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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 1–9International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx

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International Journal of Applied Earth Observation andGeoinformation

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

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Zhiqiang Gaoa,b,∗, Wei Gaob, Ni-Bin Changc4a 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

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a r t i c l e i n f o910

Article history:11Received 20 May 201012Accepted 25 October 201013

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Keywords:15Drought assessment16Remote sensing17Urbanization effect18Urban heat island19Coastal 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 a22consequence of a reduction of precipitation and/or abnormal tem-23perature over an extended period of time. In urban drought events,24which is a temporary aberration, the drought might turn pastures25brown, threaten shrubs and trees, and result in low vegetation26cover and high land surface temperature (LST) simultaneously.27Given that drought is a normal, recurrent feature of climate, it28occurs in virtually all climatic regimes. Common indicators for29drought assessment include ecological variables such as vegetation30cover and evapotranspiration (ET), meteorological variables such31as precipitation, as well as hydrological variables such as soil mois-32ture, stream flow, ground water levels, reservoir and lake levels,33and snow pack.34

The water stress index method is the ratio of the actual ET35and 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: [email protected] (Z. Gao).

With this concept, Jackson and Idso (1981) promoted the crop 37water stress index (CWSI) and Moron et al. (1994) proposed water 38deficit index (WDI). In addition, the moisture index method is an 39approach for monitoring the regional drought with water char- 40acteristics of strong absorption in shortwave infrared band (Xu, 412006; Fensholt and Sandholt, 2003; Chen et al., 2005). For example, 42Kogan (1995) proposed the vegetation condition index (VCI), and 43Mcffters (1996) proposed the normalized difference water index 44(NDWI) by combining LANDSAT TM green and near-infrared bands. 45Both of which are the moisture index method. The temperature 46vegetation dryness index (TVDI) method based on the vegetation 47index/temperature trapezoid eigenspace (VITT) (Sandholt et al., 482002) also belongs to the category of moisture index method. Ther- 49mal inertia method is the approach using thermal infrared remote 50sensing data to monitor soil moisture. Waston et al. (1971) firstly 51proposed a simple model to calculate the thermal inertia with daily 52difference of LST. Since then, many scientists carried out a vari- 53ety of experimental studies with respect to the thermal inertia 54principles (Price, 1977, 1985; England, 1990; Xue and Cracknell, 551995). 56

The spatial VITT has been applied widely in many studies reflect- 57ing the potential impact of LST on NDVI. Moron et al. (1994) 58explained the algorithm of crop water stress index (CWSI), which 59

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ARTICLE IN PRESSG ModelJAG 379 1–92 Z. Gao et al. / International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx

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

could avoid the measurements of leaf temperature when studying60the situation of vegetation cover. The slope of scatter plot com-61bined LST and VI represents degree of crop water stress gradient62based on the negative relationship between LST and VI (Carlson63et al., 1995; Moran et al., 1996; Fensholt and Sandholt, 2003;64Venturini et al., 2004; Wang et al., 2007). Many studies moni-65tored ET and soil moisture with spatial VITT to illuminate their66correlation (Goward and Hope, 1989; Price, 1990; Ridd, 1995;Q367Gillies and Carlson, 1995; Gillies et al., 1995, 1997; Sandholt et al.,682002; Wang et al., 2004; Han et al., 2006). These studies can69help us evaluate the spatial and temporal variations of drought70more accurately although each index has strengths and weaknesses71which need to be clearly understood as they are integrated into72drought early warning systems. Since the spatial and temporal73patterns of vegetation dynamics could be associated with precip-74itation changes and temperature fluctuations simultaneously, an75implicit hypothesis of the current study is that integration of dif-76ferent indices for drought assessment would be better than using77a single one.78

To test the application and adaptation potential of TVDIs with79the aid of a suite of remote sensing technologies, this study devel-80ops a synergistic approach with respect to three TVDIs that were81designed to combine temperature with four different vegetation82indices (VIs) group wise. Yet it is believed that the soil-adjusted83vegetation indices may be better coupled with TVDIs for meet-84ing the study goal (Makkeasorn and Chang, 2009). To prove the85concept, four vegetation indices were therefore included for com-86parisons 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 model89was prepared as a reference basis for the refinements of TVDIs90when monitoring the regional drought events (Bastiaanssen et al.,911998a,b). It is anticipated that the science question as to “how92changes in these relevant factors may influence the impacts of93drought episodes in vulnerability assessment?” can be examined94and answered with this synergistic approach in a fast developing95coastal region, Northern China.

