Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

ARTICLE IN PRESSG ModelJAG 379 1–9

International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx

Contents lists available at ScienceDirect

International Journal of Applied Earth Observation andGeoinformation

journa l homepage: www.e lsev ier .com/ locate / jag

Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+images

1

2

3

Zhiqiang Gaoa,b,∗, Wei Gaob, Ni-Bin Changc4

a Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China5b USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA6c Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA7

8

a r t i c l e i n f o9

10

Article history:11

Received 20 May 201012

Accepted 25 October 201013

14

Keywords:15

Drought assessment16

Remote sensing17

Urbanization effect18

Urban heat island19

Coastal management20

a b s t r a c t

This paper presents a new drought assessment method by spatially and temporally integrating temper- Q2ature vegetation dryness index (TVDI) with regional water stress index (RWSI) based on a synergisticapproach. With the aid of LANDSAT TM/ETM data, we were able to retrieve the land-use and land-cover(LULC), vegetation indices (VIs), and land surface temperature (LST), leading to the derivation of threetypes of modified TVDI, including TVDI SAVI, TVDI ANDVI and TVDI MSAVI, for drought assessment ina fast growing coastal area, Northern China. The categorical classification of four drought impact levelsassociated with the RWSI values enables us to refine the spatiotemporal relationship between the LSTand the VIs. Holistic drought impact assessment between 1987 and 2000 was carried out by linking RWSIwith TVDIs group wise. Research findings indicate that: (1) LST and VIs were negatively correlated inmost cases of low, medium, and high vegetation cover except the case of high density vegetation coverin 2000 due to the effect of urban heat island (UHI) effect; (2) the shortage of water in 1987 was moresalient than that that in 2000 based on all indices of TVDI and RWSI; and (3) TVDIs are more suitable formonitoring mild drought, normal and wet conditions when RWSI is smaller than 0.752; but they are notsuitable for monitoring moderate and severe drought conditions.

© 2010 Published by Elsevier B.V.

1. Introduction21

Drought is a normal, recurrent feature of climate having a22

consequence of a reduction of precipitation and/or abnormal tem-23

perature over an extended period of time. In urban drought events,24

which is a temporary aberration, the drought might turn pastures25

brown, threaten shrubs and trees, and result in low vegetation26

cover and high land surface temperature (LST) simultaneously.27

Given that drought is a normal, recurrent feature of climate, it28

occurs in virtually all climatic regimes. Common indicators for29

drought assessment include ecological variables such as vegetation30

cover and evapotranspiration (ET), meteorological variables such31

as precipitation, as well as hydrological variables such as soil mois-32

ture, stream flow, ground water levels, reservoir and lake levels,33

and snow pack.34

The water stress index method is the ratio of the actual ET35

and potential ET which is a kind of crop water stress index.36

∗ Corresponding author at: USDA UV-B Monitoring and Research Program, NaturalResource Ecology Laboratory, Colorado State University, 419 Canyon Ave., Suite 226,Fort Collins, CO 80521, USA. Tel.: +1 970 491 3601.Q1

E-mail address: gaoland@gmail.com (Z. Gao).

