Application of Microwave Vegetation Index (MVI) to Monitoring Drought in Sichuan Province of

China Wang Yongqian(1)(2)(3) Shi Jiancheng(3)

(1) College of Environmental and Resource Science, Chengdu University of Information Technology, Chengdu China

(2) Institute of Arid Meteorology, CMA, Lanzhou

Liu Zhihong(1) Liu Wenjuan(1)

(3) State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of

Sciences, Beijing 100101, China

Abstract-One of the most serious droughts in last century

occurred in Sichuan Basin in the summer of 2006. In this paper,

three different methods were used to monitor this drought. The

first method is TVDI (Temperature Vegetation Dryness Index).

The second method is TMVDI (Temperature Microwave

Vegetation Dryness Index). NDVI in method one was replaced by

MVI. Because the microwave radiometer data can be obtained

from descend or ascend orbit and high or low frequencies, four

TMVDI results were derived. In order to evaluate the remote

sensing based methods, SPI (Standardized Precipitation Index)

method was also performed in this paper. Comparison between

these methods shows that TMVDI from low frequency descend

pass data are most suitable for the drought monitoring. Key words-Drought monitoring, MVI, TMVDI

I. INTRODUCTION

Drought can be monitored effectively using drought indices such as the Palmer Drought Severity Index (PDSI) [1] or the Standardized Precipitation Index (SPI) [2] calculated with in-situ meteorological data from weather stations.

However, using these indices for drought monitoring is costly because the meteorological data over a large region are usually scattered and insufficient for timely drought detection, monitoring and decision making.

In contrast, satellite data have greater capability to monitor drought in a spatially continuous fashion and on a regular time interval and therefore have proven to be a dependable source for monitoring drought. Various types of satellite data are currently available for monitoring drought.

These can be categorized into: (1) visible and infrared data, (2) passive microwave data, and (3) active microwave data.

Based on these satellite data, several satellite-based

indices have been developed and used to effectively detect and monitor droughts.

The normalized difference vegetation index (NDVI) has been the most widely used for evaluating drought conditions [3]. NDVI is to express the condition of vegetation by using the spectral character of red, near infrared band or their simple combination, based on the strong absorbing property that chlorophyll has on lights. There are also many other indices to show the drought condition. Vegetation Condition Index (VCI) is obtained by scaling NDVI values from 0 to 1 using the minimum and maximum NDVI for each location [4]. Similarly, the Temperature Condition Index (TCI) was also introduced [5]. The Vegetation Health Index (VHI) which is the combination of VCI and TCI was introduced to assess the stress of vegetation related to both water and temperature [6]. Some indices based on hyperspectral remote sensing data were introduced, such as Normalized Difference Water Index (NDWI), Normalized Difference Drought Index (NDDI), and Normalized Multiband Drought Index (NMDI). Because NDVI provides little information about soil water content and land surface temperature (LST) is relatively related to water stress, the combination of LST and NDVI can provide better information on vegetation and moisture conditions at the surface (TVDI).

While many existing studies have used these remote sensing drought indices for drought monitoring, their use is limited. Most of the indices listed above are based on NDVI. However, calculation of the NDVI is sensitive to a number of perturbing factors that include: atmospheric effects (the actual

composition of the atmosphere with respect to water vapor and aerosol), clouds (deep, thin, shadow), soil effects (moisture state, color), snow cover, anisotropic effects (geometry of the target), and spectral effects (different instruments). These factors introduce uncertainty in quantitative assessments. A major limitation of the NDVI and similar indices based on optical/infrared remote sensing data is that optical sensors can only monitor a very thin layer of the canopy. They cannot provide information on woody biomass, and total above-ground live carbon, which are of great interest to drought monitoring.

In this study, we will explore and demonstrate a new method for drought monitoring based on Microwave Vegetation Index (MVI) [7] instead of NDVI in current drought monitoring indices. Microwave observations are less affected by atmospheric conditions than traditional optical methods. On the other hand, the variability in the background emission signals resulting from the soil state can have a greater effect on the microwave observations than when using optical sensors that only sense the canopy. MVI has a form that minimizes dealing with background soil emission signal problems. New vegetation information can be provided by MVI due to they reflect not only the leafy part of vegetation information but also the woody part of the vegetation information resulted from the intrinsic differences between what microwave and optical sensors observe.