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2. Methodologies97

2.1. The study area98

The study area is located at Laizhou Bay in Shandong Province,99China (Fig. 1) within the latitude of 36◦48′43′′–37◦32′49′′ and longi-100tude 118◦37′37′′–119◦44′31′′. The length along the east-west and of101north-south directions is approximately 97 km and 79 km, respec-102

tively. The total study area is 486,245 ha. Land elevation drops 103mildly from 30 m to 2 m above the sea level. Yet the length of 104the meandering coastal line within the study area is about 400 km 105long. Such coastal region is an active floodplain that was formed 106by sediment laden water being released from the neighboring 107river channel through the regional morphological and sedimen- 108tary dynamics. Three cities, including the Shouguang City, part of 109the Weifang City (e.g., the Hangting area), and most of the Changyi 110City, are situated along this coastal line. The sediment distribution 111in the alluvial plain ranges from fine sand (close to the low water 112line) to the typical mud carried by flood currents. Close to the open 113ocean, the climate system in this area is a moist, warm, temperate 114continental monsoon climate (Cao, 2002; Wang et al., 2002; Guan 115et al., 2001). 116

2.2. The satellite image processing 117

Fig. 2 delineates the flowchart of satellite image processing in 118support of the case-based drought assessment. First of all, LANDSAT 119TM/ETM+ images, digital elevation model (DEM) data, and climate 120data were collected. All datasets were vectorized and interpolated 121as grid datasets with UTM projection in advance to ease the appli- 122cation in geographical information systems (GIS). 123

In this study, the raw images were geo-referenced to a common 124UTM coordinate system, and we then re-sampled all of the images 125to unify relative resolution in images of different sizes using the 126nearest neighbor algorithm with a pixel size of 30 m × 30 m for all 127bands, including the thermal band. 128

Following the streamlines in Fig. 2, LANDSAT TM/ETM+ images 129were processed for the mapping of land use/land cover change 130(LUCC), VIs, LST and heat fluxes. LUCC associated with May 7th 1987 131and May 2nd 2000 in the study area was analyzed with respect 132to the proper interpretation of LANDSAT TM/ETM images and was 133validated with ground truth data. Regional scale heat fluxes were 134estimated with the aid of remote sensing images and the surface 135energy balance algorithm (e.g., SEBAL model) (Bastiaanssen et al., 1361998a,b). 137

LST retrieval was carried out using the thermal bands of 138TM/ETM+ data to ease the application of the radiance transfer 139equation (Qin et al., 2001). The equations for normalized differ- 140ence vegetation index (NDVI) (Rouse et al., 1973; Tucker, 1979), 141soil adjusted vegetation index (SAVI) (Huete, 1988), modified SAVI 142(MSAVI) (Qi et al., 1994) and adjusted normalized difference vege- 143tation index (ANDVI) (Liu et al., 2008) were collectively employed 144to produce a suite of VIs in support of advanced drought impact 145assessment. All of the preparatory efforts led to develop the inte- 146grated TDVI and RWSI for final analysis in the context of drought 147

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ARTICLE IN PRESSG ModelJAG 379 1–9Z. Gao et al. / International Journal of Applied Earth Observation and Geoinformation xxx (2010) xxx–xxx 3

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-148rithms/equations in a greater detail.149

