assessment of remote sensing indices for drought

2017 AARS, All rights reserved.* Corresponding author: [email protected], [email protected], Phone: +962-6-5355000-22444; Fax: +962-6-5300806

Assessment of Remote Sensing Indices for Drought Monitoring in Jordan

Jawad T. Al-Bakri1*, Areej Al-Khreisat1, Sari Shawash2, Eman Qaryouti1 and Muna Saba3

1Department of Land, Water and Environment, Faculty of Agriculture, The University of Jordan, Amman - Jordan2Projects Directorate, The Hashemite Fund for Development of Jordan Badia, Amman – Jordan

3Drought Monitoring Unit, National Center for Agricultural Research and Extension, Baqa’a, Jordan

Abstract

Remote sensing has been widely used in monitoring vegetation and in detecting agricultural droughts. The most commonly used data for this purpose is the coarse spatial and the high temporal resolution data of NDVI. This study compares different indices (NDVI, MSI, EWSI, PDI and MPDI) derived from MODIS for assessing drought conditions in Mafraq area in Jordan. The possible improvements in drought monitoring as a result of improved spatial resolution is also investigated in this study by comparing Landsat-NDVI with MODIS-NDVI. Results showed significant relationships among the different indices derived from MODIS and Landsat data. A significant relationship was found between Landsat-NDVI and MODIS-NDVI, with R2 value of 0.56 and RMSE of 0.078. The Landsat-NDVI was better than MODIS-NDVI in detecting drought conditions for fields of rainfed barley in the northern parts of the study area, while both datasets reflected the aridity conditions prevailing in the study area. The MODIS-PDI showed to be the most accurate indicator that was highly correlated (R2 = 0.73) with soil moisture measurements. Soil water stress indicators (EWSI and MSI) showed relatively lower correlations with soil moisture and modeled evapotranspiration when compared with Landsat-NDVI and MODIS-PDI. Therefore, the use of MODIS-PDI instead of MODIS-NDVI would be recommended for mapping drought severity without processing historical data. The use of NDVI deviations from historical means would be recommended with medium resolution data of NDVI, providing that temporal resolution would improve and more datasets from earth observation systems (EOS) would be available in real time.

Key words: Drought, remote sensing, NDVI, MPDI, SEBAL-ETa, Landsat, MODIS, Jordan.

1. Introduction

Drought is a natural phenomenon that is related to reduction in rainfall amounts received over an extended period, such as a season or a year, resulting in insufficient moisture stored in the soil (McKee et al., 1993). Different types of droughts can be defined, including the meteorological, agricultural and hydrologic types. The meteorological type of drought can be categorized for different time intervals, while agricultural drought has typically a short-time scale of one month when soil moisture and rainfall are inadequate to support crop growth leading to the loss of yield. The hydrological droughts have intermediate and long-time scales of 3, 6 and 12 months or higher, with marked depletion of surface and subsurface

water (Wilhite and Glantz, 1985; Wilhite, 2000).

Regardless of the type of drought, its frequencies and severity have increased and resulted in increasing the areas affected by this adverse phenomenon, particularly in arid environments. This is mainly attributed to climate change that resulted in changing meteorological characters such as temperatures; winds; relative humidity; and rainfall patterns and amounts (IPCC, 2007; Mishra and Singh, 2010). Subsequently, freshwater availability and agricultural production started to be adversely impacted by drought, leading to economic losses in many sectors, especially agriculture. Thus, mapping of drought is important and needed for assessment of its impacts on the different sectors


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so that management plans can be formulated and mitigation measures can be implemented according to drought severity.

In the past, methods to map drought were mainly based on the use of rainfall records and ground data to calculate the standardized precipitation index (SPI), rainfall deciles, the Palmer drought severity index (PDSI) and other drought indices (Mckee et al., 1993; WMO and GWP, 2016). The use of such indices and the accuracy of output maps would be highly affected by the spatial distribution of meteorological stations and the errors encountered by data collection. Therefore, geospatial techniques that utilize remote sensing data and geographic information systems (GIS) started to contribute to drought mapping by providing drought risk and hazard maps at different scales and for different agro-climatic zones (Shahid and Behrawan, 2008; Mahyou et al., 2010; Bin et al., 2011).

The use of remotely sensed data for agricultural drought mapping is mainly based on the generation of indices that are related to vegetation and soil water conditions. The well-known historical index that has been used for this purpose and for mapping vegetation changes is the normalized difference vegetation index (NDVI), which is mainly derived from the data of the Advanced Very High Resolution Radiometer (AVHRR) (Salam and Rahman, 2014). The NDVI is the most commonly used index that is correlated with vegetation biomass and conditions (Al-Bakri and Taylor, 2003) and, therefore, it has been widely used to map cropping type and to monitor agricultural land condition (Shofiyati and Uchida , 2011) and changes in agricultural lands (Kundu et al., 2014; Salam and Rahman, 2014).

The use of NDVI for monitoring drought and vegetation conditions is attributed to several reasons that include the simplicity in calculating this index and the historical records for this index that go back to the late 1970s. The NDVI showed to be a good indicator for mapping drought prone areas in tropics (Srinias et al., 2012). However, its response to rainfall conditions and drought would decrease in arid areas (Al-Bakri and Suleiman, 2004). This would be mainly attributed to the soil background effects that would reduce the correlation between NDVI and vegetation cover and conditions (Aggarwal and Minz, 2013). Therefore, more advanced indices were developed with time to suit drought monitoring in arid and semiarid areas. Among these indicators are the perpendicular and the modified perpendicular drought index (PDI and MPDI), which take vegetation fraction into consideration (Ghulam et al., 2007a &b). A list of indicators and indices for drought monitoring and mapping was recently published by WMO and GWP (2016).

Jordan is one of the countries in West Asia faced with frequent droughts that resulted in reducing available water resources in the already scarce water resources county (Al-Bakri et al., 2016 a&b). The country, which is dominated by arid and hyper-arid climates, witnessed a rapid population

growth that resulted from the influx of refugees and immigrants from the surrounding countries (Alsaaideh et al., 2011; Al-Bakri et al., 2013). Trends of climate change showed that Jordan would suffer from increased frequency and severity of droughts (Al-Qinna et al., 2011). Therefore, monitoring and assessment of drought severity is of primary importance for water management in Jordan. A unit was established at the national center for agricultural research and extension (NCARE) to produce maps of drought conditions. The maps are periodically produced from the NDVI data of the Moderate Resolution Imaging Spectroradiometer (MODIS) at 1-km spatial resolution (Al-Naber et al., 2009; Saba, 2015). In these maps, severity of drought is based on the degree of NDVI deviation from the long-term means. The drought monitoring unit started to use the 250 m resolution data of MODIS to produce maps of drought that were not assessed in terms of accuracy (Saba, 2015). This study aims to assess accuracy of different remotely sensed indices for drought monitoring in Jordan. With the improved temporal resolution and access to the medium spatial resolution data, it is also important to consider possible improvements in drought mapping. Therefore, this study compares the degree of agreement between the NDVI maps produced from coarse spatial resolution data of MODIS and the medium spatial resolution data of Landsat.

