Agricultural Drought Monitoring in Dongting Lake Basin by MODIS Data YANG Bo1, Ma Su1, LI Jing1, 2*, LIAO Yufang3, ZHOU Bin2, Claudia Kuenzer4

1. College of Resources and Environmental Sciences & GIS Research Centre, Hunan Normal University, Changsha 410081, China;

2. Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, 310000, China;

3. Climate Center of Hunan Meteorological Bureau, Changsha 410007, China;

4. German Remote Sensing Date Center, Oberpfaffenhofen, 82234, Wessling, Germany

Abstract—some remote sensing drought indices are being

widely used in northern China, but seldom in humid regions of

Southern China. Normalized Difference Vegetation Index (NDVI)

and Land Surface Temperature (LST) retrieved from Moderate

Resolution Imaging Spectroradiometer (MODIS) sensor were

used in Dongting Lake Basin and precipitation data from

Tropical Rainfall Measuring Mission (TRMM) satellite as well. A

multi-sensor remote sensing-based drought index called Drought

Condition Index (DCI) was established, which based on

Vegetation Condition Index(VCI), Temperature Condition Index

（TCI）and Tropical Rainfall Condition Index（TRCI）. Finally,

based on the combination of precipitation and the meteorological

drought composite index (CI) from 97 meteorological stations in

Hunan Province, the correlation was analyzed and results

showed that the linear correlation between DCI and average

monthly rainfall and CI was significant. The coefficient passed

through 0.01 level tests. Furthermore, by using geo-data and

Actuality Map of Land Use, the information of farmland fields

were extracted out with the aid of monthly NDVI from MODIS

data in Dongting Lake Basin, and the spatial distribution maps

of agricultural drought grades were obtained from DCI. Results

showed that spatio-temporal pattern of remote sensing

monitoring was compared with the real drought condition from

the observatories around basin, validity of DCI in drought

evaluation were verified. Agricultural drought monitoring based

on MODIS and multi-sensor remote sensing data has some

promotion and application value.

Keywords：Agricultural Drought; Dongting Lake Basin; Remote

Sensing Monitoring; Drought Condition Index (DCI) This study was supported by the National Natural Science

Foundation of China (No.41171342), the Natural Science

Foundation of Hunan Province (No.10JJ3022), the opening

foundation of university innovation platform in Hunan Province

(No.10K042) and the opening foundation of Institute of Remote

Sensing and Earth Sciences, Hangzhou Normal University (No.

PDKF2010YG08).

Author: Yang Bo (1974- ), Dr. and associate professor, research area

is resources and environment remote sensing, Email:

yb@hunnu.edu.cn

* Corresponding author: Li Jing (1957- ), professor, research area is

resources, environment and disaster remote sensing, Email:

lijing@bnu.edu.cn

Ⅰ. INTRODUCTION Drought is one of the worst natural disasters often occurs

around the world and always spread to a wide range, lasted for a long time, widely influenced agricultural production and human life (Willhite, et al., 2000). The annual average crop field affected by drought in China is about 6.67 million hm2, even reach to 26.67 million hm2 or more in severe drought years. The annual grain losses due to drought are up to 30 million tons (National Bureau of Statistics of China). Under the background of climate change and global warming, drought disaster has gradually increasing (Shi peijun, et al，2006).Five provinces in southwestern China endured severe drought which lasted for six months in 2010, with economic loss more than 35 billion RMB; in the middle and lower Yangtze River catchment occurred severe drought in the spring of 2011, which lead to the 166800 hm2 food shortages. Frequency of drought occurrence and drought-affected area are still increasing, which has become one major issue for the agriculture and rural sustainable development（Shi yafeng, et al., 1995；Wang jingai, et al., 2008）.

Traditional drought monitoring based on limited data obtained from meteorological stations, which represent point locations rather than an area (Richand, et al, 2002; Quiring, et al, 2003; Wei Fengying, 2004).Although spatial interpolation can be applied, high uncertainties may exist for many factors affect the interpolation process such as sample points by capacity, the spatial distribution (Jinyoung, et al., 2010). Remote sensing can be used to extract meteorological or biophysical characteristics of land surface, has the features of timeliness and monitoring the wide range that made it a hot and the leading method in the field of drought monitoring (Liu Zhiming, 2003).