With this concept, Jackson and Idso (1981) promoted the crop 37

water stress index (CWSI) and Moron et al. (1994) proposed water 38

deficit index (WDI). In addition, the moisture index method is an 39

approach for monitoring the regional drought with water char- 40

acteristics of strong absorption in shortwave infrared band (Xu, 41

2006; Fensholt and Sandholt, 2003; Chen et al., 2005). For example, 42

Kogan (1995) proposed the vegetation condition index (VCI), and 43

Mcffters (1996) proposed the normalized difference water index 44

(NDWI) by combining LANDSAT TM green and near-infrared bands. 45

Both of which are the moisture index method. The temperature 46

vegetation dryness index (TVDI) method based on the vegetation 47

index/temperature trapezoid eigenspace (VITT) (Sandholt et al., 48

2002) also belongs to the category of moisture index method. Ther- 49

mal inertia method is the approach using thermal infrared remote 50

sensing data to monitor soil moisture. Waston et al. (1971) firstly 51

proposed a simple model to calculate the thermal inertia with daily 52

difference of LST. Since then, many scientists carried out a vari- 53

ety of experimental studies with respect to the thermal inertia 54

principles (Price, 1977, 1985; England, 1990; Xue and Cracknell, 55

1995). 56

The spatial VITT has been applied widely in many studies reflect- 57

ing the potential impact of LST on NDVI. Moron et al. (1994) 58

explained the algorithm of crop water stress index (CWSI), which 59

0303-2434/$ – see front matter © 2010 Published by Elsevier B.V.doi:10.1016/j.jag.2010.10.005

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

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Fig. 1. The location of the study area in Shandong Province, China.