The main objective of this study is to build a drought monitoring method called Temperature Microwave Vegetation Dryness Index (TMVDI) based on MVI and LST. The TMVDI results were verified using SPI results which are from in-situ data. A comparison was also investigated between TMVDI and TVDI to evaluate the efficiency of TMVDI for drought monitoring.

II. STUDY AREA AND DATA

A. Study area

In the summer of 2006, there was a continuous high air temperature and 40% precipitation decrease in Sichuan province, China (figure 1), which resulted in a drought occurring once in 100 a Sichuan where a population of over ten million was confronted with drinking water difficulty. The drought covered about 30000 km2 of cropland, at least two

thirds of which suffered serious losses. The arid disaster not only imposed negative influences on the ecological environment of upper Yangtze River, but also threatened people’s daily life and the agriculture and industry production as well.

Sichuan province is located on the eastern edge of the Tibetan Plateau. It is bordered by the Himalayas to the west, the Qinling range to the north, and mountainous areas of Yunnan to the south. Sichuan province covers the region of Sichuan Basin and eastern Tibetan Plateau, so the terrain, vegetation and climate all show distinct spatial heterogeneity. Figure 2 shows the surface coverage of Sichuan Province and figure 3 shows the topography of Sichuan. The Sichuan’s topographical features are characterized by plains in the basin (lower than 800 m) and plateau in western. Between basin and plateau, there lies the western bank of Sichuan basin (the altitude lower than 3000 m, higher than 800 m). From figure 1, it can be seen that there are mainly crop land in Sichuan basin and grassland in Sichuan western plateau. Mixed forest are growing in Western bank of Sichuan basin.

Figure 1 The location of Sichuan Province in China

Figure 2 Surface coverage type of Sichuan Province

Figure 3 Elevation of Sichuan Province. The black points in the figure are the

distribution of weather stations

B. data

SPI has been accepted more broadly for research and operational use because of its well known advantages that SPI only uses precipitation data. SPI results will be used to verify the TMVI results. Monthly total precipitation data of 161 national basic weather stations of Sichuan province during 1976-2006 are collected for SPI calculation. The distributions of the stations in Sichuan province are shown in figure 1.

The monthly NDVI deduced from MODIS vegetation indices MOD13C2 product and LST from MOD11C35 product acquired from NASA of 2006 were used to monitor drought in Sichuan province. Cloud-screening processing of MODIS LST data eliminates many pixels of the daily and 8-day MODIS LST products. Thus, the monthly MODIS LST product seems suitable for detecting and monitoring agricultural droughts in this study. The two datasets (monthly NDVI and LST) are level-3 MODIS/Terra products with a spatial resolution of 5.6 km. The accuracy of MOD13C2 and MOD11C35 data have been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts (Solano et al., 2010; Vermote et al., 2008). The products are thus ready to use in scientific publications (Vermote et al., 2008).

One year of AMSR-E level 3 brightness temperature data from January 1 to December 31, 2006, obtained from the National Snow and Ice Data Center (NSIDC), was analyzed. This product is the 25 km x 25 km grid data re-sampled from AMSR-E level 2A brightness temperature data into a global EASE-GRID projection(http://nsidc.org/data/docs/daac/ae_land3_l3_soil_m

oisture.gd.html).Both the descending pass data (night pass) and ascending pass data (daily pass) were used. It has been demonstrated that Radio-Frequency Interference (RFI) has a significant impact on the retrieval of land surface geophysical properties from satellite microwave observations. While RFI has not been well characterized, strong RFI will result in an irregular frequency gradient of the observed brightness temperatures. In this paper, if MVI are not in normal range, the pixels that are contaminated will be eliminated. A moving median filter with seven measurements in the time domain was used to reduce the fluctuations caused by the effects of the different atmospheric conditions.

III. METHODOLOGY

TMVDI was used to investigate drought in Sichuan province using AMSR-E MVI and MODIS LST data. The data were processed for the 2006 summer season. The TMVDI results were verified using SPI results. A comparison between TMVDI and TVDI was also made to evaluate the efficiency of TMVDI for drought monitoring in Sichuan province.

A. Ground observed drought

In this paper, the drought occurred on summer of 2006 in Sichuan was monitored, therefore, one-month SPI values calculated using in-situ monthly precipitation data were used ass reference data for measuring drought conditions. The first step in the calculation of the SPI is to determine a probability density function that describes the long-term series of observations. Once this distribution is determined, the cumulative probability of an observed precipitation amount is computed. The inverse normal (Gaussian) function is then applied to the probability. The result is the SPI.