2. Study Area

The work was carried for an area that covers 963 km2 in the north of Jordan between Mafraq City and Safawi (Figure 1). The climate of the study area is arid where the average rainfall during the period 2000-2015 ranges from 110 to 136 mm; while the average potential evapotranspiration is 1500 mm. The average annual rainfall during the period 1970-2005 is 163 mm, which reflects drought occurrence during the last two decades. The rainy season starts in November and ends by early May with inter annual variations in rainfall amounts and distribution; with a noticed decrease in rainfall from north to south and from west to east. The mean monthly air temperatures is ranging between 7.4 and 24.5 ºC, with minimum and maximum temperatures being in January (coldest month) and August (warmest month), respectively. The period of peak growth for natural vegetation and rainfed crops occur during the end of March and early April.

Soils of the study area are mainly aridic and originally developed from volcanic origin. The main soils include lithic xeric torriorthents, xerochreptcalciorthids and camborthids (MoA, 1995) that are characterized by silty loam and silty clay loam textures. Soils in the eastern parts of the study area are still covered by and mixed with fragmented basalt rocks. In terms of biogeography, the area is classified as a steppe grassland, that is dominated by different vegetation species including Artemisia herba alba, Achilleafragrantisimia, Salsolavermiculata, Stipaspp., Avena spp., Poaspp., Bromus spp., Hordeum spp., Anabasis syriaca and others.

Asian Journal of Geoinformatics, Vol.17,No.3 (2017)

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Figure 1. Location of the Study Area.

A land use map for the study area (Figure 4) was prepared from the visual interpretation Landsat 8 image acquired in year 2015. Analysis of the land use map shows that percentages of land use in the study area are 71.2% as open rangelands, 13.5% as irrigated farms, 9.4% as rainfed areas and 5.9% as urban areas. The study area suffered from frequent droughts, with a reduction in rainfall that reached 2.6 mm per year (Abu Sada et al., 2015). This has enforced many farmers to shift from rainfed to irrigated agriculture (Al-Bakri, 2015; Al-Bakri et al., 2016b). It is expected that with drought prevailing conditions, rainfed agriculture will recess and rangelands conditions will deteriorate. Therefore, mapping spatial distribution of drought severity will help in identifying and in formulating appropriate management and action plans in the study area and in similar arid areas inside the scarce water resources country.

3. Data and Methodology

3.1 Remote Sensing Data

Remote sensing data of MODIS, the Operational Land Imager (OLI) of Landsat 8 and the Enhanced Thematic Mapper Plus (ETM+) of Landsat 7 were used to derive different indices for drought mapping (Table 1). The MODIS images had 250 m spatial resolution, while the OLI and ETM+ data (for path 173, row 38) had a spatial resolution of 30 m. Data of OLI and ETM+ were downloaded using the Earth Explorer gateway (https://earthexplorer.usgs.gov/) while MODIS data were downloaded from the Reverb gateway (http://reverb.echo.nasa.gov/). The peak growth of natural vegetation and rainfed barley occurs during the last week of March and early April (Al-Bakri and Taylor, 2003).

Therefore, both datasets were selected for last week of March and first week of April during 2000-2015 (Table 1), with exception for few years when cloud free Landsat images were not available. The data of MODIS used in this study included the NDVI images, in addition to the middle infrared (MIR) band, which was used to derive the moisture stress index (MSI). The data of Landsat included the red (R) and the near infrared (NIR) of the ETM+ and the visible, NIR and thermal infrared (TIR) of OLI. The data of Landsat ETM+ and OLI were used to derive NDVI and evapotranspiration water stress index (EWSI).

3.2 Ground Data

Ground data were collected during the dates of satellite acquisition and close to the time of overpass. The data included ground observations for spectral reflectance within one hour of the OLI overpass on 26th of March 2014 (Julian day 085). Comparisons between MODIS and Landsat indices were also made for this image. Spectral reflectance data was used to carry out atmospheric correction for the OLI so that ground level spectral reflectance would improve. This was important for the data prior to 2016, when spectral reflectance values in arid and semiarid areas were not accurate (Vermote et al., 2016). A handheld multispectral radiometer was used to collect ground measurements for target objects with a standard or a reference reflectance. These objects were newly paved car parks, dry desert surfaces, floors of abandoned limestone quarries and fields of parsley and tomatoes with full vegetation cover. The data of the handheld radiometer was used for absolute atmospheric correction using empirical line calibration (Smith and Milton, 1999) derived from linear equations that correlated ground measurements with OLI


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Table 1. Specification of remote sensing data used in the study.

data. The multiple-date image normalization with linear regression equations was carried out to correct the other OLI images.

Ground observations of soil moisture data were collected with portable soil moisture sensors that measured volumetric soil moisture content (Ɵv) to 50 cm depth. To avoid positional shifts, locations of measurements were averaged to correspond to twice the area of MODIS pixel (250m). They also included readings from automated soil moisture sensors installed in Um Ejmal weather station, operated by the Ministry of Water and Irrigation (MWI). Sites of measurements are shown in Figure 4. Data of air temperature, relative humidity, wind speed and solar radiation were provided by the MWI. The data was arranged in spreadsheet and used for calculating crop evapotranspiration (ETc) and actual transpiration (ETa) that were combined to derive EWSI.

3.3 Data Processing to Derive Drought Indices

The data of MODIS were downloaded and transformed into different indices that included MSI, PDI, and MPDI. The data of Landsat ETM+ was used to derive NDVI, after correcting striped lines in images acquired after year 2003 using the linear interpolation. The Landsat OLI data were used to derive NDVI and EWSI. The following subsections summarize the steps used to derive these indices.