could avoid the measurements of leaf temperature when studying60

the situation of vegetation cover. The slope of scatter plot com-61

bined LST and VI represents degree of crop water stress gradient62

based on the negative relationship between LST and VI (Carlson63

et al., 1995; Moran et al., 1996; Fensholt and Sandholt, 2003;64

Venturini et al., 2004; Wang et al., 2007). Many studies moni-65

tored ET and soil moisture with spatial VITT to illuminate their66

correlation (Goward and Hope, 1989; Price, 1990; Ridd, 1995;Q367

Gillies and Carlson, 1995; Gillies et al., 1995, 1997; Sandholt et al.,68

2002; Wang et al., 2004; Han et al., 2006). These studies can69

help us evaluate the spatial and temporal variations of drought70

more accurately although each index has strengths and weaknesses71

which need to be clearly understood as they are integrated into72

drought early warning systems. Since the spatial and temporal73

patterns of vegetation dynamics could be associated with precip-74

itation changes and temperature fluctuations simultaneously, an75

implicit hypothesis of the current study is that integration of dif-76

ferent indices for drought assessment would be better than using77

a single one.78

To test the application and adaptation potential of TVDIs with79

the aid of a suite of remote sensing technologies, this study devel-80

ops a synergistic approach with respect to three TVDIs that were81

designed to combine temperature with four different vegetation82

indices (VIs) group wise. Yet it is believed that the soil-adjusted83

vegetation indices may be better coupled with TVDIs for meet-84

ing the study goal (Makkeasorn and Chang, 2009). To prove the85

concept, four vegetation indices were therefore included for com-86

parisons based on the temperature trapezoid eigenspace (VITT)87

(Sandholt et al., 2002). In addition, the regional water stress index88

(RWSI) designed based on the CWSI mechanism and SEBAL model89

was prepared as a reference basis for the refinements of TVDIs90

when monitoring the regional drought events (Bastiaanssen et al.,91

1998a,b). It is anticipated that the science question as to “how92

changes in these relevant factors may influence the impacts of93

drought episodes in vulnerability assessment?” can be examined94

and answered with this synergistic approach in a fast developing95

coastal region, Northern China.96

2. Methodologies97

2.1. The study area98

The study area is located at Laizhou Bay in Shandong Province,99

China (Fig. 1) within the latitude of 36◦48′43′′–37◦32′49′′ and longi-100

tude 118◦37′37′′–119◦44′31′′. The length along the east-west and of101

north-south directions is approximately 97 km and 79 km, respec-102

tively. The total study area is 486,245 ha. Land elevation drops 103

mildly from 30 m to 2 m above the sea level. Yet the length of 104

the meandering coastal line within the study area is about 400 km 105

long. Such coastal region is an active floodplain that was formed 106

by sediment laden water being released from the neighboring 107

river channel through the regional morphological and sedimen- 108

tary dynamics. Three cities, including the Shouguang City, part of 109

the Weifang City (e.g., the Hangting area), and most of the Changyi 110

City, are situated along this coastal line. The sediment distribution 111

in the alluvial plain ranges from fine sand (close to the low water 112

line) to the typical mud carried by flood currents. Close to the open 113

ocean, the climate system in this area is a moist, warm, temperate 114

continental monsoon climate (Cao, 2002; Wang et al., 2002; Guan 115

et al., 2001). 116

2.2. The satellite image processing 117

Fig. 2 delineates the flowchart of satellite image processing in 118

support of the case-based drought assessment. First of all, LANDSAT 119

TM/ETM+ images, digital elevation model (DEM) data, and climate 120

data were collected. All datasets were vectorized and interpolated 121

as grid datasets with UTM projection in advance to ease the appli- 122

cation in geographical information systems (GIS). 123

In this study, the raw images were geo-referenced to a common 124

UTM coordinate system, and we then re-sampled all of the images 125

to unify relative resolution in images of different sizes using the 126

nearest neighbor algorithm with a pixel size of 30 m × 30 m for all 127

bands, including the thermal band. 128

Following the streamlines in Fig. 2, LANDSAT TM/ETM+ images 129

were processed for the mapping of land use/land cover change 130

(LUCC), VIs, LST and heat fluxes. LUCC associated with May 7th 1987 131

and May 2nd 2000 in the study area was analyzed with respect 132

to the proper interpretation of LANDSAT TM/ETM images and was 133

validated with ground truth data. Regional scale heat fluxes were 134

estimated with the aid of remote sensing images and the surface 135

energy balance algorithm (e.g., SEBAL model) (Bastiaanssen et al., 136

1998a,b). 137

LST retrieval was carried out using the thermal bands of 138

TM/ETM+ data to ease the application of the radiance transfer 139

equation (Qin et al., 2001). The equations for normalized differ- 140

ence vegetation index (NDVI) (Rouse et al., 1973; Tucker, 1979), 141

soil adjusted vegetation index (SAVI) (Huete, 1988), modified SAVI 142

(MSAVI) (Qi et al., 1994) and adjusted normalized difference vege- 143

tation index (ANDVI) (Liu et al., 2008) were collectively employed 144

to produce a suite of VIs in support of advanced drought impact 145

assessment. All of the preparatory efforts led to develop the inte- 146

grated TDVI and RWSI for final analysis in the context of drought 147

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

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Fig. 2. The flowchart of image processing for exploring the relationships between LST and VIs and drought monitoring.

monitoring. The following subsections will introduce these algo-148

rithms/equations in a greater detail.149

2.3. Retrieval of the land surface heat fluxes and land surface150

temperature (LST)151

With LANDSAT satellite images, the heat fluxes were estimated152

using SEBAL model and calculated using Arc/Info 9.0 Macro Lan-153

guage (AML) and Compaq Visual FORTRAN 6.5 mixed-language pro-154

gramming in this study (Bastiaanssen et al., 1998a,b). Our SEBAL-155

based computer package can be operated in a Microsoft Windows156

system using the ESRI GRID module as the major data format. To157

ease the application of the radiance transfer equation, Qin et al.158

(2001) derived an approximate expression for LST retrieval suit-159

able for thermal bands of TM/ETM+ data. Our LST maps were also160

derived based on the same algorithm developed by Qin et al. (2001).

2.4. Calculations of the NDVI, ANDVI, MSAVI and SAVI 161

The equations for NDVI (Rouse et al., 1973; Tucker, 1979), SAVI 162

(Huete, 1988), MSAVI (Qi et al., 1994) and ANDVI (Liu et al., 2008) 163

are summarized as follows: 164

NDVI = �nir − �red

�nir + �red(1) 165

SAVI = �nir − �red

�nir + �red + L(1 + L) (2) 166

MSAVI = 12

× [(2�nir + 1) −√

(2�nir + 1)2 − 8(�nir − �red)] (3) 167

ANDVI = �nir − �red + (1 + L)(�green − �blue)�nir + �red + (1 + L)(�green + �blue)