2.3. Retrieval of the land surface heat fluxes and land surface150temperature (LST)151

With LANDSAT satellite images, the heat fluxes were estimated152using SEBAL model and calculated using Arc/Info 9.0 Macro Lan-153guage (AML) and Compaq Visual FORTRAN 6.5 mixed-language pro-154gramming in this study (Bastiaanssen et al., 1998a,b). Our SEBAL-155based computer package can be operated in a Microsoft Windows156system using the ESRI GRID module as the major data format. To157ease the application of the radiance transfer equation, Qin et al.158(2001) derived an approximate expression for LST retrieval suit-159able for thermal bands of TM/ETM+ data. Our LST maps were also160derived 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) 163are 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

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where �red is red band (0.63–0.69 �m) reflectance, �nir is near red169band (0.76–0.90 �m) reflectance, �blue is blue band (0.45–0.52 �m)170reflectance, �green is green band (0.52–0.60 �m) reflectance, L is171adjustment factor, set to minimum background effects (L = 0.5).172

This study follows Eqs. (1)–(4) for the derivation of VIs. It is noted173that the calculations of ANDVI, SAVI and MSAVI had included a few174refinements. For instance, to resolve the barrier of vegetation index175saturation issues (e.g., the index number indicates the amount of176net rainfall that is required to reduce the index to zero, or saturation177along the scale), Gitelson et al. (1996) introduced the green band to178calculate VIs. In order to reduce the impact of soil background on179VIs, Huete (1988) introduced the soil background adjustment fac-180tor (L) to calculate VIs. The soil background adjustment factor (L)181was actually applied in the algorithms of ANDVI, SAVI and MSAVI.182Besides, the green and blue bands were used for the calculation183of ANDVI while the green band was used for the calculation of184MSAVI. As a consequence, these three VIs (ANDVI, SAVI and MSAVI)185are quite different values as compared to the corresponding NDVI186value.187

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

According to the CWSI mechanism (Jackson and Idso, 1981), this189study 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 is192the regional potential ET (m3 ha−1 day−1). The potential ET is the193maximum ET under the ideal water conditions assuming that the194sensible heat flux is minimum (H ≈ 0) causing that all effective195energy received by the land surface is used for ET. This amount196of energy is �ETwet = Rn − G. The SEBAL model can be used to gener-197ate the relevant heat fluxes (Bastiaanssen et al., 1998a,b; Gao et al.,1982009). If the energy balance equation can be applied to replace the199term ETwet in Eq. (5), we have:200

RWSI = 1 − �ET�ETwet

= HRn − 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).203Therefore, the regional deficit of water can be monitored on a near204real-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 have208different linkages with LST providing the design basis of the VITT.209Sandholt et al. (2002) pointed out that the simplified triangle space210of LST–NDVI may exhibit the soil moisture contours reflecting the211spatial patterns of the VITT. It led to the definition of the TVDI as212expressed below:213

TVDI = Ts − TsminTsmax − 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 the216dry edge (K) (see Fig. 3); and Ts is the LST in any given pixel (K) (see217Fig. 3).218

Based on the parameters of LULC, VIs, LST, RWSI, and TVDIs gen-219erated with the above algorithms, the spatial patterns of LULC, VIs220and LST and their interrelationships can be analyzed with respect221to five RWSI classification categories (Table 1) for assessing the222regional drought events. This endeavor would enable us to derive223the 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- 225ses four types of VIs (i.e., NDVI, ANDVI, SAVI, and MSAVI) proposed 226for monitoring the regional drought as described in the next sec- 227tion. This study follows Eq. (7) for the derivation of four modified 228TVDIs (TVDI NDVI, TVDI ANDVI, TVDI SAVI, and TVDI MSAVI) for 229comparison in our drought assessment practices. 230