3.3.1 NDVI

Remote sensing data of Landsat were processed to derive NDVI as following:

6

spectral reflectance values in arid and semiarid areas were not accurate (Vermote et al., 2016). A 1

handheld multispectral radiometer was used to collect ground measurements for target objects with a 2

standard or a reference reflectance. These objects were newly paved car parks, dry desert surfaces, 3

floors of abandoned limestone quarries and fields of parsley and tomatoes with full vegetation cover. 4

The data of the handheld radiometer was used for absolute atmospheric correction using empirical line 5

calibration (Smith and Milton, 1999) derived from linear equations that correlated ground measurements 6

with OLI data. The multiple-date image normalization with linear regression equations was carried out 7

to correct the other OLI images. 8

Table 1: Specification of remote sensing data used in the study. 9

Source Bands (wavelength, µm) Derived Indices

Landsat 7 ETM+ -B3 (0.63-0.69) and B4 (0.77-0.90). Dates: 27/3/2000, 30/3/2001, 24/3/2002, 5/4/2003, 22/3/2005, 13/4/2006, 2/4/200/, 27/3/2009, 30/3/2010, 26/3/2011, 4/4/2012, 31/3/2013, 26/3/2014

-NDVI

- EWSI

Landsat 8 OLI - B4 (0.64 - 0.67), B5 (0.85 - 0.88)

- B2 (0.45 - 0.51), B3 (0.53 - 0.59), B4 (0.64 - 0.67), B5 (0.85 - 0.88), B6 (1.57 - 1.65), B11 (10.60 - 11.19)

Date: 13/3/2015

- NDVI

MODIS - B1* (0.620 – 0.670), B2* (0.841 –0.876) , B6 (1.628 – 1.652)

Dates: 21/3 for years 2000, 2003, 2004, 2007, 2008, 2012 and 22/3 for years 2001, 2003, 2005, 2006, 2009-2011, 2013, 2014, 2015.

- NDVI, MSI, PDI, MPDI

* B1 and B2 are corresponding to B3 and B4, respectively, in the MOD13Q1 V005 data (250 m resolution). 10

Ground observations of soil moisture data were collected with portable soil moisture sensors that 11

measured volumetric soil moisture content (Ɵv) to 50 cm depth. To avoid positional shifts, locations of 12

measurements were averaged to correspond to twice the area of MODIS pixel (250m). They also 13

included readings from automated soil moisture sensors installed in Um Ejmal weather station, operated 14

by the Ministry of Water and Irrigation (MWI). Sites of measurements are shown in Figure 4. Data of 15

air temperature, relative humidity, wind speed and solar radiation were provided by the MWI. The data 16

was arranged in spreadsheet and used for calculating crop evapotranspiration (ETc) and actual 17

transpiration (ETa) that were combined to derive EWSI. 18

7

3.3 Data processing to derive drought indices 1

The data of MODIS were downloaded and transformed into different indices that included MSI, PDI, 2

and MPDI. The data of Landsat ETM+ was used to derive NDVI, after correcting striped lines in images 3

acquired after year 2003 using the linear interpolation. The Landsat OLI data were used to derive NDVI 4

and EWSI. The following subsections summarize the steps used to derive these indices. 5

3.3.1 NDVI 6

Remote sensing data of Landsat were processed to derive NDVI as following: 7

NDVI = NIR−RNIR+R (1) 8

Where; NIR corresponds to bands 3 and 4 in ETM+ and OLI data, respectively, and R corresponds to 9

bands 4 and 5 in ETM+ and OLI data, respectively. Calculations excluded the cloudy pixels that were 10

separated from the data using the cloud mask layer (Zhu and Woodcock, 2012). 11

In order to compare the impact of spatial resolution on NDVI, the images from Landsat and MODIS 12

data were clipped to the borders of the study area. Different comparisons were made between the 13

Landsat and MODIS NDVI images. These included the ranges of NDVI and their distribution in the 14

study area, the degree of correlation between NDVI and MODIS, the NDVI deviation from the 2000-15

2014 mean for both Landsat and MODIS data and the degree of correlation for both NDVI datasets with 16

soil moisture and modeled evapotranspiration. The current NDVI image was taken for year 2014, while 17

the average NDVI image was derived from the 2000-2015 NDVI images. 18

3.3.2 MSI 19

This simple index was calculated by dividing the MIR band by the NIR band. The MSI is believed to be 20

sensitive to increasing leaf water content (Sow et al., 2013). As the water content of leaves in vegetation 21

canopies increases, the strength of the absorption around the MIR wavelength increases and spectral 22

reflectance in this band decreases. The values of this index range from 0 to more than 3. The common 23

range for green vegetation is 0.4 to 2. The index was calculated by dividing MODIS band 6 (MIR) by 24

band 4 (NIR). 25

3.3.3 EWSI 26

The evapotranspiration water stress indicator (EWSI) is defined as follows (Allen et al., 1998): 27

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 = 1 − (𝐸𝐸𝐸𝐸𝑎𝑎𝐸𝐸𝐸𝐸𝑐𝑐

) (2) 28

7

3.3 Data processing to derive drought indices 1

The data of MODIS were downloaded and transformed into different indices that included MSI, PDI, 2

and MPDI. The data of Landsat ETM+ was used to derive NDVI, after correcting striped lines in images 3

acquired after year 2003 using the linear interpolation. The Landsat OLI data were used to derive NDVI 4

and EWSI. The following subsections summarize the steps used to derive these indices. 5

3.3.1 NDVI 6

Remote sensing data of Landsat were processed to derive NDVI as following: 7

NDVI = NIR−RNIR+R (1) 8

Where; NIR corresponds to bands 3 and 4 in ETM+ and OLI data, respectively, and R corresponds to 9

bands 4 and 5 in ETM+ and OLI data, respectively. Calculations excluded the cloudy pixels that were 10

separated from the data using the cloud mask layer (Zhu and Woodcock, 2012). 11

In order to compare the impact of spatial resolution on NDVI, the images from Landsat and MODIS 12

data were clipped to the borders of the study area. Different comparisons were made between the 13

Landsat and MODIS NDVI images. These included the ranges of NDVI and their distribution in the 14

study area, the degree of correlation between NDVI and MODIS, the NDVI deviation from the 2000-15

2014 mean for both Landsat and MODIS data and the degree of correlation for both NDVI datasets with 16

soil moisture and modeled evapotranspiration. The current NDVI image was taken for year 2014, while 17

the average NDVI image was derived from the 2000-2015 NDVI images. 18

3.3.2 MSI 19

This simple index was calculated by dividing the MIR band by the NIR band. The MSI is believed to be 20

sensitive to increasing leaf water content (Sow et al., 2013). As the water content of leaves in vegetation 21

canopies increases, the strength of the absorption around the MIR wavelength increases and spectral 22

reflectance in this band decreases. The values of this index range from 0 to more than 3. The common 23

range for green vegetation is 0.4 to 2. The index was calculated by dividing MODIS band 6 (MIR) by 24

band 4 (NIR). 25

3.3.3 EWSI 26

The evapotranspiration water stress indicator (EWSI) is defined as follows (Allen et al., 1998): 27

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 = 1 − (𝐸𝐸𝐸𝐸𝑎𝑎𝐸𝐸𝐸𝐸𝑐𝑐

) (2) 28

Where; NIR corresponds to bands 3 and 4 in ETM+ and OLI data, respectively, and R corresponds to bands 4 and 5 in ETM+ and OLI data, respectively. Calculations excluded the cloudy pixels that were separated from the data using the

cloud mask layer (Zhu and Woodcock, 2012).

