheat and 44% for barley, implying significant agricultural drought risk facing farmers (Souab, 2010).The five years of drought from caused severe water shortage and severely affected agriculture production (Milton-Edwards and Hinchcliffe, 2009). Precipitation season of 2014/2015 was only 52% of the long-term average annual precipitation and was described as the worst season in the last decade, leaving dams only 43% full, and farmers in the northwest and central regions of the country struggling to grow their crops such as wheat and barley (Jordanian Ministry of Water and Irrigation, 2015). 53

68 Figure 3.2: Long-term average monthly rainfall/precipitation distribution in millimeters in the study area from 1984 to Figure 3.3: Comparison between the average annual rainfall in millimeters in the study area from 1984 to 2015, and the long-term average rainfall represented by the dashed line at approximately mm. 54

69 3.2 Data used Remote Sensing Data In this research, we acquired data from two satellite systems: (i) Landsat-8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) freely available from U.S. Geological Survey (USGS); and (ii) MODIS freely available from National Aeronautics and Space Administration (NASA) during the growing seasons as described in Table 3.1. All dataset are described in detail in the later sections Ground-based precipitation data I obtained historical daily precipitation records for thirty years during the period available from the Jordanian Ministry of Water and Irrigation. These data records were obtained in the form of Microsoft Excel files which were then carefully processed for further analysis. Each file was consisted of several attributes including name of agro-climate station, station code, geographic coordinates for their locations, precipitation amount in millimeters, and date of recording. 55

70 Table 3.1: Description of Landsat-8 and MODIS data used in this research Dataset acquisition dates + Data types 02 Jun. 2013, 18 Jun. 2013, 04 Jul. 2013, 20 Jul. 2013, 05 Aug Landsat-8 derived surface : 24 Oct. 2013, 09 Nov. 2013, 13 Feb. 2014, 01 Mar. 2014, 17 Mar. 2014, 02 Apr. 2014, 18 Apr. 2014, 04 May 2014, 20 May 2014, reflectance * and LST ** data at 30 m spatial resolution with 16- day intervals : 27 Oct. 2014, 30 Dec. 2014, 31 Jan. 2015, 05 Apr. 2015, 21 Apr. 2015, 07 May 2015, 08 Jun Jun. 2013, Jun. 2013, Jun. 2013, 26 Jun. - 3 Jul. 2013, Jul. 2013, Jul. 2013, Jul. 2013, 28 Jul. 4 Aug. 2013, 5-12 Aug (twenty six) 8-day composites spanning between October to June per growing seasons of and day composite of MODISderived: (i) surface reflectance * at 500 m spatial resolution, and (ii) LST at 1000 m spatial resolution *** + Dates in italic were used to develop and evaluate the spatio-temporal image fusion model (STI- FM). Dates in none italic were used to develop and evaluate agricultural drought index (ADI). * Surface reflectance-values were considered for the blue (i.e., centered at ~0.475 μm), red (~0.670 μm), NIR (~0.860 μm), SWIR1 (~1.625 μm), and SWIR2 (~2.165 μm) spectral bands. ** Landsat-8 based LST-values were derived from the spectral radiance-values acquired by the thermal infrared band (i.e., centered at ~10.9 μm) using the method described in section *** 8-day composite surface reflectance and surface temperature MODIS product names are MOD09A1 and MOD11A2, respectively. 56

71 3.3 Data pre-processing The pre-processing of all the input data which mainly consisted of 8-day composite MODIS-based surface reflectance and surface temperature data products; and Landsat-8 spectral and thermal bands; precipitation data records, and other relevant spatial data layers were pre-processed as described in the following sections MODIS data pre-processing The acquired 8-day composite MODIS surface reflectance and surface temperature products were originally provided in Sinusoidal projection. we used MODIS re-projection tool (MRT 4.1; Dwyer and Schmidt, 2006) to subset the images into the spatial extent of the study area, and reproject them into the coordinate system of Landsat-8 images [i.e., Universal Transverse Mercator (UTM Zone 36N -WGS84)]. Then we co-registered these images using Landsat-8 images to allow for accurate geographic comparisons and to reduce the potential geometric errors (e.g., position and orientation) as effects of spatial miss-registration can influence the derived information (Feng et al. 2012). Finally, we checked the quality of the images and excluded the cloud contaminated pixels by using the quality control bands available with each dataset, (i.e., 500 m flag; another layer available in the MOD09A1 dataset; and 1000 m QC_Day flags; another layer available in the MOD11A2 dataset). 57

72 Constraints in relation to the 8-day composite MODIS data In general, the 8-day composite MODIS data were generated from compositing atmospherically corrected daily MODIS images from NASA, this procedure would lessen the cloud-contamination which would block some valuable information about land surface. However, the use of this data had two issues such as: The 8-day MODIS composite surface reflectance data was generated based on minimumblue criterion; which coincided with the best clear-sky condition day during the composite period of interest (Vermote et al. 2011). As such, two consecutive 8-day composite images might be potentially apart in the range of 2 to 16 days. The 8-day LST composite images were generated by averaging the LST images acquired under clear-sky conditions at approximately 10:30 am local time (Wan, 2006). Thus, they might not reflect the daily variations and/or maximum temperature Landsat-8 data pre-processing Landsat-8 spectral and thermal images were available in the form of digital numbers (DN). These DN-values were converted into surface reflectance and surface temperature images as described in the following sections: 58

73 Converting DN-values into surface reflectance In converting DN-values into surface reflectance, we followed several steps. Firstly, we converted DN-values into top of atmosphere (TOA) reflectance using the following expression illustrated in USGS (2013): ρ TOA = M*DN+A Sin (θ SE ) (3.1) where, TOA is the band-specific Landsat-8 TOA reflectance; M is band-specific multiplicative rescaling factor; A is band-specific additive rescaling factor; and SE is the local sun elevation angle at the scene center. The values of A, M, and SE were available in the metadata file of each image. Secondly, we transformed TOA reflectance into surface reflectance by employing a simple but effective atmospheric correction method that would not require any information about the atmosphere conditions during the image acquisition period. This was done using MODIS surface reflectance images based on the fact that MODIS and Landsat are having consistent surface reflectance values (Masek et al. 2006, Gao et al. 2006; Feng et al and 2013). This was accomplished in three distinct sub-steps. In the first sub-step, we employed an averaging method over a moving window of 17x17 pixels (i.e., approximately the equivalent of 500x500 m) for up-scaling pixels of Landsat- 8 TOA images into the spatial resolution of MODIS images (i.e., 500 m). This was done in order to make both of Landsat-8 and MODIS images similar in the context of their spatial resolutions and to increase the spectral reliability (Ling et al. 2008) By this way, the spectral variance between the images would decrease while the spatial autocorrelation 59