(4) 168

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

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where �red is red band (0.63–0.69 �m) reflectance, �nir is near red169

band (0.76–0.90 �m) reflectance, �blue is blue band (0.45–0.52 �m)170

reflectance, �green is green band (0.52–0.60 �m) reflectance, L is171

adjustment factor, set to minimum background effects (L = 0.5).172

This study follows Eqs. (1)–(4) for the derivation of VIs. It is noted173

that the calculations of ANDVI, SAVI and MSAVI had included a few174

refinements. For instance, to resolve the barrier of vegetation index175

saturation issues (e.g., the index number indicates the amount of176

net rainfall that is required to reduce the index to zero, or saturation177

along the scale), Gitelson et al. (1996) introduced the green band to178

calculate VIs. In order to reduce the impact of soil background on179

VIs, Huete (1988) introduced the soil background adjustment fac-180

tor (L) to calculate VIs. The soil background adjustment factor (L)181

was actually applied in the algorithms of ANDVI, SAVI and MSAVI.182

Besides, the green and blue bands were used for the calculation183

of ANDVI while the green band was used for the calculation of184

MSAVI. As a consequence, these three VIs (ANDVI, SAVI and MSAVI)185

are quite different values as compared to the corresponding NDVI186

value.187

2.5. Calculations of the regional water stress index (RWSI)188

According to the CWSI mechanism (Jackson and Idso, 1981), this189

study defines the RWSI as follows:190

RWSI = 1 − ETETwet

(5)191

where ET is the regional actual ET (m3 ha−1 day−1), and ETwet is192

the regional potential ET (m3 ha−1 day−1). The potential ET is the193

maximum ET under the ideal water conditions assuming that the194

sensible heat flux is minimum (H ≈ 0) causing that all effective195

energy received by the land surface is used for ET. This amount196

of energy is �ETwet = Rn − G. The SEBAL model can be used to gener-197

ate the relevant heat fluxes (Bastiaanssen et al., 1998a,b; Gao et al.,198

2009). If the energy balance equation can be applied to replace the199

term ETwet in Eq. (5), we have:200

RWSI = 1 − �ET�ETwet

= H

Rn − G(6)201

where H is the sensible heat flux (W/m2); Rn is net radiation flux202

(W/m2); and G is soil heat flux (W/m2) (Bastiaanssen et al., 1998a,b).203

Therefore, the regional deficit of water can be monitored on a near204

real-time basis with the aid of remote sensing technologies. Eqs.205

(5) and (6) were thus used for the derivation of RWSI.206

2.6. Calculations of the temperature vegetation dryness index207

Different VIs such as NDVI, ANDVI, MSAVI and SAVI may have208

different linkages with LST providing the design basis of the VITT.209

Sandholt et al. (2002) pointed out that the simplified triangle space210

of LST–NDVI may exhibit the soil moisture contours reflecting the211

spatial patterns of the VITT. It led to the definition of the TVDI as212

expressed below:213

TVDI = Ts − Tsmin

Tsmax − Tsmin(7)214

where Tsmin is the minimum LST given the NDVI along the wet edge215

(K) (see Fig. 3); Tsmax is the maximum LST given the NDVI along the216

dry edge (K) (see Fig. 3); and Ts is the LST in any given pixel (K) (see217

Fig. 3).218

Based on the parameters of LULC, VIs, LST, RWSI, and TVDIs gen-219

erated with the above algorithms, the spatial patterns of LULC, VIs220

and LST and their interrelationships can be analyzed with respect221

to five RWSI classification categories (Table 1) for assessing the222

regional drought events. This endeavor would enable us to derive223

the linkages between the RWSI and the TVDIs, and therefore help224

Fig. 3. The spatial VITT configured by NDVI and LST.