In order to compare the impact of spatial resolution on NDVI, the images from Landsat and MODIS data were clipped to the borders of the study area. Different comparisons were made between the Landsat and MODIS NDVI images. These included the ranges of NDVI and their distribution in the study area, the degree of correlation between NDVI and MODIS, the NDVI deviation from the 2000-2014 mean for both Landsat and MODIS data and the degree of correlation for both NDVI datasets with soil moisture and modeled evapotranspiration. The current NDVI image was taken for year 2014, while the average NDVI image was derived from the 2000-2015 NDVI images.

3.3.2 MSI

This simple index was calculated by dividing the MIR band by the NIR band. The MSI is believed to be sensitive to increasing leaf water content (Sow et al., 2013). As the water content of leaves in vegetation canopies increases, the strength of the absorption around the MIR wavelength increases and spectral reflectance in this band decreases. The values of this index range from 0 to more than 3. The common range for green vegetation is 0.4 to 2. The index was calculated by dividing MODIS band 6 (MIR) by band 4 (NIR).

3.3.3 EWSI

The evapotranspiration water stress indicator (EWSI) is defined as follows (Allen et al., 1998):

Where ETa is actual daily evapotranspiration and ETc is the crop daily evapotranspiration.

The EWSI values range between 0 (no water stress) to 1 (severe water stress). The standard method of FAO 56 (Allen et al., 1998) was used to calculate ETc using daily weather


5

records of MWI to calculate grass reference evapotranspiration (ETo) and theoretical crop coefficient (Kc) for the different crops. The “Surface Energy Balance Algorithms for Land or SEBAL” model was used to compute ETa using meteorological and remote sensing data (Bastiaanssen et al., 1998a&b). The model includes many steps for calculating the variables needed for the energy budget components. Following a sequence of steps, the residual energy from the energy budget was used to calculate instantaneous and daily ETa. SEBAL model was tested and calibrated in the same study area in Jordan (Shawash, 2015), with all calculation steps being stored as models within an image processing software that was used in this study.

The map of EWSI was derived by dividing the map of ETa, produced from SEBAL, by the map of ETc and subtracting the product of division from 1. The map of ETc was derived by multiplying the maps of Kc and ETo. The map of Kc was generated from a Land use/cover map, while ETo map was derived from interpolating the ETo from four weather stations. The spline interpolation technique was applied for the spatial presentation of data to a resolution of 250m. The method was recommended for spatial interpolation of ETo from few observation across a range of Mediterranean climates (Todorovic et al., 2013). The range of ETo that was interpolated on the form of a spatial map was 4.2-4.6 mm. The map of EWSI was classified based on the range of this index, where drought was assumed to occur when EWSI exceeded 0.50 (Suleiman and Al-Bakri, 2011).

3.3.4 PDI

This drought index considers both of vegetation and soil in assessing water stress (Ghulam et al., 2007a; Shahabfar et al., 2012). The PDI was calculated as follows:

8

Where ETa is actual daily evapotranspiration and ETc is the crop daily evapotranspiration. 1

The EWSI values range between 0 (no water stress) to 1 (severe water stress). The standard method of 2

FAO 56 (Allen et al., 1998) was used to calculate ETc using daily weather records of MWI to calculate 3

grass reference evapotranspiration (ETo) and theoretical crop coefficient (Kc) for the different crops. 4

The “Surface Energy Balance Algorithms for Land or SEBAL” model was used to compute ETa using 5

meteorological and remote sensing data (Bastiaanssen et al., 1998a&b). The model includes many steps 6

for calculating the variables needed for the energy budget components. Following a sequence of steps, 7

the residual energy from the energy budget was used to calculate instantaneous and daily ETa. SEBAL 8

model was tested and calibrated in the same study area in Jordan (Shawash, 2015), with all calculation 9

steps being stored as models within an image processing software that was used in this study. 10

The map of EWSI was derived by dividing the map of ETa, produced from SEBAL, by the map of ETc 11

and subtracting the product of division from 1. The map of ETc was derived by multiplying the maps of 12

Kc and ETo. The map of Kc was generated from a Land use/cover map, while ETo map was derived 13

from interpolating the ETo from four weather stations. The spline interpolation technique was applied 14

for the spatial presentation of data to a resolution of 250m. The method was recommended for spatial 15

interpolation of ETo from few observation across a range of Mediterranean climates (Todorovic et al., 16

2013). The range of ETo that was interpolated on the form of a spatial map was 4.2-4.6 mm. The map of 17

EWSI was classified based on the range of this index, where drought was assumed to occur when EWSI 18

exceeded 0.50 (Suleiman and Al-Bakri, 2011). 19

3.3.4 PDI 20

This drought index considers both of vegetation and soil in assessing water stress (Ghulam et al., 2007a; 21

Shahabfar et al., 2012). The PDI was calculated as follows: 22

PDI = 1√M2+1 (Rred + M. RNIR) (3) 23

Where, Rred and RNIR are the atmospherically corrected surface reflectance of Red and Near Infrared 24

bands of remotely sensed data, respectively, and M represents the slope of the soil line in the NIR-Red 25

spectral feature space. The red and NIR bands correspond to MODIS bands 3 and 4, respectively, in the 26

MOD13Q1 file. The value of M was derived the plot of Red vs. NIR reflectance, where the equation 27

from this plot is linear with zero intercept (RNIR =M*Rred+0). The value of M (slope of soil line) for 28

Mafraq was 1.3, which was close to the value (1.4) reported by Ghulam et al. (2007a) and Shahabfar et 29

al. (2012). PDI values vary between 0.0 (severe water stress) and 1.0 (less water stress or wet surface). 30

8

Where ETa is actual daily evapotranspiration and ETc is the crop daily evapotranspiration. 1

The EWSI values range between 0 (no water stress) to 1 (severe water stress). The standard method of 2