74 would increase; which were investigated in different studies (Goodin et al. 2002; Ju et al. 2005; Nelson et al,. 2009). In the second sub-step, we determined linear relationships between the up-scaled Landsat- 8 and MODIS images for each of the spectral bands by generating scatter plots between them. The coefficients of the linear relationships (i.e., slope and intercept) were then used with the original Landsat-8 TOA images (i.e., 30 m) in order to generate Landsat-8 surface reflectance images in the scope of the third step. It would be worthwhile to mention that the use of LEDAPS (the Landsat Ecosystem Disturbance Adaptive Processing System) atmospheric correction algorithm was not applicable for Landsat-8 (NASA 2014). Finally, we employed the Landsat-8 quality assessment (QA) bands for determining the cloud-contaminated pixels which were excluded in further analysis Converting DN-values into surface temperature Landsat-8 thermal images was available in the form of digital number (DN). Then, we followed several steps in transforming these DN-values into LSTs. Firstly, we converted DN-values into brightness temperature (Tb) using the following equations described in USGS (2013): r=m * DN + A (3.2) T b = K 2 ln( K 1 r + 1) (3.3) 60

75 where, r is top of atmosphere (TOA) spectral radiance in w/m 2 *sr*μm, M is band-specific multiplicative rescaling factor, A is band-specific additive rescaling factor, DN is digital number of the pixel, Tb is the at-satellite brightness temperature in Kelvin (K), K1 and K2 are band-specific thermal conversion constants. The values of M, A, K1, and K2 are found in the metadata file of each image. Secondly, we transformed the Tb-values into LST using MODIS LST images; which was accomplished in three steps. For example: (i) we resampled the spatial resolution of Landsat-8 Tb from 30 m to 1000 m (i.e., the spatial resolution of MODIS LSTs) by averaging over a moving window of 33x33 pixels; (ii) then we established linear relations between the resampled Landsat- 8 Tb-values and MODIS LST-values; and (iii) finally, the determined coefficients (i.e., slope and intercept) from the linear relationships in step (ii) were used in conjunction with the original Landsat-8 Tb images at 30 m spatial resolution to calculate the LSTs Processing of ground-based precipitation data and its use in calculating SPI Processing of ground-based precipitation data Though, there were 34 agro-climate stations located within the study area; however, we considered only 19 stations (see Figure 3.1 for locational information and Table 3.2 for description). As these stations had very limited amount of missing data and also located within 1 km buffer zone of agricultural land types. This was important to insure data continuity and to reflect the spatial distribution of precipitation. After that, we generated point feature maps showing the distribution of these stations using GIS tools (see Figure 3.1 as an example of the point feature maps) and 61

76 projected them into UTM projection system to be compatible with Landsat-8 images projection for superimposition analysis. All relevant attribute information was added to the point feature maps for further analysis. Table 3.2: Description of the 19 agro-climate stations used in this research Num. Station ID Station Name Longitude Latitude 1 AB0004 Kh.El-Wahadneh 35 o 44' " E 32 o 19' " N 2 AB0008 Kufr Awan 35 o 41' " E 32 o 25' " N 3 AD0011 En Nueiyime 35 o 54' " E 32 o 25' 1.833" N 4 AF0002 Rihaba 35 o 47' 3.511" E 32 o 26' 9.342" N 5 AH0003 Ras Muneif 35 o 48' " E 32 o 22' " N 6 AJ0002 Kufrinja 35 o 42' 9.880" E 32 o 17' " N 7 AL0004 Jarash 35 o 53' " E 32 o 16' " N 8 AL0005 Kitta 35 o 50' " E 32 o 16' " N 9 AL0017 Marka 35 o 50' " E 32 o 1' " N 10 AL0027 Subeihi 35 o 42' " E 32 o 8' " N 11 AL0035 Baq'a 35 o 50' " E 32 o 4' " N 12 AL0036 P. Feisal Nursery 35 o 53' " E 32 o 12' " N 13 AL0045 Um Jauza 35 o 44' " E 32 o 5' " N 14 AL0050 Qafqafa 35 o 56' " E 32 o 20' " N 15 AL0053 King Talal Dam 35 o 49' " E 32 o 11' " N 16 AM0001 Salt 35 o 43' " E 32 o 2' " N 16 AM0002 W.Shu eib 35 o 43' 4.474" E 31 o 58' 2.019" N 17 AM0007 South Shuna 35 o 38' " E 31 o 54' " N 18 AN0002 Wadi Es-Sir 35 o 49' 5.853" E 31 o 57' 2.195" N 19 AD0018 Ibbin 3548' " E 3221' " N Calculating SPI At each of the 19 stations, we used the station-specific daily precipitation records in calculating 8- day SPI-values using the protocols described in McKee et al. (1993). As per protocols, the SPI 62

77 value of a given station at a specific time period (e.g., 8-day total daily precipitation) is calculated by comparing the precipitation totals from the same 8-day period for all the years in the long-term record (i.e., in my case). For example, 8-day SPI at 16 of February compares the precipitation total in the period 9-16 of February in a particular year with the same period precipitation totals of all the years. After that, the long-term precipitation is fitted to Gamma probability distribution function which will have a specific standard deviation and a mean. Therefore, depending on the rainfall amounts; each station will have a different standard deviation and a different mean (Edward and McKee 1997). Thus, they will not be comparable to each other in terms of drought. This is because drought is compared to the normal rainfall which varies from one area to another. To solve this, the cumulative probability gamma function is converted to Gaussian distribution with mean of zero and standard deviation of one. This will ensure that the probability that the rainfall is less than or equal to any rainfall amount will be the same as the probability that the new variate is less than or equal to the corresponding value of that rainfall amount (Edward and McKee 1997). Note that these protocols were followed to calculate SPI for each one of the 19 station in the study area. The rationale of computing 8-day SPI-values was to synchronize with the 8-day intervals of the Landsat-8 actual/synthetic images. Also, note that we computed groups of 8-day SPI-values termed as: SPI-1, SPI-2, SPI-3, SPI-4, SPI-5, SPI-6, SPI-7, and SPI-8 coinciding with each of the Landsat-8 image dates (see Figure 3.4 for details). In fact, both calculations and uses of short-term SPI-values (e.g., daily, weekly, 10 days, and bi-weekly intervals) for agricultural drought monitoring were evident in the literature. For example: (i) Hayes et al. (2002), Svoboda et al. (2002), Wu and Wilhite (2004), and Brown et al. (2008) used weekly SPI-values over various 63