Among many indices based on remote sensing, the Normalized Difference Vegetation Index (NDVI) has been most widely used for drought monitoring. The photosynthetic capacity of vegetation during growing seasons is affected by drought, NDVI can be used to detect drought of vegetation

conditions (Peters, et al., 1991; Tucker & Choudhury, 1987). The annual variations of NDVI are believed dues to both of the weather fluctuation and the change of ecological system elements, on this basis the vegetation condition index (VCI) was designed. VCI can reflect the changes of the climate factors such as precipitation or temperature, widely used in remote sensing drought monitoring, the Temperature Condition Index (TCI) was designed similarly; the Vegetation Health Index (VHI) which combined with VCI and TCI was introduced to assess the stress of vegetation related to both water and temperature (Kogan, 1995b, 1997, 2001). Gao (2007) based on the near infrared (NIR）and shortwave infrared (SWIR) put forward the Normalized Water Index (NDWI); By comparison and analyses, Gu, et al., (2007) found out that the NDWI responses more sensitive to drought than NDVI. They proposed a more sensitive drought indicator, the Normalized Difference Drought Index (NDDI).Wang and Qu (2007) proposed the Normalized Multi-band Drought Indicator (NMDI), which based on the difference between two SWIR channels, which suitable for dense vegetation area. In addition to the vegetation-based remote sensing drought indices, remote sensing rainfall products such as the Tropical Rainfall Measuring Mission (TRMM) monthly rainfall product data can also contribute to drought monitoring. TRMM has the characteristics of the high precision, large area, accurate synchronization. (Li jinggang, 2010; Zang wenbin, 2010; David, 2009).

Most of the existing drought index used for arid/semi-arid regions in northern China, in humid/sub-humid regions in southern China have a certain limitation (Ji, et al, 2003; Wan, et al, 2004).Exploring drought monitoring model based on remote sensing suitable for humid regions could be invaluable for sustainable development of agriculture, ecological, water resources and social economic and other aspects. This article proposed the remote sensing drought monitoring index called DCI, which used remote sensing and GIS technology, and based on VCI, TCI and TRMM, and studied agricultural spring drought monitoring in the Dongting Lake Basin in 2011.

Ⅱ. STUDY AREA AND DATA SOURCES A. Location of Study Area

Dongting lake basin is located in the south of the Yangtze River and to the north of Mt. Nanling, extending from 107°16'-114°17'East and 24°38'-30°26'North, covers an area of 263,000 km2.Dongting lake basin includes the Dongting lake area and four tributaries named Xiang, Zi, Yuan and Li, majority in Hunan Province, part in Hubei, Guangxi, Guizhou and Chongqing, is about 14% area of the Yangtze River

catchment, and Hunan Province accounts for 82.7% of the total area. East, west and south of the Dongting lake basin is surrounded by mountains, opening to the north, constituting a unique “horseshoe” pattern, with complex and diverse topographical features and dense river network; the south is poly spokes (fan) river pattern. Most area belongs to the typical subtropical monsoon climate zone (Fig1).

The Dongting lake basin has humid climate, rich in solar energy, superior natural condition, and the agricultural production has a long history, which is an important base for food production in southern China. However, region of the main rice production occurred drought in recent years, the rice yield decreased, has an adverse effect on the steady and sustainable development of the food production in China. Therefore, to effectively evaluate damage of drought for rice production, and provide scientific basis for rice yield prediction, it is very necessary to conduct timely, effective and dynamic rice drought assessment.

Fig.1 Location of the Dongting Lake Basin

B. Data Analyses Moderate Resolution Imaging Spectroradiometer

(MODIS) data and in situ investigation data were used in this study. MODIS was the main data sources for moderate spectral and spatial resolution and can be free download.