Overall, the built up area can be excluded from our entire study 231area by LULC classification. This can be done using the GRID mod- 232ule in ARC/INFO software package. In addition to the LNADSAT 233TM/ETM data, ground based climate data such as precipitation aver- 234age temperature, maximum temperature, minimum temperature, 235precipitation, average wind speed, amount of cloud and others were 236used to compute the relevant indices. In our case study, we have 237compared the same cells in the study region for the two reference 238years to form the basis for comparisons. The scatter plots of LST 239versus VIs in 2000 as opposed to the one in 1987 may be adopted 240to answer the science question as to “how changes in these rel- 241evant factors may influence the impacts of drought episodes in 242vulnerability 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 246aid of ground truth data throughout the calibration and valida- 247tion stages, the findings clearly indicate that LULC can be classified 248into 7 categories including farmland, grassland, woodland, water 249bodies, beach land, build-up land and saline-alkali land. Fig. 4 fea- 250tures the side-by-side comparison of the spatial variations of LULC 251two 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 0.892 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



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

include beach land, water body, saline-alkali land, and farmland.253Yet the distribution of grassland and woodland in this area is rela-254tively small accounting for only 0.1% and 1.6% of the entire region,255respectively, in 2000. The change of LULC from 1987 to 2000 is256134,940 ha, which accounted for 28% of the total study area. Grass-257land, beach land and saline-alkali land decreased by 1.73%, 5.47%258and 7.99%, respectively. Within 13 years, the saline-alkali land259had been largely transformed to salt land (4.52%), farmland (4.0%),260build-up land (3.17%), and shrimp ponds (2.99%) (Fig. 4).261

In general, this costal area was covered with beach land, saline-262alkali land and farmland contributing to relatively smaller values263of VIs. Conversely, the inland region covered with farmland and264grassland would have relatively higher values of VIs. The regional265economic development in these two decades led to a significant266reduction of saline-alkali land and an increase in shrimp pond,267farmland and built-up land, resulting in a net decrease of LST.268Because of the inclusion of soil background adjustment factor (L)269for calculating ANDIV, SAVI and MSAVI, these three VIs are highly270unlikely to reach saturation easily, thereby resulting in larger adap-271tation potential for a better drought vulnerability assessment when272facing drastic changes of LULC conditions.273

3.2. The spatial correlation analysis between LST and VIs274

Although there was a clear negative correlation between LST275and VI across a variety of spatial and temporal scales (Carlson et al.,2761995; Sandholt et al., 2002; Chen et al., 2006; Price, 1990; Goward277and Hope, 1989), our findings could entail the interactions between278precipitation and temperature impacts on vegetation index by a279different viewpoint. The long-term changes of LULC might alter the280perceived relationship between LST and VIs, and it actually led to a281positive 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 on284the remote sensing data sets. Fig. 5 delineates the comparative anal-285ysis between VIs and LST across different types of VIs. To obtain the286values of LST in Fig. 5, we queried out index values of pixels by 0.01287intervals, and then averaged the corresponding pixels for retrieving288their temperature values.289

The relationships between LST and VIs in 1987 and 2000 were290both arranged simultaneously with respect to four different types291of VIs in Fig. 5. Before reaching the first peak (i.e., turning point)292on these curves of all four cases in Fig. 5, the slopes vary from the293smallest one in Fig. 5(a) to the largest one in Fig. 5(d) according294to the fitted linear equations between LST and VIs. This is partially295due to that the effect of soil background was considered by the296algorithms of MSAVI and SAVI (Huete, 1988; Qi et al., 1994).297

When the vegetation cover was up to a certain level (e.g., 298NDVI ≥ 0.18, ANDVI ≥ 0.09, SAVI ≥ 0.11 and MSAVI ≥ 0.10), the 299areas of concern were limited to those regions being mainly cov- 300ered with high-density grassland and farmland, resulting in a sharp 301drop of LST. The trend between VIs and LST was changed to be 302opposite making them negatively correlated with the correlation 303coefficient (r) greater than or equal to 0.96. The slopes derived from 304the fitted linear equations between LST and VIs turned out to be 305negative. Comparatively, the absolute value of the slope between 306LST and ANDVI is the biggest whereas the absolute value of the 307slope between LST and NDVI is the smallest. Because the impact 308of soil background was considered by the ANDVI algorithm, this is 309why the values of LST become sensitive to the changes of ANDVI. 310