FAO 56 (Allen et al., 1998) was used to calculate ETc using daily weather records of MWI to calculate 3

grass reference evapotranspiration (ETo) and theoretical crop coefficient (Kc) for the different crops. 4

The “Surface Energy Balance Algorithms for Land or SEBAL” model was used to compute ETa using 5

meteorological and remote sensing data (Bastiaanssen et al., 1998a&b). The model includes many steps 6

for calculating the variables needed for the energy budget components. Following a sequence of steps, 7

the residual energy from the energy budget was used to calculate instantaneous and daily ETa. SEBAL 8

model was tested and calibrated in the same study area in Jordan (Shawash, 2015), with all calculation 9

steps being stored as models within an image processing software that was used in this study. 10

The map of EWSI was derived by dividing the map of ETa, produced from SEBAL, by the map of ETc 11

and subtracting the product of division from 1. The map of ETc was derived by multiplying the maps of 12

Kc and ETo. The map of Kc was generated from a Land use/cover map, while ETo map was derived 13

from interpolating the ETo from four weather stations. The spline interpolation technique was applied 14

for the spatial presentation of data to a resolution of 250m. The method was recommended for spatial 15

interpolation of ETo from few observation across a range of Mediterranean climates (Todorovic et al., 16

2013). The range of ETo that was interpolated on the form of a spatial map was 4.2-4.6 mm. The map of 17

EWSI was classified based on the range of this index, where drought was assumed to occur when EWSI 18

exceeded 0.50 (Suleiman and Al-Bakri, 2011). 19

3.3.4 PDI 20

This drought index considers both of vegetation and soil in assessing water stress (Ghulam et al., 2007a; 21

Shahabfar et al., 2012). The PDI was calculated as follows: 22

PDI = 1√M2+1 (Rred + M. RNIR) (3) 23

Where, Rred and RNIR are the atmospherically corrected surface reflectance of Red and Near Infrared 24

bands of remotely sensed data, respectively, and M represents the slope of the soil line in the NIR-Red 25

spectral feature space. The red and NIR bands correspond to MODIS bands 3 and 4, respectively, in the 26

MOD13Q1 file. The value of M was derived the plot of Red vs. NIR reflectance, where the equation 27

from this plot is linear with zero intercept (RNIR =M*Rred+0). The value of M (slope of soil line) for 28

Mafraq was 1.3, which was close to the value (1.4) reported by Ghulam et al. (2007a) and Shahabfar et 29

al. (2012). PDI values vary between 0.0 (severe water stress) and 1.0 (less water stress or wet surface). 30

Where, Rred and RNIR are the atmospherically corrected surface reflectance of Red and Near Infrared bands of remotely sensed data, respectively, and M represents the slope of the soil line in the NIR-Red spectral feature space. The red and NIR bands correspond to MODIS bands 3 and 4, respectively, in the MOD13Q1 file. The value of M was derived from the plot of Red vs. NIR reflectance, where the equation from this plot is linear with zero intercept (RNIR =M*Rred+0). The value of M (slope of soil line) for Mafraq was 1.3, which was close to the value (1.4) reported by Ghulam et al. (2007a) and Shahabfar et al. (2012). PDI values vary between 0.0 (severe water stress) and 1.0 (less water stress or wet surface). The suggested ranges for classifying drought severity using PDI are the normal (0.0-0.3), moderate (0.3-0.5) and the severe (>0.5).

3.3.5 MPDI

Unlike PDI, which assumes homogenous land cover and soil

types, MPDI considers vegetation fraction and soil moisture content. Ghulam et al. (2007b) introduced the term vegetation fraction (fv) into the PDI to separate the effects of vegetation on the index. Similar to the PDI, the MPDI was derived using the Red and NIR reflectance, as following:

9

The suggested ranges for classifying drought severity using PDI are the normal (0.0-0.3), moderate (0.3-1

0.5) and the severe (>0.5). 2

3.3.5 MPDI 3

Unlike PDI, which assumes homogenous land cover and soil types, MPDI considers vegetation fraction 4

and soil moisture content. Ghulam et al. (2007b) introduced the term vegetation fraction (fv) into the PDI 5

to separate the effects of vegetation on the index. Similar to the PDI, the MPDI was derived using the 6

Red and NIR reflectance, as following: 7

𝑀𝑀𝑃𝑃𝑃𝑃𝑃𝑃 = 𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟+𝑀𝑀.𝑅𝑅𝑁𝑁𝑁𝑁𝑁𝑁−𝑓𝑓𝑣𝑣(𝑅𝑅𝑣𝑣,𝑟𝑟𝑟𝑟𝑟𝑟+𝑀𝑀.𝑅𝑅𝑣𝑣,𝑁𝑁𝑁𝑁𝑁𝑁)(1−𝑓𝑓𝑣𝑣)√𝑀𝑀2+1 (4) 8

Where fv is defined as the fraction of ground surface covered by vegetation (Baret et al., 1995). Rv,red and 9

Rv,NIR are coefficients that represent the pure vegetation reflectance in the Red and NIR bands, 10

respectively. In this study, Rv,red and Rv,NIR were 0.10 and 0.4, respectively. The fraction of vegetation 11

was derived from the NDVI using the following transformation (Baret et al., 1995): 12

𝑓𝑓𝑣𝑣 = 1 − ( 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑚𝑚𝑚𝑚𝑚𝑚−𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑚𝑚𝑚𝑚𝑚𝑚− 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑚𝑚𝑚𝑚𝑚𝑚

)0.6175 (5) 13

Applying the equation on the MODIS image showed that fv was in the range of 0.00 to 0.81. 14

3.3.5 Analysis of maps 15

In order to compare the two dataset of Landsat and MODIS, the image of NDVI derived from Landsat 16

was decimated to the resolution of MODIS using the bilinear interpolation method. Decimation was also 17

carried out for the image of ETa produced from SEBAL model and the EWSI map. Following these 18

steps, the degree of correlation between MODIS and Landsat NDVI was investigated to assess the 19

improvements that might result from the use of medium resolution data. The MSI, PDI, MPDI and 20

EWSI were compared with NDVI, in terms of spatial distribution of drought and in terms of correlation 21

among the different indices and between each drought index, soil moisture collected from different 22

locations and ETa from SEBAL model. The comparisons were made for MODIS and Landsat data of 23

March 2014, the date which coincided with ground data collection and in which both images were close 24

in terms of acquisition time. 25

26

9



3.3.5 MPDI 3











)0.6175 (5) 13













26

9



3.3.5 MPDI 3











)0.6175 (5) 13













26

9



3.3.5 MPDI 3











)0.6175 (5) 13













26

Where fv is defined as the fraction of ground surface covered by vegetation (Baret et al., 1995). Rv,red and Rv,NIR are coefficients that represent the pure vegetation reflectance in the Red and NIR bands, respectively. In this study, Rv,red and Rv,NIR were 0.10 and 0.4, respectively. The fraction of vegetation was derived from the NDVI using the following transformation (Baret et al., 1995):

Applying the equation on the MODIS image showed that fv was in the range of 0.00 to 0.81.