SPI values are positive (negative) for greater (less) than median precipitation. The departure from zero is a probability indication of the severity of the wetness or aridity that can be used for drought monitoring. A stochastic model of Ordinary Kriging is used to interpolate SPI drought index values. Figure 4 shows the drought of May, July, June, August, September 2006 of Sichuan province.

Figure 4 Drought monitoring results derived from SPI

B. Optical remote sensing drought indices

TVDI was used to make a comparison to TMVDI using monthly MODIS NDVI and LST data. The data were processed for the 2006 summer season (i.e., from May to September 2006). TVDI is an index based on the empirical interpretation of NDVI-LST triangle (Figure 5). The combination of NDVI and LST can provide more complete information on soil moisture at the surface.

Baresoil Partial

coverFull

cover

TVDI=0 Wet edge LSTmin

Max evaporation

No evaporation

TVDI=1

Dry edgeLSTmax=a+b*NDVI

LS

TNDVI Figur

Figure 5 NDVI-LST triangle for TVDI

The TVDI is calculated using equation (1),

min

min

LSTNDVIbaLSTLSTTVDI���

��

(1)

Where LST is the observed surface temperature at a given pixel; NDVI is the observed normalized difference vegetation index; and a and b are the intercept and slope of the dry edge (the upper straight line in the triangle) calculated from the NDVI-LST space regression with small intervals of NDVI

(LSTmax=a+b*NDVI), where LSTmax is the maximum surface temperature observation for a given NDVI.

The lower horizontal line of the triangle represents the wet edge (LSTmin). LSTmin was calculated by averaging a group of points in the lower limits of the scatter plots. The TVDI values range from 0 to 1: TVDI=1 at the dry edge, indicating no evaporation from the soil or limited moisture supply; and TVDI=0 at the wet edge, indicating maximum evaporation from the soil or unlimited moisture supply. The NDVI-LST scatter plots for 2006 summer season were shown in figure 6.

TVDI Sichuan Basin

272

280

288

296

304

312

320

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1NDVI

LST

TVDI Western Plateau

272

280

288

296

304

312

0 0.2 0.4 0.6 0.8 1NDVI

LST

Figure 6 The NDVI and LST scatter plot of TVDI for (left) Sichuan Basin

and (right) Western Plateau

Figure 7 Drought monitoring results derived from TVDI

C. Microwave vegetation drought monitoring indices

Under the AMSR-E configuration and at frequencies f1 and f2, MVI is calculated using equation (2):

��

� (2)

Where TBv and TBh are vertical and horizontal polarization brightness temperatures for a given frequency. MVI minimizes the ground surface emission signals by using the ratio of the polarization differences obtained from two adjacent AMSR-E frequencies. MVI can provide new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the

microwave sensor can see through it. The drought monitoring index TMVDI developed in this

paper is the modification of TVDI that NDVI is replaced by MVI. The three lowest AMSR-E frequencies (6.925, 10.65, 18.7) and two polarizations (V and H) were used to derive two MVIs. For AMSR-E, global swath coverage is achieved every two days or less, separately for ascending and descending passes, except for small region near the poles. In order to consist with the time resolution of LST, MVIs were also processed for one month time scale. Finally, four MVIs were derived from AMSR-E including ascending and descending passes of low frequency pair (6.925 GHz/10.65GHz) and the high frequency pair (10.65 GHz/18.7GHz). The MVI-LST scatter plots for 2006 summer season were shown in figure 8. figure 9.