identify the possible adaptation and application potentials of the- 225

ses four types of VIs (i.e., NDVI, ANDVI, SAVI, and MSAVI) proposed 226

for monitoring the regional drought as described in the next sec- 227

tion. This study follows Eq. (7) for the derivation of four modified 228

TVDIs (TVDI NDVI, TVDI ANDVI, TVDI SAVI, and TVDI MSAVI) for 229

comparison in our drought assessment practices. 230

Overall, the built up area can be excluded from our entire study 231

area by LULC classification. This can be done using the GRID mod- 232

ule in ARC/INFO software package. In addition to the LNADSAT 233

TM/ETM data, ground based climate data such as precipitation aver- 234

age temperature, maximum temperature, minimum temperature, 235

precipitation, average wind speed, amount of cloud and others were 236

used to compute the relevant indices. In our case study, we have 237

compared the same cells in the study region for the two reference 238

years to form the basis for comparisons. The scatter plots of LST 239

versus VIs in 2000 as opposed to the one in 1987 may be adopted 240

to answer the science question as to “how changes in these rel- 241

evant factors may influence the impacts of drought episodes in 242

vulnerability assessment?”. 243

3. Results and discussion 244

3.1. The spatial patterns of LULC 245

LANDSAT TM data were used for the analysis of LULC. With the 246

aid of ground truth data throughout the calibration and valida- 247

tion stages, the findings clearly indicate that LULC can be classified 248

into 7 categories including farmland, grassland, woodland, water 249

bodies, beach land, build-up land and saline-alkali land. Fig. 4 fea- 250

tures the side-by-side comparison of the spatial variations of LULC 251

two decades apart. Four dominant types of LULC in the study area 252

Table 1Regional drought classification categories.

Class Relative soil moisture RWSI Drought level

1 <0.4 >0.892 Heavy drought2 0.4–0.5 0.752–0.892 Medium drought3 0.5–0.6 0.612–0.752 Light drought4 0.6–0.8 0.332–0.612 Normal5 >0.8 <0.332 Wet spell

Relative soil moisture = soil moisture/soil saturation moisture × 100.

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

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Fig. 4. The LULC maps in 1987 and 2000.