78 states in USA; (ii) Brown (2008) and Roudier and Mahe (2010) used 10-day SPI values over Africa and Bani river basin in Mali, respectively; (iii) Sims et al. (2002) compared SPI against PDSI at daily, weekly and biweekly temporal scales over North Carolina, USA; and observed SPI was better indicator in comparison to PDSI. we then categorized these SPI-values into four classes upon modifying the schema described in Lloyd-Hughes and Saunders (2002) as follows: (i) wet (i.e., SPI-value > 0); (ii) mild drought (i.e., SPI-value between 0 and -0.99); (iii) moderate drought (i.e., SPI-value between -0.1 and -1.49); and (iv) severe drought (i.e., SPI-value -1.5). Note that there were four classes describing wetness conditions in Lloyd-Hughes and Saunders (2002); however, we grouped them into one class as the detailed wetness conditions were beyond the scope of our research. Note that we selected SPI to evaluate our results due to its simplicity and robustness in characterizing drought conditions and its flexibility to be calculated for different time periods (Logan 2010, WMO 2012); which was also used effectively in assessing agricultural drought in different studies (Wu and Wilhite 2004, Quiring et. al. 2003, Brown 2008, Rhee, 2010). 64

79 Actual Landsat-8 image date Synthetic Landsat-8 image date Actual Landsat-8 image date Day SPI-1 SPI-2 SPI-3 SPI-4 SPI-5 SPI-6 SPI-7 SPI-8 Figure 3.4: Schematic diagram illustrating the ways of calculating 8-day SPI-values around the Landsat-8 actual/synthetic image dates. For example, SPI-1 represents the SPIvalue of the total precipitation during 7 days before the image date plus the amount of that date comparing to the long-term precipitation of the same dates. SPI-5 represents the SPI value of the total precipitation during 3 days before the image date plus the amount of that date and 4 days after that date comparing to the long-term precipitation of the same dates Processing of other geographical data The other geographical data such as boundary of study area and land used map was processed in GIS environment and projected to Universal Transverse Mercator (UTM Zone 36N -WGS84) to be compatible with Landsat-8 data projection for further use in the analysis. 65

80 Chapter Four: Methods In this chapter, we synthesize the methods employed in the scope of this thesis in order to achieve the objectives outlines in Section 1.3 in Chapter 1. The methods consist of three major components, such as (i) developing spatio-temporal image fusion model (STI-FM) for enhancing the temporal resolution of Landsat-8 land surface temperature (LST) images; (ii) evaluating the spatio-temporal image fusion model (STI-FM) with its required modifications for generating high spatio-temporal resolution Landsat-8 surface reflectance images; and (iii) developing a remote sensing-based method for monitoring agricultural drought conditions and its evaluation. The following subsections show the description of each of these components. 4.1 Developing spatio-temporal image fusion model (STI-FM) for enhancing the temporal resolution of Landsat-8 land surface temperature (LST) images Figure 4.1 shows the schematic diagram for generating synthetic Landsat-8 LST images and its validation. Here, we proposed two major assumptions. Firstly, there would be linear relationship between the two MODIS LST images [i.e., M(t1) and M(t2)]. This would be the case as temperature regimes usually would follow a distinct temporal pattern if the land cover types wouldn t change (Hassan et al. 2007). Secondly, LSTs derived from both Landsat-8 and MODIS images at a particular time period would be similar [e.g., L(t1) M(t1) or (L(t2) M(t2)]; because the acquisition of these images were taken place under similar atmospheric conditions (Gao et al. 2006). So thus, we determined a linear relationship (i.e., slope a and intercept c) between M(t1) and M(t2) and then applied with the L(t1) to generate the synthetic Landsat-8 LST image at time 2 [i.e., synth-l(t2)] (see equations 4.1 and 4.2). 66

81 M(t2)= a *M(t1)+ c (4.1) synth- L(t2)= a *L(t1) + c (4.2) Figure 4.1: Schematic diagram of the methodology for generating synthetic Landsat-8 LST images 67

82 4.1.1 Validating the synthetic Landsat-8 LST images In order to validate the accuracy of the synthetic Landsat-8 LST images, we compared them with actual Landsat-8 LST images in two ways: (i) qualitative evaluation by comparing shapes, textures, and tones of different land cover types in the synthetic and actual images; and (ii) quantitative evaluation using statistical metrics, such as coefficient of determination (r 2 ), root mean square error (RMSE), and absolute average difference (AAD). Note that we generated and synth-l(t2) images at 16-day interval also and evaluated with the actual L(t2) images (i.e., acquired at 16-day intervals); despite the fact that we generated synthetic images at every 8-day intervals. The formulations of these statistical measures are as follows: (A (t) -A (t) )(S (t) -S (t) ) r 2 = [ (A (t) -A (t) ) 2 (S 2] (t) -S (t) ) 2 (4.3) RMSE = [S (t)-a (t) ] 2 n (4.4) AAD = 1 n S (t)-a (t) (4.5) where, A(t) and S(t) are the actual and the synthetic Landsat-8 surface reflectance images; A (t) and S (t) are the mean values of the actual and the synthetic Landsat-8 images; and n is the number of observations. 68

83 4.2 Evaluating the spatio-temporal image fusion model (STI-FM) with its required modifications for generating high spatio-temporal resolution Landsat-8 surface reflectance images I evaluated the spatio-temporal image fusion model (STI-FM) to generate a synthetic Landsat-8 image [i.e., synth-l(t2)] for the blue, red, NIR, and SWIR spectral bands by integrating a pair of MODIS images taken at two different times [i.e., M(t1) and M(t2)] and a Landsat-8 image taken at time one [i.e., L(t1)]. The rationale behind the choosing of these spectral bands were due to their widely usage in the calculation of vegetation greenness and wetness conditions. Our aim with regard to this part was to implement this technique over a heterogeneous agriculture-dominant semi-arid region in Jordan, Middle East. Figure 4.2 shows a schematic diagram of the proposed spatio-temporal image-fusion model (STI- FM) framework with its specific modification for surface reflectance data. It consisted of two major components, such as (i) establishing the relationships between MODIS images acquired at two different times [i.e., M(t1) and M(t2)], and (ii) generating the synthetic Landsat-8 surface reflectance images at time two [i.e., synth-l(t2)] by combining the Landsat-8 images acquired at time 1 [i.e., L(t1)] and the relationship constructed in the first component; and its validation. These components are briefly described in the following subsections. 69