Table 1 Description of Remote Sensing data products

Data Product Product

level

Spatial

resolution

Time

resolution

MOD09A1 Surface reflectance L3 500m 8days

MOD11A2 LST L3 1km 8 days

MOD13A3 Vegetation index L3 1km month

TRMM3B43 precipitation L3 0.25° month

MODIS were free downloaded from the Warehouse Inventory Search Tool (http://Wist.echo.nasa.gov/api/, WIST). The Dongting Lake Basin covers three tiles h27v05, h27v06 and h28v06.MOD11A2 is land surface temperature products

with time resolution of eight-day composition and 1km spatial resolution, including a daytime and nighttime land surface temperature product, surface emissivity and so on. MOD13A3 is vegetation index products, including NDVI, Enhanced Vegetation Index (EVI), reflectance in Visible (VIS) light, Near Infrared (NIR), SWIR and so on. Tropical Rainfall Measurement Mission (TRMM) delivers monthly rainfall product data (level 3, collection V006) with 0.25° resolution from China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/). MOD11A2, MOD13A3 and TRMM 3B43 for the twelve-year period of spring (4-5 months) in 2000-2011 were used. In addition to the remote sensing data, the meteorological drought composite index (CI) and precipitation data from 97 meteorological stations in Hunan Province and other auxiliary data such as statistical data and land use map were used as well. C. Data Processing

Drought composite index (CI) can be obtained from standardized precipitation index (SPI) in 30 days (nearly monthly scale) and 90 days (nearly seasonal scale), and relative humidity index (RHI) in nearly 30 days. CI also considers factors of precipitation and evaporation, and it has great advantages compared to the drought index which only simply considered precipitation (Zou XuKai, 2010). Details of CI calculation can be seen in state standard of meteorological drought level GB/T20481-2006 (zhang qiang, 2006).

MODIS Reprojection Tools (MRT) was developed by NASA and used for format and projection transformation, HDF format converted to TIFF format, convert projection from the SIN projection to the WGS84/Geographic projection, mosaic and resample simultaneously. The Dongting Lake basin was subset by vector boundary. The maximum value compositing (MVC) was used to merge Land Surface Temperature (LST) values from 8 consecutive days to get LST in every month. MVC could effectively eliminate the effects of the sun elevation angle, satellite perspective, path drifting and clouds shadowing (Holben, et al., 1986).In addition, convert the projection of TRMM rainfall data and match to that of MODIS in ENVI, and pixel resampling in order to combine with other remote sensing data (Fig2).

Ⅲ. RESEARCH METHODS AND TECHNOLOGIES A. Research Flow

Soil moisture is an important indicator for agricultural drought monitoring, vegetation status and surface temperature can reflect crop water stress condition in different degree. In the RED and NIR bands, remote sensing drought monitoring focus on the vegetation index changes, its theoretical basis on the difference between the maximum absorption of the

radiation in the red spectral region and the maximum reflectance in the near infrared spectral region. LST can be retrieved from thermal infrared bands. The land surface temperature is an index of the earth surface energy balance and greenhouse effect, bared soil called soil temperature, vegetation called canopy temperature. Change of soil temperature reflects the soil moisture, and the rise of the canopy temperature shows vegetation affected by water stress. Therefore, monitoring soil moisture by LST is useful.

Fig 2 Flow chart of Remote Sensing drought monitoring

At the same time, drought occurrences have great relationship with the rainfall, Tropical rainfall satellite TRMM can make up the limitation of less density of rain-gauge stations and ground-based precipitation radar stations, which is helpful for further study of spatial and temporal variations of rainfall (FuYunFei, 2008; Fu yong, et al, 2003), suitable for auxiliary analysis of agricultural drought. B. Modeling

Sandholt described NDVI as a water stress indicator had time lags between drought occurrences at some consecutive time periods, and Goetz found that temperature as a water stress indicator timely. The methods that combine vegetation index and land surface temperature provide a better characterization of surface drought status, contributing to study the spatio-temporal distribution and evolution of the drought accurately and efficiently. Subsequently, combine VCI, TCI and TRCI altogether, a new drought monitoring model named drought condition index (DCI) were set up.