include beach land, water body, saline-alkali land, and farmland.253

Yet the distribution of grassland and woodland in this area is rela-254

tively small accounting for only 0.1% and 1.6% of the entire region,255

respectively, in 2000. The change of LULC from 1987 to 2000 is256

134,940 ha, which accounted for 28% of the total study area. Grass-257

land, beach land and saline-alkali land decreased by 1.73%, 5.47%258

and 7.99%, respectively. Within 13 years, the saline-alkali land259

had been largely transformed to salt land (4.52%), farmland (4.0%),260

build-up land (3.17%), and shrimp ponds (2.99%) (Fig. 4).261

In general, this costal area was covered with beach land, saline-262

alkali land and farmland contributing to relatively smaller values263

of VIs. Conversely, the inland region covered with farmland and264

grassland would have relatively higher values of VIs. The regional265

economic development in these two decades led to a significant266

reduction of saline-alkali land and an increase in shrimp pond,267

farmland and built-up land, resulting in a net decrease of LST.268

Because of the inclusion of soil background adjustment factor (L)269

for calculating ANDIV, SAVI and MSAVI, these three VIs are highly270

unlikely to reach saturation easily, thereby resulting in larger adap-271

tation potential for a better drought vulnerability assessment when272

facing drastic changes of LULC conditions.273

3.2. The spatial correlation analysis between LST and VIs274

Although there was a clear negative correlation between LST275

and VI across a variety of spatial and temporal scales (Carlson et al.,276

1995; Sandholt et al., 2002; Chen et al., 2006; Price, 1990; Goward277

and Hope, 1989), our findings could entail the interactions between278

precipitation and temperature impacts on vegetation index by a279

different viewpoint. The long-term changes of LULC might alter the280

perceived relationship between LST and VIs, and it actually led to a281

positive correlation between VIs and LST in some episodes.282

With this said, the spatial correlations between LST and VIs283

(NDVI, ANDVI, SAVI, and MSAVI) can be further analyzed based on284

the remote sensing data sets. Fig. 5 delineates the comparative anal-285

ysis between VIs and LST across different types of VIs. To obtain the286

values of LST in Fig. 5, we queried out index values of pixels by 0.01287

intervals, and then averaged the corresponding pixels for retrieving288

their temperature values.289

The relationships between LST and VIs in 1987 and 2000 were290

both arranged simultaneously with respect to four different types291

of VIs in Fig. 5. Before reaching the first peak (i.e., turning point)292

on these curves of all four cases in Fig. 5, the slopes vary from the293

smallest one in Fig. 5(a) to the largest one in Fig. 5(d) according294

to the fitted linear equations between LST and VIs. This is partially295

due to that the effect of soil background was considered by the296

algorithms of MSAVI and SAVI (Huete, 1988; Qi et al., 1994).297

When the vegetation cover was up to a certain level (e.g., 298

NDVI ≥ 0.18, ANDVI ≥ 0.09, SAVI ≥ 0.11 and MSAVI ≥ 0.10), the 299

areas of concern were limited to those regions being mainly cov- 300

ered with high-density grassland and farmland, resulting in a sharp 301

drop of LST. The trend between VIs and LST was changed to be 302

opposite making them negatively correlated with the correlation 303

coefficient (r) greater than or equal to 0.96. The slopes derived from 304

the fitted linear equations between LST and VIs turned out to be 305

negative. Comparatively, the absolute value of the slope between 306

LST and ANDVI is the biggest whereas the absolute value of the 307

slope between LST and NDVI is the smallest. Because the impact 308

of soil background was considered by the ANDVI algorithm, this is 309

why the values of LST become sensitive to the changes of ANDVI. 310

When looking up Fig. 5 more closely, there are two turning 311

points of the scatter plots of LST versus VIs in 2000 as opposed to the 312

single one in 1987. The first turning point in 2000 occurred in the 313

specific ranges of VIs when NDVI < 0.11, ANDVI < 0.04, SAVI < 0.08 314

and MSAVI < 0.07; these regions are located nearby the shoreline 315

which was mainly covered with beach land and saline-alkali land 316

where the density of vegetation cover was very low, making VIs 317

be positively correlated with LST. It is evidenced by the corre- 318

lation coefficient (r) that is greater than or equal to 0.97. The 319

second turning point in 2000 occurred in the specific ranges of 320

VIs when NDVI > 0.58, ANDVI > 0.19, SAVI > 0.41 and MSAVI < 0.40; 321

these regions were mainly covered with crops (mainly wheat) 322

where the density of vegetation cover was very high, making 323

VIs be positively correlated with LST too. The slopes after the 324

second turning point are smaller than those before the first 325

turning point occurs over all four cases (see Fig. 5). The sec- 326

tions between these two turning points may be classified based 327

on 0.11 < NDVI < 0.58, 0.04 < ANDVI < 0.19, 0.08 < SAVI < 0.41, and 328

0.07 < MSAVI < 0.40, which represent the transition region covered 329

with saline-alkali land and farmland where the density of veg- 330

etation cover was medium. It inevitably made LST sensitive to 331

vegetation cover, resulting in a negative correlation between VIs 332

and LST. This observation is evidenced by the correlation coefficient 333

(r) that is around −0.99 across relevant cases. 334

Overall, with different densities of vegetation cover, there are 335

different patterns between LST and VIs as presented in Fig. 5. When 336

the density of vegetation cover was lower, the correlation between 337

LST and VIs is positive with the correlation coefficients (r) greater 338

than 0.96; conversely, when the density of vegetation cover was 339

medium, the correlation between LST and VIs is negative with the 340

correlation coefficient (r) around −0.99. When the density of vege- 341

tation cover was higher only in 2000, the correlation between LST 342

and VIs turned out to be positive again with the correlation coef- 343

ficient (r) around 0.95. Through our comparative study, since the 344

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

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Fig. 5. The scatter plots between VIs and LST.