3.3.5 Analysis of maps

In order to compare the two dataset of Landsat and MODIS, the image of NDVI derived from Landsat was decimated to the resolution of MODIS using the bilinear interpolation method. Decimation was also carried out for the image of ETa produced from SEBAL model and the EWSI map. Following these steps, the degree of correlation between MODIS and Landsat NDVI was investigated to assess the improvements that might result from the use of medium resolution data. The MSI, PDI, MPDI and EWSI were compared with NDVI, in terms of spatial distribution of drought and in terms of correlation among the different indices and between each drought index, soil moisture collected from different locations and ETa from SEBAL model. The comparisons were made for MODIS and Landsat data of March 2014, the date which coincided with ground data collection and in which both images were close in terms of acquisition time.

4. Results and Discussion

4.1. NDVI of MODIS and Landsat

Results showed that NDVI values for MODIS image were in the range of 0.017 to 0.581, with a mean value of 0.130. For the image of Landsat, the NDVI ranged from 0.000 to 0.920, with a mean value of 0.13. A significant correlation between Landsat and MODIS NDVI was observed for the whole study area; with a coefficient of determination (R2) that reached 0.56 (Figure 2). The root-mean-square error (RMSE) for this relationship was 0.078. The relatively low R2 value could be attributed to the aridity of Mafraq, where more than 71% of this study area was an open rangeland; a land use that eventually lead to low vegetation cover and low NDVI values. Both datasets showed that more than 90% of the


6

study area had NDVI values of less 0.20 in the period of peak vegetation growth, which reflected the aridity and drought condition in the study area. The image of Landsat-NDVI revealed that 77% of the area had an NDVI value of 0.10 or less. Such values would characterize non-vegetated or sparsely vegetated areas (Rababa’a and Al-Bakri, 2006; Kumar et al., 2012; Srinias et al., 2012; Saba, 2015). For rainfed areas of barley, a weak correlation (R2=0.09) was observed between MODIS and Landsat data. For these areas, the Landsat-NDVI ranged between 0.143 and 0.541, with an average value of 0.225. For the MODIS data, rainfed areas had an NDVI range of 0.109 to 0.374 with an average value of 0.199.

For further comparisons, both NDVI images were reclassified to calculate the percent of each range of NDVI (Table 2). Results showed that 77% of the Landsat-NDVI values were in the range of 0.00 to 0.10, while 68% of the MODIS-NDVI were in the range of 0.10 to 0.20. Due to its higher spatial resolution, the Landsat-NDVI was also better in detecting NDVI values above 0.40 (Figure 3). The total area with NDVI values that exceeded 0.40 was 4.5% in the Landsat image and 0.24% in the MODIS image. Another impact for the spatial resolution was the higher values of MODIS-NDVI when compared with Landsat-NDVI for NDVI range of 0.10-0.30. Therefore, Landsat was better in detecting non-vegetated and sparsely vegetated areas where NDVI was less than 0.10.

Maps of NDVI from MODIS and Landsat showed that the

Figure 2. Relationship between MODIS-NDVI and Landsat-NDVI.

general spatial distribution of NDVI is similar in terms of low and high NDVI values (Figure 3). However, values and ranges of low and high NDVI from both data were different. Generally, distribution of areas with high and extremely low NDVI values was better for the Landsat-NDVI than for MODIS-NDVI (Figure 3). With the exception of very large irrigated farms and rainfed plots, it is expected that MODIS-NDVI pixel very rarely covers a single homogeneous rainfed agricultural region, even during peak growth of vegetation. This can be attributed to the small landholding size and the mixture of land covers that may exist in the 250-m pixel of MODIS. Therefore, MODIS-NDVI can be considered as a general indicator of the overall condition of the vegetation in an area, including natural vegetation and agricultural fields, while high spatial resolution data of Landsat can provide more detailed information on conditions of rainfed crops and rangelands.

In terms of maps agreement, Landsat-NDVI was less than MODIS-NDVI for rainfed areas, while an opposite trend was observed for irrigated areas (Yellow, orange brown and pink colors on Figure 3). The agreement between both indices for highly vegetated areas (Pink colors on Figure 3) would be expected, as indicated by Ke et al. (2015), who reported a deviation between both within a range of 0.05-0.11. In Mafraq study area, the prevailed arid conditions decreased the correlation between both datasets. Therefore, the agreement between MODIS and Landsat-NDVI in rainfed areas occurred in the NDVI range of 0.10 to 0.30 (Blue color in Figure 4). For these areas, Landsat-NDVI was

11

image. Another impact for the spatial resolution was the higher values of MODIS-NDVI when 1

compared with Landsat-NDVI for NDVI range of 0.10-0.30. Therefore, Landsat was better in detecting 2

non-vegetated and sparsely vegetated areas where NDVI was less than 0.10. 3

Table 2: NDVI ranges for MODIS and Landsat images during the last week of March 2014. 4

NDVI range % in MODIS % in Landsat 0.00 – 0.10 21.53 77.24 0.10 – 0.20 67.50 13.29 0.20 – 0.30 9.20 4.93 0.30 – 0.40 1.54 2.30 0.40 – 0.50 0.21 1.11 0.50 – 0.60 0.03 0.60 0.60 – 1.00 0.00 0.52

5 Maps of NDVI from MODIS and Landsat showed that the general spatial distribution of NDVI is similar 6

in terms of low and high NDVI values (Figure 3). However, values and ranges of low and high NDVI 7

from both data were different. Generally, distribution of areas with high and extremely low NDVI values 8

was better for the Landsat-NDVI than for MODIS-NDVI (Figure 3). With the exception of very large 9

irrigated farms and rainfed plots, it is expected that MODIS-NDVI pixel very rarely covers a single 10

homogeneous rainfed agricultural region, even during peak growth of vegetation. This can be attributed 11

to the small landholding size and the mixture of land covers that may exist in the 250-m pixel of 12