When looking up Fig. 5 more closely, there are two turning 311points of the scatter plots of LST versus VIs in 2000 as opposed to the 312single one in 1987. The first turning point in 2000 occurred in the 313specific ranges of VIs when NDVI < 0.11, ANDVI < 0.04, SAVI < 0.08 314and MSAVI < 0.07; these regions are located nearby the shoreline 315which was mainly covered with beach land and saline-alkali land 316where the density of vegetation cover was very low, making VIs 317be positively correlated with LST. It is evidenced by the corre- 318lation coefficient (r) that is greater than or equal to 0.97. The 319second turning point in 2000 occurred in the specific ranges of 320VIs when NDVI > 0.58, ANDVI > 0.19, SAVI > 0.41 and MSAVI < 0.40; 321these regions were mainly covered with crops (mainly wheat) 322where the density of vegetation cover was very high, making 323VIs be positively correlated with LST too. The slopes after the 324second turning point are smaller than those before the first 325turning point occurs over all four cases (see Fig. 5). The sec- 326tions between these two turning points may be classified based 327on 0.11 < NDVI < 0.58, 0.04 < ANDVI < 0.19, 0.08 < SAVI < 0.41, and 3280.07 < MSAVI < 0.40, which represent the transition region covered 329with saline-alkali land and farmland where the density of veg- 330etation cover was medium. It inevitably made LST sensitive to 331vegetation cover, resulting in a negative correlation between VIs 332and 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 335different patterns between LST and VIs as presented in Fig. 5. When 336the density of vegetation cover was lower, the correlation between 337LST and VIs is positive with the correlation coefficients (r) greater 338than 0.96; conversely, when the density of vegetation cover was 339medium, the correlation between LST and VIs is negative with the 340correlation coefficient (r) around −0.99. When the density of vege- 341tation cover was higher only in 2000, the correlation between LST 342and VIs turned out to be positive again with the correlation coef- 343ficient (r) around 0.95. Through our comparative study, since the 344

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

region’s urbanization was more phenomenal in 2000 than that in3451987, there is negative correlation between VIs and LST in the areas346covered with higher density vegetation; this negative correlation is347different with that of positive correlation in 1987 in the areas cov-348ered with higher density vegetation. The reason of this difference349is 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 scheme352for drought impact assessment in which RWSI was set as a refer-353ence basis for addressing regional water deficit with respect to four354categories to feature a suite of TVDIs (i.e., TVDI NDVI, TVDI ANDVI,355TVDI SAVI and TVDI MSAVI). Fig. 6 shows the maps of RWSI in 1987356and 2000, respectively, which imply that the larger the value of357RWSI the higher the drought impact is.358

In Fig. 6, it can be seen that the regions covered with saline-alkali359land and low density of grassland exhibited the larger RWSI, both360of which are mainly located in the transition regions where the ET361was salient. The soil moisture in this coastal area being covered362with beach land and the inland area being covered with farmland363rendered smaller RWSI, which implies a relatively water abundant364condition. 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 366area were 0.54 in 1987 and 0.28 in 2000, respectively. Given that all 367satellite data (ETM/TM) had gone through radiometric calibration 368and atmospheric correction, such observations help draw our con- 369clusion that the degree of water shortage in 1987 was more severe 370than that in 2000. Since the areas of unused land (saline-alkali land, 371beach land) in 1987 were larger than those in 2000, the vegetation 372cover was sparse and the ET was higher in 1987. As a consequence, 373it resulted in a relatively larger deficit of soil water. 374

Fig. 7 shows the collection of distribution maps of TVDIs in 3751987 and 2000. Four subgroups were organized for TVDI NDVI 376and TVDI MSAVI for the purpose of comparison. Numerically, the 377range of the TVDIs should be between 0 and 1 and the larger 378values of TVDIs imply the lower soil moisture contents. By com- 379paring the spatial distributions of TVDIs in 1987 and 2000, the 380average values of TVDIs of the study area in 1987 are 0.46, 0.43, 3810.37, and 0.45 associated with TVDI SAVI, TVDI ANDVI, TVDI NDVI, 382and TVDI MSAVI, respectively. In addition, the average values of 383TVDIs of the study area in 2000 are 0.41, 0.40, 0.40, and 0.41 asso- 384ciated with TVDI SAVI, TVDI ANDVI, TVDI NDVI, and TVDI MSAVI, 385respectively. Hence, three out of four subgroups (i.e., TVDI SAVI, 386TVDI ANDVI and TVDI MSAVI) confirmed that the water shortage 387in 1987 was worse than that in 2000. 388