region’s urbanization was more phenomenal in 2000 than that in345

1987, there is negative correlation between VIs and LST in the areas346

covered with higher density vegetation; this negative correlation is347

different with that of positive correlation in 1987 in the areas cov-348

ered with higher density vegetation. The reason of this difference349

is due to the urban heat island (UHI) effect.350

3.3. Integrating TVDI with RWSI for drought assessment351

Integration between RWSI and TVDI led to an innovative scheme352

for drought impact assessment in which RWSI was set as a refer-353

ence basis for addressing regional water deficit with respect to four354

categories to feature a suite of TVDIs (i.e., TVDI NDVI, TVDI ANDVI,355

TVDI SAVI and TVDI MSAVI). Fig. 6 shows the maps of RWSI in 1987356

and 2000, respectively, which imply that the larger the value of357

RWSI the higher the drought impact is.358

In Fig. 6, it can be seen that the regions covered with saline-alkali359

land and low density of grassland exhibited the larger RWSI, both360

of which are mainly located in the transition regions where the ET361

was salient. The soil moisture in this coastal area being covered362

with beach land and the inland area being covered with farmland363

rendered smaller RWSI, which implies a relatively water abundant364

condition. When the range of RWSI is between 0 and 1.68 in 1987365

and between 0 and 1.46 in 2000, the average RWSIs in the study 366

area were 0.54 in 1987 and 0.28 in 2000, respectively. Given that all 367

satellite data (ETM/TM) had gone through radiometric calibration 368

and atmospheric correction, such observations help draw our con- 369

clusion that the degree of water shortage in 1987 was more severe 370

than that in 2000. Since the areas of unused land (saline-alkali land, 371

beach land) in 1987 were larger than those in 2000, the vegetation 372

cover was sparse and the ET was higher in 1987. As a consequence, 373

it resulted in a relatively larger deficit of soil water. 374

Fig. 7 shows the collection of distribution maps of TVDIs in 375

1987 and 2000. Four subgroups were organized for TVDI NDVI 376

and TVDI MSAVI for the purpose of comparison. Numerically, the 377

range of the TVDIs should be between 0 and 1 and the larger 378

values of TVDIs imply the lower soil moisture contents. By com- 379

paring the spatial distributions of TVDIs in 1987 and 2000, the 380

average values of TVDIs of the study area in 1987 are 0.46, 0.43, 381

0.37, and 0.45 associated with TVDI SAVI, TVDI ANDVI, TVDI NDVI, 382

and TVDI MSAVI, respectively. In addition, the average values of 383

TVDIs of the study area in 2000 are 0.41, 0.40, 0.40, and 0.41 asso- 384

ciated with TVDI SAVI, TVDI ANDVI, TVDI NDVI, and TVDI MSAVI, 385

respectively. Hence, three out of four subgroups (i.e., TVDI SAVI, 386

TVDI ANDVI and TVDI MSAVI) confirmed that the water shortage 387

in 1987 was worse than that in 2000. 388

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

ARTICLE IN PRESSG ModelJAG 379 1–9

Z. Gao et al. / International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx 7

Fig. 6. The RWSI maps in 1987 and 2000.