84 Figure 4.2: Schematic diagram of the proposed spatio-temporal image-fusion model (STI- FM) for enhancing the temporal resolution of Landsat-8 surface reflectance images. 70

85 4.2.1 Establishing relationships between MODIS images acquired at two different times In order to establish relations between the two MODIS images [i.e., M(t1) and M(t2)], we performed the following steps: We calculated a ratio image [i.e., M(t2)/M(t1)] using the pair of MODIS images for each of the spectral bands of interest in order to determine the rate of the temporal change between the two dates; The ratio image was then classified into three clusters on the basis of assuming that approximately ± 15% variation in surface reflectance (i.e., albedo) is common for various natural surfaces (e.g., conifer forests, deciduous forest, agriculture crops, grass, etc.) (Oke, 1987; Ahrens, 2012). These clusters included: (i) negligible changes [i.e., variation within ± 15%; M(t2) M(t1)]; (ii) negative change [i.e., <15%; M(t2)<M(t1)]; and (iii) positive change [i.e., >15%; M(t2)>M(t1)]; and For each of the three clusters, we produced cluster-specific scatter plots between M(t1) and M(t2); and performed linear regressions (see Figure 4.3 for details). 71

86 Figure 4.3: Conceptual relationships between the two MODIS images at two different times Generating the synthetic Landsat-8 surface reflectance images at time two and its validation In generating the synthetic Landsat-8 surface reflectance image [i.e., synth-l(t2)] at 8-day intervals, we employed the Landsat-8 image acquired at time 1 [i.e., L(t1)] in conjunction with the classified image and cluster-specific linear regression models derived in section Note that we produced the synth-l(t2) images at 16-day interval also andevaluated them with the actual L(t2) images acquired at 16-day intervals as the actual Landsat-8 were only available at every 16 day temporal resolution. In this case, we used two methods: (i) qualitative evaluation that involved visual 72

87 examination; and (ii) quantitative evaluation using statistical measurements, such as coefficient of determination (r 2 ), root mean square error (RMSE), and absolute average difference (AAD). 4.3 Developing a remote sensing-based method for monitoring agricultural drought conditions and its evaluation The development of the remote sensing-based agricultural drought index consists of the following main steps such as, (i) calculating drought-related variables and determining uncorrelated ones; (ii) developing a remote sensing-based agricultural drought index; and (iii) evaluating remote sensing-based agricultural drought index using SPI Calculating drought-related variables and determining uncorrelated ones In this part of our research, we used Landsat-8 and MODIS data spanning from October to June during the growing seasons (refer to Table 3.1). This included 16 Landsat-8 images (i.e., nine for growing season and seven for the growing season ); and 52 MODIS images (i.e., 26 image covering each growing season). The both datasets were fused using the STI- FM algorithm in order to generate synthetic Landsat-8 data to create 8-day time-series of Landsat- 8 actual and synthetic data over the entire growing seasons. The implementation of STI-FM algorithm revealed the generation of 17 and 19 number of synthetic Landsat-8 data/images for the growing season of and , respectively. 73

88 I then calculated the following set of drought-related variables, such as NDWI (Gao et al. 1996), VSDI (Zhang et al. 2013a), MSI (Hunt and Rock 1989), NDVI (Tucker and Choudhury 1987), and NMDI (Wang and Qu 2007). NDWI = ρ NIR - ρ SWIR1 ρ NIR + ρ SWIR1 (4.6) VSDI = 1 - [(ρ SWIR2 - ρ B ) + (ρ R - ρ B ) (4.7) MSI = ρ SWIR2 ρ NIR (4.8) NDVI = ρ NIR - ρ R ρ NIR + ρ R (4.9) NMDI = ρ NIR (ρ SWIR1 - ρ SWIR2 ) ρ NIR + (ρ SWIR1 + ρ SWIR2 ) (4.10) where, ρ is the surface reflectance value of blue (B), red (R), near infrared (NIR), and shortwave infrared (SWIR1, and SWIR2 centred at ~1.64, and ~2.14 µm, respectively) bands. Along with the above-mentioned spectral indices and LST, we implemented principal component analysis (PCA) in order to transform the original 6 variables into a substantially smaller and easer to interpret set of uncorrelated variables that represent most of the information present in the original dataset (Jensen 2005). In this case, we computed covariance matrix of the six input variables, then we calculated eigenvalues and related eigenvector matrix (Jensen 2005). Then, we used these values to determine: (i) the percentage of total variance explained by each principal component using equation 4.11; and (ii) the correlation or how each variable loads in each principal component using equation Note that we performed the analysis for 4 different dataset (i.e., 17 Mar. 2014, 02 Apr. 2014, 18 Apr. 2014, and 04 May. 2014). 74

89 %p = E p * 100 E p (4.11) Rvp = a vp * E p Var v (4.12) where, %p is the total variance explained by component p; Ep is eigenvalue of component p; Rvp is correlation between variable v and component p; avp is the eigenvector for variable v and component p; and Varv is the variance of variable v in the covariance matrix. This analysis led to determine a set of uncorrelated (i.e., NDWI, VSDI, and LST; see in Section for details); which would be used as input variables in developing the method for monitoring agriculture drought conditions Developing a remote sensing-based agricultural drought index Over the agricultural land cover (see Figure 3.1b), we extracted the set of uncorrelated variables (i.e., NDWIi, VSDIi, and LSTi) for each of the 8-day periods (i); and calculated the study areaspecific average values for the variable of interests (i.e., NDWI, i VSDI, i and ) LST i over each 8- day period. we then compared the instantaneous values of the variable of interest (i.e., NDWIi, VSDIi, and LSTi) against NDWI, i VSDI, i and LST ; i and defined as either dry or wet conditions. Such definition of dry and wet conditions was based on the natural response for the variable of interest in the context of drought. For example: if NDWI i or VSDI i <NDWI i or VSDI, i then the pixel would be classified as dry as low values of both NDWI and VSDI would indicate more dry conditions, or vice-versa; and if LST i >LST i, then the pixel would be classified as dry as high values of LST would indicate more dry conditions, or vice-versa. 75

90 Finally, we combined the binary agricultural drought maps resulted from each of the individual variables and integrated into the following 4 classes as part of the proposed agricultural drought index : (i) severe drought if all three variables indicated dry conditions; (ii) moderate drought if any two variables indicated dry conditions; (iii) mild drought if one variable indicated dry conditions; and (iv) wet if all the variables indicated wet conditions Evaluating remote sensing-based agricultural drought index using SPI Upon generating the agricultural drought index maps at 8-day intervals, we evaluated them against the set of 8-day SPI-values shown in Figure 3.4. At the location of each of the 19 agro-climate stations where the SPI-values were calculated, we created a 1 km buffer zone and then computed the dominant (i.e., majority of occurrences) drought class within each buffer from the drought maps. Subsequently, we compared these drought classes with correspond to the point-based SPIvalues (i.e., SPI-1, SPI-2,, SPI-8) by generating confusion matrices. I, then, used the confusion matrices to compute agreements between the remote sensing-based drought index maps and ground-based SPI-values in terms of overall, user s, and producer s accuracies; and Kappa statistics. 76