(1) Vegetation condition index (VCI) According to vegetation condition index(VCI) designed

by Kogan (1995), from monthly time series NDVI to get the absolute maximum and minimum NDVI in 2000-2011, the

procedure was formalized by Eqs.(1):

)() minmaxmin( NDVINDVINDVINDVI iVCI i −−= (1)

Where NDVIi is the monthly normalized difference vegetation index; NDVImax and NDVImin are the multi-year absolute maximum and minimum NDVI respectively. NDVImax-NDVImin represents for the biggest change of NDVI for the period of analysis, reflecting the local vegetation condition. The smaller is VCI, the poorer growing crops.

(2) Temperature Condition Index (TCI) Kogan developed Temperature Condition Index (TCI).

)()( minmaxmax LSTLSTLSTiLSTTCIi −−= (2)

Where TCIi is the monthly temperature condition index, LSTmax and LSTmin are the absolute maximum and minimum LST from annual time series respectively. The smaller is TCI, the more serious drought.

(3)Rainfall Condition Index (TRCI) TRCI was computed from the TRMM rainfall data,

according to the VCI and TCI.

)()( minmaxmin TRMMTRMMTRMMTRMMiTRCIi −−= (3)

Where TRCIi is the monthly rainfall, TRMMmax and TRMMmin are the maximum and minimum from annual time series. The smaller is TRCI, the less rainfall.

（4）Drought Condition Index（DCI） Taking into account of three factors above, Drought

Condition Index (DCI) were designed as follows:

TCIcTRCIbVCIDCI ×+×+×= a (4)

Where a, b and c are coefficients quantifying a share of VCI(Vegetation Condition Index), TRCI（Rainfall Condition Index）and TCI(Temperature Condition Index) contribution in the total DCI. The DCI was calculated from NDVI-based Vegetation Condition Index(VCI), Rainfall-based Rainfall Condition Index （ TRCI ） and LST-based Temperature Condition Index (TCI) from 2000-2010. C. Determine of Weight Coefficients

Drought condition index model need to solve at least two main questions: whether the parameters in the model have a significant contribution to the drought index? How many contributions to the drought condition index of parameters, namely weights of parameters in the model? Using data obtained in September 2009 as experimental data, a, b and c, respectively, by 1/n step length to compute DCI, calculating DCI and VCI, TCI, DCI TRCI, NDVI, LST, TRMM correlation coefficient (Table 2, only list out 10 combinations with higher correlation coefficient), to confirm parameters

with significant contributions to DCI. From table 2 we can see that the correlation coefficients of VCI, TCI, TRCI and DCI are more remarkable than that of NDVI, LST, TRMM and DCI. Therefore, VCI, TCI and TRCI as modeling factors for remote sensing agricultural drought monitoring is more reasonable than that of NDVI, LST and TRMM.

The weight coefficients a, b and c have three combinations respectively of 1/3, 1/3, 1/3; 1/4, 2/4, 1/4 and 1/5, 2/5, 2/5, the correlation coefficients of VCI, TCI, TRCI and DCI greater than or equal to 0.6 in table 2, showing that each model parameters of the three combinations has a greater contribution to DCI, effect of module is best of all; the correlation coefficients decreases when instead of the weights in other values, and the contributions to DCI weakened.

TCITRCIVCIDCI ×+×+×= )3/1()3/1()3/1(1 (5)

TCITRCIVCIDCI ×+×+×= )4/1()4/2()4/1(2 (6)

TCITRCIVCIDCI ×+×+×= )5/2()5/2()5/1(3 (7)