Based on the values of TVDI NDVI that were 0.40 in 2000 and389

0.37 in 1987, it can be summarized that the drought was more390

severe in 2000 than that in 1987. Yet the question left over was why391

the values of TVDI NDVI showed such a controversial outcome? The392

exclusion of soil background in NDVI resulted in such discrepancies.393

Conversely, the inclusion of adjustment factor of soil background in394

ANDVI, SAVI, and MSAVI can promote the accuracy of vulnerability395

assessment.396

Because of the affects of soil background, linkages between RWSI397

and TVDIs (TVDI NDVI, TVDI SAVI, TVDI ANDVI and TVDI MSAVI)398

would become more meaningful if the intervals of 0.01 of RWSI may399

be picked up for categorical classification. Such efforts enable us to 400

present a series of deliberate scatter plots in Fig. 8 with a system- 401

atic structure for regional drought assessment. When taking Fig. 8 402

into account, it is indicative that as the values of TVDIs increase 403

the values of RWSI increase too, making them positively correlated 404

with each other in both 1987 and 2000. Such relationships between 405

RWSI and TVDIs can be further illuminated based on the partitioned 406

ranges of RWSI. When the degree of regional drought turned out to 407

be worse and the values of RWSI reached a higher level (RWSI > 0.8 408

or so), the relationship between RWSI and TVDIs became weakened 409

because TVDIs cannot reflect the actual condition of soil moisture. 410

Fig. 7. The TVDIs maps in 1987 and 2000.

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

ARTICLE IN PRESSG ModelJAG 379 1–9

8 Z. Gao et al. / International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx

Fig. 8. The scatter plots between the RWSI and the TVDIs.

When the values of RWSI were in between 0 and 0.8, how-411

ever, there was a significant positive correlation between RWSI and412

TVDIs in both 1987 and 2000 with the correlation coefficient (r)413

greater than 0.95. It may be concluded that use of TVDIs for moni-414

toring drought is only suitable for the situations of wet, normal and415

light dry. In other words, when RWSI ≤ 0.82 (the medium dry), the416

values of TVDIs can reflect the drought condition correctly; yet it417

was not the case when RWSI > 0.82 (medium dry and heavy dry). At418

the practical level, the advanced classification of regional drought419

in Table 1 helps such comparisons.420

4. Conclusions421

Three types of drought are commonly noted including meteoro-422

logical, agricultural, and hydrological droughts. This paper presents423

a synergistic approach spatially and temporarily between two424

types of drought indices associated with two reference years of425

1987 and 2000. With the aid of advancements of contemporary426

remote sensing technologies, cross-linkages and -comparisons can427

be made possible to assess these three types of drought in an all-428

inclusive framework. Our culminating experience obtained in a429

field-scale study in China proved the efficacy and effectiveness of430

our approach.431

Both drought indices of TVDIs and RWSI can be tied together 432

to address soil moisture dynamics and drought impacts. When the 433

values of RWSI may be integrated with TVDI SAVI, TVDI ANDVI and 434

TVDI MSAVI for drought assessment, we found that the shortage of 435

soil water in 1987 was more severe than that in 2000. However, 436

the use of TVDI NDVI cannot draw on the same conclusion. It was 437

due to that TVDIs are suitable for monitoring situations of wet, nor- 438

mal and light dry conditions when RWSI < 0.752. In the situation of 439

medium dry as the value of RWSI is smaller than or equal to 0.8, 440

the TVDIs can still monitor drought correctly. Nevertheless, when 441

dealing with medium dry and heavy dry as the value of RWSI is 442

greater than 0.8, TVDIs cannot correctly portray the situation of 443

water shortage for drought assessment. As a consequence, TVDIs 444

should not be used to monitor the medium and heavy drought 445

(RWSI > 0.8). 446

Acknowledgments 447

The authors are grateful for the financial support from the 448

National Natural Science Foundation of China (41071278), the 449

National 973 Key Project of China (2010CB951603), and the USDA 450

CSREES Project (2006-34263-16926). 451

Please cite this article in press as: Gao, Z., et al., Integrating temperature vegetation dryness index (TVDI) and regional waterstress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinf. (2010),doi:10.1016/j.jag.2010.10.005

ARTICLE IN PRESSG ModelJAG 379 1–9

Z. Gao et al. / International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx 9

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