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

Based on the values of TVDI NDVI that were 0.40 in 2000 and3890.37 in 1987, it can be summarized that the drought was more390severe in 2000 than that in 1987. Yet the question left over was why391the values of TVDI NDVI showed such a controversial outcome? The392exclusion of soil background in NDVI resulted in such discrepancies.393Conversely, the inclusion of adjustment factor of soil background in394ANDVI, SAVI, and MSAVI can promote the accuracy of vulnerability395assessment.396

Because of the affects of soil background, linkages between RWSI397and TVDIs (TVDI NDVI, TVDI SAVI, TVDI ANDVI and TVDI MSAVI)398would become more meaningful if the intervals of 0.01 of RWSI may399

be picked up for categorical classification. Such efforts enable us to 400present a series of deliberate scatter plots in Fig. 8 with a system- 401atic structure for regional drought assessment. When taking Fig. 8 402into account, it is indicative that as the values of TVDIs increase 403the values of RWSI increase too, making them positively correlated 404with each other in both 1987 and 2000. Such relationships between 405RWSI and TVDIs can be further illuminated based on the partitioned 406ranges of RWSI. When the degree of regional drought turned out to 407be worse and the values of RWSI reached a higher level (RWSI > 0.8 408or so), the relationship between RWSI and TVDIs became weakened 409because TVDIs cannot reflect the actual condition of soil moisture. 410

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




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

When the values of RWSI were in between 0 and 0.8, how-411ever, there was a significant positive correlation between RWSI and412TVDIs in both 1987 and 2000 with the correlation coefficient (r)413greater than 0.95. It may be concluded that use of TVDIs for moni-414toring drought is only suitable for the situations of wet, normal and415light dry. In other words, when RWSI ≤ 0.82 (the medium dry), the416values of TVDIs can reflect the drought condition correctly; yet it417was not the case when RWSI > 0.82 (medium dry and heavy dry). At418the practical level, the advanced classification of regional drought419in Table 1 helps such comparisons.420

4. Conclusions421

Three types of drought are commonly noted including meteoro-422logical, agricultural, and hydrological droughts. This paper presents423a synergistic approach spatially and temporarily between two424types of drought indices associated with two reference years of4251987 and 2000. With the aid of advancements of contemporary426remote sensing technologies, cross-linkages and -comparisons can427be made possible to assess these three types of drought in an all-428inclusive framework. Our culminating experience obtained in a429field-scale study in China proved the efficacy and effectiveness of430our approach.431

Both drought indices of TVDIs and RWSI can be tied together 432to address soil moisture dynamics and drought impacts. When the 433values of RWSI may be integrated with TVDI SAVI, TVDI ANDVI and 434TVDI MSAVI for drought assessment, we found that the shortage of 435soil water in 1987 was more severe than that in 2000. However, 436the use of TVDI NDVI cannot draw on the same conclusion. It was 437due to that TVDIs are suitable for monitoring situations of wet, nor- 438mal and light dry conditions when RWSI < 0.752. In the situation of 439medium dry as the value of RWSI is smaller than or equal to 0.8, 440the TVDIs can still monitor drought correctly. Nevertheless, when 441dealing with medium dry and heavy dry as the value of RWSI is 442greater than 0.8, TVDIs cannot correctly portray the situation of 443water shortage for drought assessment. As a consequence, TVDIs 444should 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 448National Natural Science Foundation of China (41071278), the 449National 973 Key Project of China (2010CB951603), and the USDA 450CSREES Project (2006-34263-16926). 451




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