Table 2 correlations of DCI and parameters

Pearson a,b,c NDVI LST TRMM VCI TCI TRCI

1/2,1/4,1/4 0.535 ﹣0.288 0.19 0.784 0.359 0.313

1/3,1/3,1/3 0.450 ﹣0.334 0.341 0.683 0.629 0.607

1/4,2/4,1/4 0.400 ﹣0.275 0.513 0.607 0.605 0.763

1/5,2/5,1/5 0.324 ﹣0.302 0.466 0.647 0.642 0.644

1/6,4/6,1/6 0.295 ﹣0.191 0.626 0.201 0.250 0.923

1/7,3/7,3/7 0.258 ﹣0.266 0.506 0.230 0.672 0.685

1/8,1/8,6/8 0.109 ﹣0.299 0.226 0.204 0.800 0.213

1/9,4/9,4/9 0.191 ﹣0.224 0.538 0.164 0.633 0.702

1/10，4/10,5/10 0.172 ﹣0.185 0.618 0.065 0.524 0.848

To eliminate errors from spatial location, using Neighborhood Statistics of ArcGIS, pixels on each meteorological station as center pixels, and selecting convolution kernel size of 3*3 pixels respectively, to calculate and obtain the average value of indices as the meteorological data from corresponding values of meteorological stations. Later based on statistical software SPSS, to analyze correlation coefficients of observation values from 97 meteorological stations and their corresponding indices (Table 3). From table 3, it is known that the Pearson correlation coefficient of integrated parameter VCI, TCI, TRCI and CI, Average Monthly rainfall (AMR) is more remarkable than single parameters of NDVI, LST and TRMM, which shown the rationality of modeling by VCI, TCI and TRCI.

Using three combinations that have the high correlations, mapping drought category in September 2009, statistically

analyze drought endured areas, when a =1/5, b=2/5, c=2/5, the drought areas results from remote sensing drought monitoring were closest to that from agricultural statistics. Based on comprehensive analyses above, the DCI3 was the suitable drought index in the Dongting Lake Basin.

Furthermore, comprehensively analyzed agricultural drought hazard, drought bulletin, drought occurrences of survey data in Hunan province and remote sensing drought monitoring, comparing with the category of CI, the threshold value of drought occurrence classes were obtained (Table 4).

Table3 Correlation coefficients between RS and meteorological parameters

Pearson CI AMR

VCI 0.515** 0.519**

TCI 0.643** 0.661**

TRCI 0.658** 0.791**

NDVI 0.243* 0.217*

LST ﹣0.537** ﹣0.544**

TRMM 0.595** 0.674**

DCI1 0.669** 0.733**

DCI2 0.678** 0.751**

DCI3 0.717** 0.776**

Note: **0.05 significant level(two-tailed t test)；** 0.01 significant level (two-tailed t test)

Table4 Grading index of drought monitor by Remote Sensing

Drought categories DCI

Exceptional Drought 0≤DCI≤0.06

Severe Drought 0.06＜DCI�0.12

Moderate Drought 0.12＜DCI�0.17

Mild Drought 0.17＜DCI�0.23

Normal 0.23＜DCI�1

Ⅳ. ANALYSES OF RESULTS The normalized difference vegetation index (NDVI),

enhanced normalized difference vegetation index (EVI) and land surface water index (LSWI) were computed by using of surface reflectance values from the BLUE, RED, NIR (841-875nm), and SWIR(1628-1652nm) bands, those can be obtained from the production of MODIS09A1.

)()( nir ρρρρ rednirredNDVI +−= (8)

)15.76(5.2 nir +×−×+−×= ρρρρρ bluerednirredEVI ）（ (9)

)()( ρρρρ swirnirswirnirLSWI +−= (10)

According to phenological calendar of rice, to identify rice fields by typical characteristics in three crucial periods: transplanting period, growth period and harvest period. During the transplanting period, the land surface is the

mixture of green rice plants and surface water. Spectral bands or the vegetation index are sensitive to both water and vegetation such as NDVI and LSWI to identify rice fields. 50 to 60 days after transplanting, rice canopies cover most of the surface area, NDVI and EVI rise gradually. At the end of the growth period, there is a decrease of leaf chlorophyll and a decrease of the number of green leaves, NDVI and EVI value also gradually reduced. The seasonal dynamics of NDVI, EVI, and LSWI (Fig3), Fig4 is rice distribution map of Dongting lake basin in 2009. The specific procedures are as follows: (1)Rice fields were selected by GPS as training samples. (2)Mask region with elevation greater than 400m by using of digital elevation data (DEM).for 400m above the sea level in the Dongting Lake Basin, non-rice cultivation. (3)Identification of rice fields according to rice growth and spectral characteristics: (LSWI+0.05)>EVI, LSWI>0.12 and EVI<0.25 in transplanting period, and in the following 1-3months of growth period, EVI> 0.35.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

97 113 129 145 161 177 193 209 225 241 257 273

Time（8-day interval）

Vegetation indices

EVI

LSWI

LSWI+0.05

Fig3 Spectral characteristics of rice in Hunan Province

Due to the El nino, less precipitation and serious drought occurred in the Yangtze River Catchment especially in the Dongting Lake Basin in 2009. It was the vital stage for Double cropping rice and the harvest period of Single cropping rice, and the shell-rate was increased by less water and the high temperature in September. Drought lasted to October caused the rice crop yield decreased for lack of water supply, finally relieved in November.