91 Chapter Five: Results and Discussion The results of the developed spatio-temporal data fusion techniques and the operational agricultural drought monitoring index are presented as follows: (i) evaluation of synthetic Landsat- 8 LST images; (ii) evaluation of synthetic Landsat-8 surface reflectance images; (iii) evaluation of remote sensing-based agricultural drought index (ADI). 5.1 Evaluation of synthetic Landsat-8 LST images Relationship between two MODIS LST images Figure 5.1 shows examples of the relationship between two MODIS LST images during the period 18 June to 12 August In all cases, we found strong relationships between the LST images, (i.e., r 2 between ; slopes between ; and intercepts between ). Such strong relationships might be explained from the distinct pattern of temperature regimes in the study area (see Figure 5.2 for details). Figure 5.2 revealed that 8-day average: (i) ground-based air temperature at Marka climate station (see Figure 3.1 for the location); (ii) MODIS LST for Marka station; and (iii) MODIS LST for the entire study area were similar for the specific type of measurements/estimates over the period of interest (i.e., 18 June 2013 to 24 August 2013). It would be interesting to note that it was not possible to compare our results with other studies as we didn t find similar ones in the literature so far. Also LST values were found to be higher than groundbased air temperature measurements (see Figure 5.2). This would be the case as the surface would be much warmer than the adjacent air masses during summer day-time in mid latitudes (i.e., 25 o to 40 o ) as a result of increased incident solar radiation (Mildrexler et al. 2011). It would be worthwhile 77

92 to mention that these findings supported our first assumption, which thought distinct LST patterns in case of same/similar land cover types. Figure 5.1: Relation between two MODIS LST images acquired at time 1 and time 2 [i.e., M(t1) and M(t2)]. Figure 5.2: 8-day average: (i) air temperature at Marka climate station; (ii) MODIS-derived 8-day LST at Marka station; and (iii) MODIS-derived study area-specific average LST; during the period 18 June 2013 to 12 August

93 5.1.2 Evaluating the synthetic Landsat-8 LST images Figure 5.3 shows an example of qualitative evaluation between actual and synthetic Landsat-8 LST images during 4 July This comparison demonstrated an obvious matching in terms of patterns, shapes, sizes, and textures of its features. In this context, we investigated closely four dominant land cover types, such as forests (see Figure 5.3 panels a1 and b1), water body (Figure 5.3 panels a2 and b2), agricultural lands (Figure 5.3 panels a3 and b3), and urban areas (Figure 5.3 panels a4 and b4); and found that the synthetic image predicted LST of these land cover types accurately. In addition to this visual evaluation, we also generated histograms for actual and synthetic LST images for the whole study area and four dominant land cover types; and observed their similarities (see Figure 5.4 panels a-e). Also, we extracted the LSTs from both of the actual and synthetic Landsat-8 images during 4 July 2013 along a transect of about 50 km originated from the northwest to the southeast direction traveling through various land cover types (see the black and gray arrows in Figure 5.3). In general, they were similar to each other as the peaks and the spikes of LST values were consistent along the two transects passing through the actual and synthetic LST images (see Figure 5.5). 79

94 Figure 5.3: Comparative example between actual (a) and synthetic (b) Landsat-8 LST images for 4 July 2013). The panels [(a1), (b1)], [(a2), (b2)], [(a3), (b3)], and [(a4), (b4)] shows enlarged views over forest, water body, agricultural lands, and urban area respectively for both actual and synthetic images. 80

95 Figure 5.4: Panels (a), (b), (c), (d), and (e) represent histograms of actual (black solid line) and synthetic (gray dashed line) images for the whole study area, forests, water body, agricultural fields, and urban area, respectively. Figure 5.5: Spatial profiles of the pixels along ~ 50 km northwest southeast transect travelling through various land cover types for actual LST image (i.e., the black solid line in Figure 5.3a) and synthetic LST image (i.e., the gray dashed line in Figure 5.3b). 81

96 In terms of quantitative evaluation, we plotted the actual and synthetic Landsat-8 LST-values July 2013, 20 July 2013, and 5 August 2013 as shown in Figure 5.6. In all of the cases, we found that strong relationships were existed between the variables of interest. For example: r 2, RMSE, and AAD-values were in the ranges , K, and K, respectively. In addition, the regression lines were having close relation with the 1:1 lines, such as the slopes and intercepts were in the ranges and respectively. we also calculated the minimum, maximum, mean, and standard deviation values of each actual and its corresponding synthetic LST images (see Table 5.1). Note that these values were very close to each other. In fact, the strong agreement between the synthetic and the actual Landsat-8 LST images would support/validate our second assumption that the LSTs derived from Landsat-8 and MODIS sensors would have similar values as they would acquire images almost at the same time. Figure 5.6: Scatter plots of the relation between actual and synthetic Landsat-8 LST image for (a) 4 July 2013, (b) 20 July 2013, and (c) 5 August The dotted and continue lines represent 1:1 and regression line respectively. 82

97 Table 5.1: Statistical comparisons between actual and synthetic Landsat-8 LST images. The LST values are given in Kelvin. Date LST Image Minimum Maximum Mean Standard deviation 4 Jul Jul Aug Actual Synthetic Actual Synthetic Actual Synthetic It would be interesting to note that the results of the model were similar or even better than other studies reported in the literature. For example: (i) Liu and Weng (2012) observed less than 0.2 AAD-values between the actual and simulated LST images and less than 1 o C of standard deviation; (ii) Huang et al. (2013) found that the correlation coefficient between the observed and the predicted LST images was in the range 0.72 to 0.83, and RMSE was between 0.96 K and 2.6 K; (iii) Wu et al. (2013) obtained AAD and RMSE values of 1.3 K and 1.6 K between the actual and the predicted images respectively; (iv) Weng et al. (2014) found good agreements between the actual and predicted LST images with correlation coefficient values in the range and mean difference and AAD-values in the range K and K respectively. 83

