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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
3. RESEARCH DROUGHT MONITORING IN 
CHONGQING WITH REMOTE SENSING 
3.1 The Status of Drought Monitoring with Remote 
Sensing 
The agriculture drought indicators in China are generally 
divided into three categories: precipitation index, soil moisture 
indicator, water balance indicator. Soil moisture is one of the 
important drought indicators. It is the major parameter in 
climate, ecological, agricultural areas. Drought often happens in 
large areas and lasts for a period of time, it is easy to cause 
serious losses. With the development of remote sensing 
technology, multiphase, multi-spectral of remote sensing data 
may provide information related with surface soil moisture and. 
It can be used to evaluate soil water conditions and monitor 
agriculture drought. 
NOAA/AVHRR (Kogan.F, 1997), Landsat TM were often used 
for drought monitoring. Now, MODIS plays an important role 
in agriculture drought monitoring. The following are the major 
methods. 
i) Thermal inertia approach. It usually applies to bare land or 
low vegetation coverage area for remote sensing drought 
monitoring. 
ii) Vegetation supply water index. This method is particularly 
suitable for vegetation coverage area or the abundance of 
vegetation in better. 
iii) Energy index model. It is more suitable for soil moisture 
monitoring. 
iv) Temperature vegetation drought index. This method has 
been used in regional drought monitoring and has achieved very 
good results. And so is water stress index (WDI). 
3.2 Soil Moisture Index 
Soil moisture is an important index in Chongqing agriculture 
drought monitoring. There are 170 soil moisture (SI) 
observation points in Chongqing city. SI is observed from 
March to October on every 3rd and 8th. The data used in our 
experiment was August 4 to 9 2006, which was supported by 
the Chongqing Municipal Climate Center. Drought degrees are 
as follows: Soil relative humidity ^ 30%: special drought; 30 
to 40 percent: heavy drought; 40 to 50 percent: moderate 
drought; 50 to 60 percent: Light drought; 60 to 90 percent: 
suitable or basic suitable; > 90%: too wet. 
3.2.1 The Necessity of Researching Soil Moisture Index 
Since a MODIS station has been constructed in Chongqing city. 
It is convenient to use MODIS as a remote sensing data 
resource in agriculture drought monitoring in Chongqing. The 
greatest advantage of remote sensing drought monitoring is its 
real-time, fast, and objective. The greatest disadvantage is lack 
of ground verification. So it is necessary to combine weather 
observation, the ground truth observation and remote sensing 
information together to fulfil accurate soil moisture monitoring. 
3.2.2 Affecting Factors of Soil Moisture 
The major factor of agricultural drought monitoring is the crop 
water stress. So, the growth of crops and temperature (surface 
temperature, including the canopy temperature) are important 
indicators. The expression of soil moisture function can be 
expressed as the following function: 
SMI = f (NDVI, LST) 
(3-1) 
Vegetation index is used by the most researchers, and it is the 
factor in operational work. Many scholars research a variety of 
vegetation indexes. After analyzing and comparing the 
vegetation indexes, the test with MODIS data from July to 
August in Chongqing city shows that there is no saturation in 
normalized difference vegetation index (NDVI), so we decide 
to use NDVI as vegetation index. 
3.3 Soil Moisture Retrieval Practice in Chongqing Summer 
of 2006 
Soil moisture retrieval formula is deduced by regression of 
remote sensing data and ground observation data: 
SMI = 0.856x NDVIx -X > — -0.0189 
LST 
(3-2) 
Where LST is land surface temperature (K). NDVI is obtained 
by reflectivity of MODIS band 1 and band 2. 
NDVI =——— 
K+r. 
(3-3) 
Where r j, r 2 are reflectivity of MODIS band 1 and band 2. 
Figure 3-1 is Chongqing NDVI map on August 7 th , 2006, 
without removing cloud area. The cloud areas were removed in 
soil moisture calculation. 
The results are shown in Figure 3-2 and Figure 3-3: 
Figure 3-2 is the retrieval soil moisture map. Each pixel 
corresponds quantitative soil moisture. The region with value of 
0 is cloud area and not involved in the calculation of moisture. 
Figure 3-3 is the classification of soil moisture map basing on 
Figure 3-2. Regions 1, 2, 3 and 4 denote special heavy drought, 
heavy drought, moderate drought and slight drought. In our 
study the soil moisture only referred to the moisture of 0-10cm 
below the land surface. 
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