Fig4 Distribution map of rice fields in Dongting Lake Basin, 2009

Severe drought happened as the cumulative precipitation was insufficient in September, 2009, which expanded in October and relieved in the next month as Fig5.

(a) September, 2009 (b) October, 2009

(c) November, 2009

Fig5 Autumn drought monitoring for rice fields in Dongting Lake Basin, 2009

The average precipitations of Hunan, Hubei, Guangxi, Guizhou and Chongqing in 2009 were the minimum in 1951-2009. Guizhou, Hunan, Chongqing were fifty to seventy percent less than normal years. Severe drought was caused by the high temperature and less rain in the Dongting lake basin.

Part of the reservoirs and pools were dry up and living water for residents and agricultural production also being affected. Drought happened in most parts of the river basin for the precipitation, the status was continued in October. There was a rainfall in the middle of Xiang River and the downstream of Yuan and Li in November, drought relieved. The trend of drought development in the Dongting lake basin in 2009 can be seen in Table5.

Tab5.The development trend of autumn drought of Hunan Province in 2009

District September,2009 October,2009 November,2009

Zhangjiajie Severe drought === ---

Yueyang Severe drought === ---

Jishou Severe drought === ---

Changde Moderate drought +++ ---

Yiyang Severe drought +++ ---

Changsha Extreme drought === ---

Huaihua Extreme drought +++ ---

Loudi Exceptional drought === ---

Shaoyang Exceptional drought === ---

Xiangtan Moderate drought +++ ---

Zhuzhou Moderate drought +++ ---

Yongzhou Extreme drought --- ---

+++ Increase; ===Maintain or without change; --- Relief The remote sensing drought monitoring results of rice

could be used to reflect the spatio-temporal distribution characteristics of Dongting lake basin. It shows that the vegetation index, temperature conditions and rainfall data could be used to establish the remote sensing monitoring model, and dynamic changes of the rice drought can be monitored effectively.

Ⅴ. CONCLUSIONS AND EXPECTATION A. Conclusions

(1)Vegetation Condition Index (VCI), Temperature Condition Index（TCI）and Tropical Rainfall Condition Index（TRCI）were used to establish Drought Condition Index (DCI) model, and the higher weight were assigned to the TCI and TRCI in the DCI model. The reasonable of DCI for drought monitoring was evaluated in Lake Dongting basin, using data in 2009. Results showed that DCI was the better promising method for agricultural drought monitoring in South China.

(2)Rice field extraction and drought dynamic monitoring with DCI model in the Dongting Lake basin is useful. B. Discussion

(1)Vegetation Index obtained from MODIS data now widely used has a lot of shortcomings, such as NDVI can reflect the vegetation growth condition, and the vegetation subjected to water stress, the short term can be still green, so in time there is a lag, it cannot reflect the sudden drought events; NDVI easy to saturated, other vegetation index such as Leaf Area Index (LAI) need to be explored for better agricultural drought quantitative monitoring.

(2) 8-day composite LST were calculated from the split window which obtained from linear combination of brightness temperature of MODIS band 31 and band 32 .Because of MODIS is the optical sensor, surface radiation can't penetrate the clouds, sometimes it can't get accurate surface temperature information, affecting the accuracy of drought monitoring .

(3) Overall results indicated that remote sensing data from TRMM can provide valuable information on drought conditions despite its coarse spatial resolution.

Each index has certain limitation, the combination of different indices; make remote sensing drought monitoring research further improved. By experimental conditions, data and time limit, it is necessary to conduct extensive experiment and verify the drought monitoring model proposed above in the future, such as the use of different areas, different season, different parameters, different time span, and classification of parameters and the corrections of the weights.

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