98 5.2 Evaluation of STI-FM using surface reflectance images Evaluation of the relationships between MODIS images acquired at two different times Figure 5.7 shows the relation between 8-day composite of MODIS images acquired in two different dates for the spectral bands of red, NIR and SWIR2.13µm during the period 2 June to 12 August It revealed that strong relation were existed for each of the clusters (i.e., negligible change, negative change, and positive change) over all of the spectral bands during the period of observation. For example: the r 2, slope, and intercept-values were in the range: (i) , , and respectively, for negligible change cluster; (ii) , , and respectively, for negative change cluster; and (iii) , , and respectively, for positive change cluster. Regression analysis showed that the negligible change clusters revealed the highest correlation values because no significant changes occurred in the study area during the two 8-day composite MODIS images of interest at 16 days interval. Note that we were unable to compare our findings as there were no similar studies found in the literature so far. 84

99 Figure 5.7: Relation between 8-day composite of MODIS surface reflectance images acquired at time 1 [i.e., M(t1)] and time 2 [i.e., M(t2)] for the spectral bands of red [panels (a)- (d)], NIR [(e)-(h)], and SWIR2.13µm [(i)-(l)] during the period 2 June to 12 August

100 5.2.2 Evaluation of the synthetic Landsat-8 surface reflectance images Prior to conducting quantitative evaluations, we performed qualitative evaluations by comparing the actual and synthetic Landsat-8 images. In these cases, we generated pseudo-colour composite images by putting the NIR, Red and SWIR spectral bands in the Red, Green, and Blue colourplanes of the computer; and such an example is shown in Figure 5.8. In fact, we evaluate four different land cover types (i.e., agricultural lands in Figures 5.8a1 and 5.8b1; forests in Figures 5.8a2 and 5.8b2; water body in Figures 5.8a3 and 5.8b3; and urban areas in Figures 5.8a4 and 5.8b4). In general, we observed that the visual clues (e.g., location, shape, size, and texture in particular) were reproduced in the synthetic images with negligible differences in comparison to that of the actual images. However, the tones (i.e., the digital numbers representing the surface reflectancevalues) were having some differences. These might happen due to the use of 500 m spatial resolution MODIS surface reflectance images in calculating the Landsat-8 surface reflectancevalues at 30 m resolution. 86

101 Figure 5.8: Example comparison between pseudo-colour composites images by putting the NIR, red and SWIR spectral bands in the red, green, and blue colour-planes of the computer respectively for actual and synthetic Landsat-8 images during 18 June

102 Figure 5.9 shows the relationship between the actual Landsat-8 surface reflectance images and the synthetic Landsat-8 surface reflectance images for red, NIR and SWIR2.2µm spectral bands for the 18 June 2013, 4 July 2013, 20 July 2013, and 5 August It demonstrated that strong relations were existed between the actual and synthetic images for all the spectral bands of interest over the period of study. In the context of linear regression analysis; the r 2, slope, and intercept-values were in the range: (i) , , and respectively, for red spectral band; (ii) , , and respectively, for NIR spectral band; and (iii) , , and respectively, for SWIR2.2µm spectral band. In the context of RMSE analyses, they were: (i) in between for red spectral band; (ii) for NIR spectral band; and (iii) for SWIR2.2µm spectral band. In addition, the AAD-values were , , and between for the red, NIR, and SWIR2.2µm spectral bands respectively. 88

103 Figure 5.9: Relation between the actual Landsat-8 surface reflectance image and its corresponding synthetic Landsat-8 surface reflectance images for the red panels [(a)- (d)], NIR [(e)-(h)], SWIR2.2µm [(i)-(l)] spectral bands. The dotted and continue lines represent 1:1 and regression line respectively. 89

104 It would be worthwhile to note that our findings were quite similar or even better in some cases with compare to other studies. For example: (i) Gao et al. (2006) implemented STARFM over boreal forest and obtained AAD-values of 0.004, and for red, NIR and SWIR2.2µm spectral bands respectively; (ii) Roy et al. (2008) applied a semi-physical fusion model over two study sites in United States (Oregon and Idaho) and got AAD-values of 0.015, 0.22, and 0.28 for Oregon site for red, NIR and SWIR2.2µm spectral bands respectively; (iii) Zhu et al. (2010) applied ESTARFM over heterogeneous regions and achieved AAD-values of and for red and NIR spectral bands respectively; (iv) Walker et all., (2012) used STARFM to generate synthetic Landsat ETM+ surface reflectance images over dry-land forests; and found that the r 2 values were 0.85 and 0.51 for red and NIR spectral bands; (v) Song and Hang (2013) employed spars representation-based synthetic technique over boreal forests and found r 2 values of 0.71 and 0.90; RMSE values of 0.02 and 0.03; and AAD-values of 0.01 and 0.21 for red and NIR spectral bands respectively; and (vi) Zhang et al. (2013) used ESTDFM and observed r 2 values of 0.73 and 0.82, and AAD-values of and for red and NIR spectral bands respectively. 5.3 Evaluation of remote sensing-based agricultural drought index (ADI) Principal component analysis Table 5.2 shows the eigenvalues, percentage of variance, and cumulative variance explained by each principal component (PC) for four Landsat-8 images acquired on 17 Mar. 2014, and 02 Apr. 2014, 18 Apr and 04 May It revealed that PC1, PC2, and PC3 accounted for between %, %, and % of the total variance, respectively. Cumulatively, these three principal components accounted for between % of the variance in the 90

105 dataset. Table 5.3 shows the corresponding correlation/loading values of each variable in these three major principal components (i.e., PC1, PC2, and PC3). According to the theory, the variable responsible for the highest loading might represent a particular PC of interest (Meng et al. 2009). For example, PC1 consisted of four variables (i.e., NDWI, MSI, NMDI, and VSDI) with relatively high negative correlations for all the four images. Among them, NDWI contributed the most in all the acquisition dates (i.e., to -0.99), which was considered as the representative variable for PC1. In the case of the PC2, LST was found to be the mostly contributed variable with negative loading (i.e., to -0.98) so that considered as the representative of PC2. For PC3, VSDI was the highest contributor with positive values (i.e., 0.45 to 0.52) in all the dataset; so it was picked as PC3 s representative. Based on this analysis, we considered NDWI, VSDI, and LST would be the set of uncorrelated input variables for developing agricultural drought monitoring index. 91

106 Table 5.2: Eigenvalues, percentage of variance, and cumulative variance explained by each principal component for four Landsat-8 images acquired during the growing season. Acquisition Date PC Eigenvalues % of variance Cumulative variance (%) Mar Apr Apr May