MODIS. Therefore, MODIS-NDVI can be considered as a general indicator of the overall condition of 13

the vegetation in an area, including natural vegetation and agricultural fields, while high spatial 14

resolution data of Landsat can provide more detailed information on conditions of rainfed crops and 15

rangelands. 16

In terms of maps agreement, Landsat-NDVI was less than MODIS-NDVI for rainfed areas, while an 17

opposite trend was observed for irrigated areas (Yellow, orange brown and pink colors on Figure 3). The 18

agreement between both indices for highly vegetated areas (Pink colors on Figure 3) would be expected, 19

as indicated by Ke et al. (2015), who reported a deviation between both within a range of 0.05-0.11. In 20

Mafraq study area, the prevailed arid conditions decreased the correlation between both datasets. 21

Therefore, the agreement between MODIS and Landsat-NDVI in rainfed areas occurred in the NDVI 22

range of 0.10 to 0.30 (Blue color in Figure 4). For these areas, Landsat-NDVI was less than MODIS-23

NDVI by one range (an NDVI range of 0.10). 24

Table 2. NDVI ranges for MODIS and Landsat images during the last week of March 2014.


7

less than MODIS-NDVI by one range (an NDVI range of 0.10).

Since the current drought monitoring in Jordan is based on the degree of NDVI deviations from the historical mean, a comparison between MODIS and Landsat NDVI deviations from the 2000-2014 means was carried out. Maps of spatial distribution of NDVI deviations from their means are shown in Figure 5, while analysis of ranges of deviation is included in Table 3. The ranges in this table are similar to those used by the drought monitoring unit at NCARE (Saba, 2015). Analysis of MODIS-NDVI indicated that most of the area was above the average by 0.11-0.15 NDVI. The data of Landsat showed that 67% of the area had NDVI above the average by only 0.06-0.10. The data of MODIS showed that NDVI was not below average for this particular period, while Landsat data showed that 15% of the area was below

Figure 3. Map of MODIS-NDVI (Top) and Landsat-NDVI (Bottom) for the last week of March 2014.

average. In both datasets, the NDVI increase of more than 0.16 (shown in black in Figure 5), could be attributed to the expansion in irrigated areas and the development of tree crops with time. The coarse resolution of MODIS resulted in more areas with increased NDVI when compared with Landsat data.

It can be said that Landsat data detected drought conditions in the northern parts of the study area, where rainfed barley was cultivated, while MODIS data detected no drought in the same area. This could be attributed to the small landholding size that characterized the study area. Previous studies (Millington et al., 1999; Al-Bakri et al., 2003) indicated that landholding size in Jordan and the study area, known as part of the northern Badia of Jordan, was decreasing with time. Results also implied that NDVI-deviation method was able to improve drought detection for areas where


8

Figure 4. Differences between MODIS and Landsat NDVI (Top) and the distribution of agricultural areas (Bottom).

14

NDVI was not below average for this particular period, while Landsat data showed that 15% of the area 1

was below average. In both data sets, the NDVI increase of more than 0.16 (shown in black in Figure 5), 2

could be attributed to the expansion in irrigated areas and the development of tree crops with time. The 3

coarse resolution of MODIS resulted in more areas with increased NDVI when compared with Landsat 4

data. 5

Table 3: Summary of the NDVI deviation for March 2014 from the 2000-2015 average for the same 6 period for both MODIS and Landsat 8 images. 7

NDVI deviation % in MODIS-NDVI % in Landsat-NDVI < -0.10 0.0 6.4 -0.09 - -0.05 0.0 1.8 -0.04 - -0.02 0.0 2.9 -0.01 - 0.02 0.0 3.6 0.03 - 0.05 0.0 9.7 0.06 - 0.10 27.2 67.2 0.11 - 0.15 56.3 5.5 0.16 - 0.20 16.5 2.9

It can be said that Landsat data detected drought conditions in the northern parts of the study area, where 8

rainfed barley was cultivated, while MODIS data detected no drought in the same area. This could be 9

attributed to the small landholding size that characterized the study area. Previous studies (Millington et 10

al., 1999; Al-Bakri et al., 2003) indicated that landholding size in Jordan and the study area, known as 11

part of the northern Badia of Jordan, was decreasing with time. Results also implied that NDVI-12

deviation method was able to improve drought detection for areas where deviations were between 0.05 13

and 0.10. For the image of March 2014, these areas represented 24% of Mafraq. In terms of NDVI 14

deviations agreement, both of MODIS and Landsat NDVI showed high agreement in NDVI deviation 15

that was in the range of 0.06-0.15. The percent of the areas that were characterized by this range was 16

84% for MODIS 73% for Landsat. Therefore, such results would indicate that Landsat data would more 17

accurate for monitoring drought in this arid area and in similar ones. The main limitation at present is the 18

temporal resolution (16 day) of Landsat 8, which may not capture vegetation dynamics and drought 19

conditions during critical periods when coverage is not available. 20

Table 3. Summary of the NDVI deviation for March 2014 from the 2000-2015 average for the same period for both MODIS and Landsat 8 images

deviations were between 0.05 and 0.10. For the image of March 2014, these areas represented 24% of Mafraq. In terms of NDVI deviations agreement, both of MODIS and Landsat NDVI showed high agreement in NDVI deviation that was in the range of 0.06-0.15. The percent of the areas that were characterized by this range was 84% for MODIS 73% for Landsat. Therefore, such results would indicate that Landsat data would be more accurate for monitoring drought in this arid area and in similar ones. The main limitation at present is the temporal resolution (16 day) of Landsat 8,

which may not capture vegetation dynamics and drought conditions during critical periods when coverage is not available.

4.2. MSI and EWSI

Water stress indices of MODIS-MSI and EWSI showed different spatial distribution and severity of drought (Figure 6). The MSI map showed that no water stress was observed in irrigated areas and in rainfed areas in the middle north of


9

the study area, while remaining parts of the study area were characterized by slight drought conditions. The EWSI map, on the other hand, showed more wet conditions in the middle of the study area and severe droughts in the east and in southwest. Both indices showed different distribution of drought when compared with NDVI deviation map (Figure 5). The severe drought conditions indicated by EWSI could be attributed to the low ETa in the dry areas in the southwest and the area covered by basalt rocks in the east. The unexpected severe drought conditions in some irrigated farms were mainly attributed to the nature of EWSI rather than to existing water stress. This could be explained by the errors in estimating ETc and resulting from the use of theoretical values of Kc. As indicated by a previous study (Al-Bakri et al., 2016b), the variable crop calendar and the length of growing season could be also contributing to the inconsistent spatial distribution of drought when EWSI was used. For rainfed barley and rangelands, no data was available for Kc, and therefore, the calculated EWSI was based on a single value of Kc which could result in inaccurate maps of drought severity when this index was used to map drought severity.