Low Ascend Sichuan Basin

272

280

288

296

304

312

0.40.50.60.70.80.9 MVI

LST

Low Ascend Western Plateau

MVI

LST

High Ascend Sichuan Basin

272

280

288

296

304

312

0.40.50.60.70.80.9

LST

High Ascend Western Plateau

MVI

LST

Figure 8 The MVI and LST scatter plot of TMVDI for low and high ascend

frequency in Sichuan Basin and Western Plateau

Low Descend Sichuan Basin

272

280

288

296

304

312

0.40.50.60.70.80.9 MVI

LST

Low Descend Western Plateau

MVI

LST

High Descend Sichuan Basin

272

280

288

296

304

312

320

0.40.50.60.70.80.9 MVI

LST

High Descend Western Plateau

272

280

288

296

304

312

0.40.50.60.70.80.9 MVI

LST

Figure 9 The MVI and LST scatter plot of TMVDI for low and high descend

frequency in Sichuan Basin and Western Plateau

TABLE 1 THE INTERCEPTIONS AND SLOPES FOR ALL THE LINEAR REGRESSION

OF THE TVDI AND TMVDI SCATTER PLOTS IN FIGURE 6, FIGURE 8 AND

FIGURE 9

Dry edge Wet edge

Basin Plateau Basin Plateau

TVDI

Slope -35.807 22.134 -11.466 2.081

Interception 320.03 289.69 318 283

r 0.900 0.801 0.572 0.124

TMVDI

(High

Ascend)

Slope 45.777 28.815 11.042 19.832

Interception 279.69 285.73 291.86 276.4

r 0.738 0.718 0.435 0.446

TMVDI

(High

Descend)

Slope 37.01 6.3551 19.867 19.422

Interception 288.57 299.97 288.35 277.31

r 0.931 0.149 0.534 0.480

TMVDI

(Low

Ascend)

Slope 42.351 26.468 14.76 37.672

Interception 277.36 284.78 288.58 265.02

r 0.967 0.835 0.720 0.649

TMVDI

(Low

Descend)

Slope 36.748 25.282 30.922 34.491

Interception 281.12 285.7 278.55 266.34

r 0.973 0.892 0.855 0.695

Table 1 gives the interceptions and slopes for all the linear regression of the MVI-LST scatter plots in figure 8 and figure 9. Figure 10 shows the drought results monitored by different MVIs including high ascend, high descend, low ascend and low descend.

Figure 10 Drought monitoring results derived from TMVDI for high and low

frequency ascend and descend pass data

IV. RESULTS AND DISCUSSION

Figure 3 shows the different surface coverage of the whole Sichuan province. In Sichuan basin, the surface vegetation is mainly crop and on the western plateau, grassland is the main surface coverage. Therefore, figure 8 and figure 9 shows the different scatter plot of MVI and surface temperature. Sichuan basin has a relative higher temperature than western plateau. From table 1, it can be seen that for dry edge, the slopes of the linear regression for Sichuan basin is bigger than for western plateau. For wet edge, it was contrast.

Because the complicate terrain of western plateau that can be seen from figure 3, the drought monitoring results are not as good as the Sichuan basin results. From table 1, it can be seen that the relative coefficients of Sichuan basin are higher than Western Plateau.

For the comparison between high frequency and low frequency results, it can be seen that the low frequency MVI has a wider range and this is because low frequency can penetrate the depth more than high frequency. Therefore, the low frequency MVI can reflect more vegetation information than high frequency.

For the comparison between ascend and descend passes, the drought monitoring results from descending pass data are

more reliable than ascending pass data. This is because MVI was derived on the assumption that the physical temperature of vegetation and soil is equal. However, for the ascending pass data (daily pass), there are difference between the physical temperature of vegetation and soil. The descending pass data (night pass) was used and the temperature errors could be minimized.

The microwave radiometer data has a low resolution (25 km in this paper), therefore, the terrain pattern can not been reflected on the drought monitoring results from TMVDI method. Through comparing figure 4, figure 7 and figure 10, the TMVDI results are more consistent with the SPI results than TVDI. The scatter plot between NDVI and LST in figure 6 is not show the exact NDVI-LST triangle, because the complex terrain and weather condition of Sichuan Province, as a result, the TVDI drought monitoring results are not very good.

Among the TMVDI results, the low descend results are better than other results because low frequency can reflect more woody information than high frequency and descend pass can avoid the temperature error which was analyzed in above paragraph. Acknowledgement: This work was supported by Institute of Arid Meteorology Open Fund (IAM201102)

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[3] Tucker, C. J., “Red and photographic infrared linear combinations for monitoring vegetation”. Remote Sensing of Environment, vol. 8, pp. 127−150, 1979

[4] Kogan, F. N., “Drought of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data”. Bulletin of the American Meteorological Society,Vol. 76, pp. 655−668, 1995

[5] Kogan, F. N., “Application of vegetation index and brightness temperature for drought detection”, Advanced Space Research, vol.15, pp. 91−100,1995

[6] Kogan, F. N., “Operational space technology for global vegetation assessment”, Bulletin of the American Meteorological Society, vol.82, pp. 1949−1964,2001

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