107 Table 5.3: Degree of correlation/loading between each variable and each PC Date 17 Mar Apr Apr May Variable PC1 PC2 PC3 PC1 PC2 PC3 PC1 PC2 PC3 PC1 PC2 PC3 LST MSI NDVI NDWI NMDI VSDI Evaluating the results of remote sensing-based agricultural drought index Table 5.4 shows the agreements between the remote sensing-derived ADI-classes and corresponding point-based SPI values (i.e., SPI-1, SPI-2,, SPI-8). we observed higher degrees of agreements between the remote sensing-derived ADI classes and: (i) SPI-4 (i.e., overall accuracy 83% and Kappa-values 76%); and (ii) SPI-5 (i.e., overall accuracy 85% and Kappavalues 78%). This might be referred to the direct and fast response of wetness agricultural drought related variables (that included NDWI, VSDI, and LST) in the context of vegetation and soil water content stress (Dennison et al. 2005, Gu et al. 2008). 93

108 Table 5.4: Agreements between remote sensing-derived agricultural drought index classes and corresponding point-based SPI-values. SPI-values Overall accuracy (%) Kappa (%) SPI SPI SPI SPI SPI SPI SPI SPI In addition, we also calculated the user s and producer s accuracies for each confusion matrix to measure how well each individual class agreed with the SPI classes; and example cases (i.e., ADI vs. SPI-4, and ADI vs. SPI-5) are presented in Table 5.5. In both of the cases, we found that the drought class-specific user s and producer s accuracies were in the range 67-93% and 62-97% respectively. Our findings would, in fact, support the effectiveness of our method in monitoring agricultural drought conditions at field scale over the short time periods. However, we observed some percentage of disagreement between remote sensing-derived ADI and SPI, which might be referred to different factors including; topographical and soil parameters (i.e., elevation, aspect, slope, moisture, and soil type; van Wesemael et. al. 2003), meteorological parameters (e.g., solar radiation and air temperature, amount and duration of precipitation, and local wind regimes), and vegetation types and phenology (Boken et al. 2005). Such these factors might have important roles in defining agricultural drought conditions. It would be worthwhile to mention that our proposed 94

109 method had shown to forecast drought over short time period (e.g., 3 to 4 days) if no rainfall would occur. Table 5.5: Examples of confusion matrices between remote sensing-derived agricultural drought index (ADI) and SPI classes, i.e., (a) ADI vs. SPI-4, and (b) ADI vs. SPI-5. (a) Remote sensing-derived ADI classes Severe drought Moderate drought Mild drought Wet Row total Producer s accuracy (%) Severe drought SPI-4 classes Moderate drought Mild drought Wet Column total User s accuracy (%) (b) Remote sensing-derived ADI classes Severe drought Moderate drought Mild drought Wet Row total Producer s accuracy (%) Severe drought SPI-5 classes Moderate drought Mild drought Wet Column total User s accuracy (%)

110 In order to comprehend the spatial dynamic of agricultural drought conditions, we generated agricultural drought distribution maps for each time period during the growing seasons of interest (i.e., 26 maps for each growing season); and such example cases are shown in Figures 5.10 to Figure 5.10 shows agricultural drought map generated by combining the three uncorrelated variables (i.e., NDWI, VSDI, and LST) derived from actual Landsat-8 image acquired on 13 Feb [i.e., during the period of vegetation germination (Saba et al. 2010)]. It showed that approximately 60% of the study area fell under the three drought classes (i.e., 28% mild drought, 14% moderate drought, and 18% severe drought). In another case (see Figure 5.11), we combined the synthetic Landsat-8 data on 10 Apr [during the peak period of vegetation growth (Saba et al. 2010)]. It showed that approximately 79% of the study area fell under the three drought classes (i.e., 19% mild drought, 29% moderate drought, and 31% severe drought). Similarly, the agricultural drought map generated from synthetic Landsat-8 data on 16 Feb (see Figure 5.12) showed approximately 80% of the study area fell under the drought classes (i.e., 30% mild drought, 28% moderate drought and 22% severe drought). The drought conditions derived from actual Landsat-8 data on 21 Apr (see Figure 5.13) showed approximately 74% from the study area fell under the drought classes (i.e., 24% mild drought, 23% moderate drought and 27% severe drought). Please note that we used the station-based SPI-values to compare with the remote sensing-derived drought classes at the 19 weather station sites within approximately 40 km x 90 km catchment area where long-term precipitation records were available. Given the data availability, we considered that the use of 19 stations SPI data for evaluating the proposed index would be reasonable. In addition, due to the limitations of the GIS-based interpolation methods (Li and Heap 2014), we opted not to generate spatial dynamics of SPI. Thus, upon validating this 96

111 remote sensing-derived index by use of ground-based SPI-values at 19 point locations, we were reasonably confident in our map as it was developed independent of SPI regimes. Figure 5.10: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on actual Landsat-8 data acquired on 13 Feb coinciding with the period of vegetation germination. Note that the white colored areas represented non-agricultural lands. 97

112 Figure 5.11: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on synthetic Landsat-8 data generated on 10 Apr at the peak period of the vegetation growth. Note that the white colored areas represented non-agricultural lands. 98

113 Figure 5.12: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on synthetic Landsat-8 data generated on 16 Feb coinciding with the period of vegetation germination. Note that the white colored areas represented nonagricultural lands. 99

114 Figure 5.13: Example case of remote sensing-derived agricultural drought map upon combining three uncorrelated variables (i.e., NDWI, VSDI, and LST) based on actual Landsat-8 data generated on 21 Apr coinciding with the period of vegetation germination. Note that the white colored areas represented non-agricultural lands. 100

115 Chapter Six: Conclusions & Recommendations 6.1 Concluding remarks The following summarizes the methods and the outcomes of our research according to each objective. Objective 1: Developing a spatio-temporal image fusion model (STI-FM) for enhancing the temporal resolution (i.e., from 16 to 8 days) of Landsat-8 land surface temperature (LST) images Here, we developed a STI-FM technique and demonstrated its applicability for enhancing the temporal resolution of Landsat-8 LST images from typical 16 days to 8 days as a function of MODIS 8-day composite LST images over a heterogeneous semi-arid study area in Jordan, Middle East. Results showed strong agreements (i.e., r 2 -values in the range , RMSE-values between , and AAD-values in the range ) between the actual and synthetic LST images. we believe that the proposed technique would be applicable for satellite systems that would have similar spectral and orbital configurations other than MODIS and Landsat-8 (e.g., ASTER, MERIS, and AVHRR etc.), and for ecosystems other than semi-arid areas. However, we strongly suggest that the technique should be properly evaluated (that include calibration and validation in particular) prior to adoption. In addition, we believe that our proposed technique will provide synthetic high spatio-temporal surface temperature Landsat-8 data for different environmental applications; especially those require both high spatial and high temporal information such as agricultural drought, irrigation management, and crops monitoring. 101