4.3. PDI and MPDI

Maps of PDI and MPDI showed that most of agricultural

Figure 5. Deviation of NDVI for the last week of March 2014 from the 2000-2015 average for the images of Landsat (Top) and MODIS (Bottom).

fields were located in the moderately stressed areas located in the west of the study area (Figure 7). This could be attributed to the low fraction of vegetation in these areas. MPDI showed more severe drought conditions than the PDI, although the trend of both was similar. Compared with NDVI deviation (Figure 5), both of PDI and MPDI had different trends of drought than the NDVI deviation method. The PDI and MPDI showed moderate drought for most of the study area, while NDVI indicated better condition in the same period. When compared with MSI and EWSI, a moderate drought was indicated by PDI and MPDI in the middle north of the study area.

4.4. Correlations Among Drought Indices

Results showed that both of Landsat and MODIS NDVI had similar trends in terms of their correlation with other indices (Table 4). The Landsat-NDVI had better correlation with SEBAL-ETa than other indices, while PDI had the highest correlation with soil water content. The relatively high correlation between Landsat NDVI and SEBAL-ETa would be attributed to the fact that SEBAL model was based on Landsat data. The correlation between PDI and MPDI was very high (R2 = 0.97), indicating that none of the both was performing better than the other. This could be attributed to the arid conditions prevailing in the study area. These


10

Figure 6. Map of MSI (Top) and EWSI (Bottom) for the last week of March 2014.

findings were also indicated by Shahabfar et al. (2012) who concluded that PDI would be more convenient to use than MPDI to monitor drought in arid and semiarid areas, as its calculations would not require an additional step for calculating vegetation fraction.

Both of PDI and NDVI had significant relationships with soil water content. Comparing PDI with NDVI for the same spatial resolution (MODIS), then PDI would be seen as a

Figure 7. Maps of PDI (Top) and MPDI (Bottom) for the last week of March 2014.

18

Figure 7: Maps of PDI (Top) and MPDI (Bottom) for the last week of March 2014. 4.4. Correlations among drought indices 1

Results showed that both of Landsat and MODIS NDVI had similar trends in terms of their correlation 2

with other indices (Table 4). The Landsat-NDVI had better correlation with SEBAL-ETa than other 3

indices, while PDI had the highest correlation with soil water content. The relatively high correlation 4

between Landsat NDVI and SEBAL-ETa would be attributed to the fact that SEBAL model was based 5

on Landsat data. The correlation between PDI and MPDI was very high (R2 = 0.97), indicating that none 6

of the both was performing better than the other. This could be attributed to the arid conditions 7

prevailing in the study area. These findings were also indicated by Shahabfar et al. (2012) who 8

concluded that PDI would be more convenient to use than MPDI to monitor drought in arid and semiarid 9

areas, as its calculations would not require an additional step for calculating vegetation fraction . 10

Table 4: Coefficient of determination(R2) among the different drought indices with SEBAL-ETa and 11 measured soil water content. 12

Landsat-NDVI MODIS-NDVI MSI EWSI PDI MPDI

Landsat-NDVI -- 0.56 0.11 0.017 ns 0.21 0.16

MODIS-NDVI 0.56 -- 0.26 0.011 ns 0.28 0.18

MSI 0.11 0.26 -- <0.01 ns <0.01 ns <0.07 ns

EWSI 0.017 ns 0.011 ns <0.01 ns -- 0.14 0.14

PDI 0.21 0.28 <0.01 ns 0.14 -- 0.97

MPDI 0.16 0.18 <0.01 ns 0.14 0.97 --

SEBAL-ETa 0.69 0.41 <0.07 ns 0.29 0.38 0.33

Soil water content 0.58 0.63 0.35 0.18 0.72 0.53

ns: no significant relationship at P<0.05. 13 14

Both of PDI and NDVI had significant relationships with soil water content. Comparing PDI with NDVI 15

for the same spatial resolution (MODIS), then PDI would be seen as a better index for drought 16

monitoring than NDVI (Figure 8), as its correlation with soil water content was more than that for NDVI 17

(Figure 8). Therefore, these results would suggest the use of PDI for drought monitoring than other 18

indices for the same spatial resolution. 19

20

Table 4. Coefficient of determination(R2) among the different drought indices with SEBAL-ETa and measured soil water content.

better index for drought monitoring than NDVI (Figure 8), as its correlation with soil water content was more than that for NDVI (Figure 8). Therefore, these results would suggest the use of PDI for drought monitoring than other indices for the same spatial resolution.

5. Conclusions and Recommendations

Results showed that MODIS and Landsat NDVI were


11

different in terms of spatial distribution, although both were significantly correlated over the whole study area.. When compared with MODIS, the use of Landsat-NDVI data would improve drought mapping as it would accurately detect deviations from the historically NDVI-mean. This was obvious in the study area, as the small land holding size made it difficult for MODIS coarse spatial resolution to detect drought in small rainfed fields. Since the temporal resolution of Landsat 8 is 16-day and may not capture vegetation changes during critical periods of drought, it would be recommended to utilize both datasets of Landsat 8 and MODIS. The synergy between both shall be investigated and utilized for drought monitoring; with a shift towards the use of PDI which showed more correlation with soil water content. In the future, Landsat 8 and similar medium resolution data shall be considered and utilized for detecting drought and for producing periodic drought maps. The process will be encouraged by the availability of more medium resolution data. At present, the use of PDI as a single drought index, or as a component of a combined drought index, is recommended when a single image of MODIS or Landsat is to be used for deriving a map for spatial distribution of drought severity without processing historical data. For comparison of drought severity among seasons, the method of NDVI deviation can be used with Landsat data for small geographic area and with MODIS for large geographic area or the country level.

Acknowledgment

This publication was supported by Ministry of Water and Irrigation (MWI) and the University of Jordan, Amman, Jordan. The work was carried out through a project “Regional Coordination on Improved Water Resources Management and Capacity Building Project” funded by the Global Environment Facility (GEF), managed by the World Bank (Project ID P117170), and jointly implemented in

Figure 8. Relationship between soil water content and MODIS-PDI (Left) and MODIS-NDVI (Right).

Cooperation with NASA. The authors acknowledge the cooperation of MWI and its staff, particularly Engineers Ali Subh, Su’ad Nasser, Rania Abdelkhaliq, Ali Hayjneh and Ali Ghanim.

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