116 Objective 2: Evaluating the spatio-temporal image fusion model (STI-FM) with its required modifications in generating high spatio-temporal resolution Landsat-8 surface reflectance images In the context of objective 2, we demonstrated the applicability of the STI-FM technique for enhancing temporal resolution of Landsat-8 images from 16 to 8 days using 8-day MODIS based surface reflectance images; and demonstrated its implementation over heterogeneous agriculturedominant semi-arid region in Jordan. Results showed that the proposed method could generate synthetic Landsat-8 surface reflectance images for red, NIR, and SWIR spectral bands with relatively strong accuracies, (r 2, RMSE, and AAD values were in the range ; ; and respectively). In general, our method was found to be simpler and more robust in comparison to other methods, as it does not require any specific parameters or high quality land use maps in order to predict the synthetic images, in addition, it is applicable for both homogenous and heterogeneous landscapes. Despite the accuracy and simplicity, we would recommend that the proposed method should be thoroughly evaluated prior to adopting in other environmental conditions except for semi-arid regions like the ones used in this research.. Objective 3: Developing a remote sensing-based method for monitoring agricultural drought conditions; and evaluating its performance over a semi-arid heterogeneous rainfed agricultural dominant landscape in Jordan, Middle East. In this study, we developed a remote sensing-based method for monitoring agricultural drought conditions termed as ADI and evaluated its applicability over heterogeneous agriculture-dominant semi-arid region in Jordan. The ADI employed time series of uncorrelated remote sensing-derived agricultural drought related variables (i.e., NDWI, VSDI, and LST) generated from actual and 102

117 synthetic Landsat-8 like data at 8-day interval and 30 m spatial resolution. Results showed 85% agreements in terms of overall accuracy and 78% of Kappa-values when compared to ground based group SPI-5 values which were calculated from 8-day precipitation from ground stations. We believe that our method would provide better understanding and more comprehensive view of agricultural drought conditions which would, in turn, help to mitigate its severe consequences and enhance agricultural and water management practices. 6.2 Contribution to science The contributions of this research include: 1. The proposed spatio-temporal images fusion models enhanced the temporal resolution of Landsat-8 images from 16 day into 8 day interval. This covered the commonly data missing problem found in high spatial optical and thermal remote sensing data due to the presence of cloud and long revisit period. 2. The agricultural drought monitoring index presented here has greater advantages over the traditional drought monitoring methods in regards capturing the spatial variability of shortterm drought conditions in a heterogeneous agricultural land. 3. From the operational perspective, the proposed methods combined environmental variables for agricultural drought condition at 8 day time scale; this will be greatly helpful for better understanding of plants water status in the targeted areas. Also, this will provide the support to farmers and decision makers for improving agricultural process and practices. 103

118 6.3 Recommendations for future work Though results showed strong relationships between actual and synthetic Landsat-8 LST images; it would be worthwhile to investigate the following issues in the future work: In this study, we used MODIS-derived LSTs to calibrate the Landsat-8 Tb at the top of atmosphere in order to generate the Landsat-8 LSTs images. However, this sort of calibration could also be performed using climate data over the study area if available (Li et al. 2013). Here, we used only one thermal band of Landsat-8 (i.e., µm). However, it would be possible to use two available thermal bands (i.e., µm and µm) by employing split-window method (Zhao et al. 2009; Yu et al. 2008), if properly calibrated second thermal band images would be available (USGS 2014). One of the major requirements of STI-FM would be the use of cloud-free images. However, in some regions or seasons it would be difficult to obtain such images. Therefore, cloud infilling algorithms might be useful in such cases. Though our results demonstrated strong relations between actual and synthetic Landsat-8 surface reflectance images; however some issues would be worthwhile to consider for further improvements, such as: In this study, we used MODIS surface reflectance images at 500 m spatial resolution. However, it would be possible to use such images acquired at 250 m spatial resolution in 104

119 case of red and NIR spectral bands in particular; which might enhance the quality of the synthetic image (Ling et al. 2008). One of the major requirements for the input images [i.e., L(t1), M(t1), and M(t2)] was to be free from cloud-contamination. However, it might not be possible to have images completely free from such contamination. In such events, we might use cloud-infilling algorithm. Although the use of MODIS surface reflectance products to generate Landsat-8 surface reflectance images led to predicting reasonable synthetic Landsat-8 surface reflectance images; however, it might be useful to use climate data records for generating surface reflectance images and comparing its outcome with the method adopted here. However such information were not available for Landsat-8 images at the time of conducting this study (USGS 2014). Despite the strong performance of our proposed remote sensing-derived ADI, we would like to mention the following issues for potential enhancement: Other factors, such as topography, soil properties, land use/cover, vegetation phenology, and agricultural management practices, would be worthwhile to investigate as they might play significant role in agricultural drought conditions and levels. Due to the limited access to the ground-based data form its sources, we used ground-based precipitation index (i.e., SPI) only for validation. Though, the analysis showed strong results; however, we strongly recommend to use other ground-based drought related information, such as temperature and evapotranspiration. 105

120 The dynamics of agricultural drought occurrences might significantly differ from one to another ecosystem. Thus, we strongly recommend the method to be thoroughly evaluated prior any implementation in environments other than semi-arid ones. Due to the limited number of available agro-climate stations and the limitations of geospatial interpolation techniques we opted not to generate SPI spatial distribution maps for comparison with the remote sensing-derived maps. Therefore, using most recently satellites for precipitation estimation (e.g., GPM/GCCP) or numerical weather prediction models (e.g., MERRA, ECMWF, and NCEP) might be useful. For future work, ADI procedures may be automated in order to provide frequent monitoring maps of the distribution of agricultural drought conditions. 106

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144 Appendices 130

145 Appendix A: Copyright Related Information A1. Copyright permissions Certificate from Publications: Published journal papers: 1. Hazaymeh, K.; and Hassan, Q.K. Fusion of MODIS and Landsat-8 surface temperature images: A new approach. PLoS One 2015, 10, e :1-e : Hazaymeh, K.; and Hassan, Q.K. Spatiotemporal image-fusion model for enhancing temporal resolution of Landsat-8 surface reflectance images using MODIS images. Journal of Applied Remote Sensing 2015, 9, : :

146 (1) Hazaymeh and Hassan,

147 (2) Hazaymeh and Hassan,

148 A2. Copyright certificates for other figures used in this thesis 134

149 135

150

151 137

152 138

153 139

154 140

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