情報地質 第27巻 第2号 106-107頁 2016年
Geoinformatics, vol.27, no.2, pp.106-107, 2016

Crop Monitoring Using Time-series MODIS Data: ApplicationValidation for Mekong Delta Region in South VietnamPhuong Le-Thi*, Phung Phi-Hoang*** Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan. E-mail: ltphuongrs@gmail.com.
** Vietnam Southern Satellite Technology application Center, Vietnam National Satellite Center

Key words ：MODIS, SVM, NDVI, rice crop.
1. INTRODUCTION
The Mekong Delta located in the South Vietnam isthe rice key region and annually produces approximately
half of the country's rice production. The quantity of rice
exported reached approximately five million tons in 2008
and hit the record of 7.72 million tons with value of
US$ 3.546 billion in 2012. Rice production plays acritical role in terms of food supply and export. Moreover,the impacts of climate change and rice diseases are themain factors causing the crop production reduction. For
these reasons, it is necessary to develop a ricemonitoring system to assess rice growing area. Suchquantitative information about rice cropping system
might provide essential information for policy makers todevise better agricultural strategies. Remote sensing has
been recognized as an important data source for crop andvegetation monitoring. MODIS is a key instrument onboard the Terra and Aqua satellites, which views theentire Earth’s surface every 1-2 days with 36 spectral
bands acquired in wavelengths from 0.4to 14.4 m. In
this study, the Normalized Difference Vegetation Index(NDVI) derived from MODIS data was used to classify
the rice area/rice cropping system in Mekong Delta.
NDVI which reflects vegetation greenness and indicatedlevels of healthiness in the vegetation development, isthe most frequently used for crop monitoring andproduction estimation (Ren et al., 2008; Wuttichai
Gunnula et al., 2011). The time series MODIS-NDVI
data have been proved that it can be successfully applied
to investigate vegetation dynamics (Zhang et al., 2003).
There exists a high correlation between crop and NDVI
around the time of maximum green leaf biomass
development (Quarmby et al., 1993). To map thecropping system in the study area, the supervised
Support Vector Machine (SVM) classification method
was performed. This classification is based on statisticallearning theory as proposed by Vanik and Chervonenkis(1971) and widely used as non-parametric classifier.
2. DATA
MODIS/Terra Surface Reflectance 8-day L3 Global
500m SIN Grid V005 (MOD09A1) acquired from Nov
2013 to Dec 2014 were used for rice croppingclassification in the study area. The data has sevenbands at 500 m spatial resolution. The data wereformatted using the Sinusoidal projection. Each
MOD09A1 pixel contains the best possible L2G
observation during an 8-day period as selected on thebasis of high observation coverage, low view angle, theabsence of clouds or cloud shadow. The images werere-projected to the Universal Transverse Mercator
coordinate system (zone 48N). LandUse/LandCover in2012 and statistical data were used for accuracy
assessment.
3. METHOD
3.1. Constructing Smoothed Time-Series NDVI Data
The MODIS images were first re-projected into theUniversal Transverse Mercator coordinate system (zone48N) and were then subset over the study area. TheNDVI index was calculated from these images using thefollowing form:(1)
Where NIR (841 – 876 nn) and RED (620 – 660 nn)
are MODIS band 4, 3 respectively. To construct thetime-series NDVI data, the NDVI for each 8-day MODIS
image was first calculated. These NDVI images werethen stacked into one 8-day composite scene for 2014 rice
cropping seasons. In this study, the wavelet transform
was applied to filter out the noise from NDVI time series
due to cloud cover and impacts from atmospheric. Thewavelet function is defined as:(2)
Where implies a mother wavelet, and x represents
the time-step in the one-dimensional time series overwhich integration is performed (Zhang Shengwei et al.,2011).
The mother waveletis as following:(3)
Where a is the dilation or scaling parameter and b is thetranslation or shifting parameterIn this study, Coiflet wavelet with order 4 was usedbecause it give the best result (among Daubechies andSymlet wavelet functions) for determining regional
characteristics of rice phenology (Chen et al., 2011).
3.2. Image masking
In order to mask out the non-rice area, the image
masking step was implemented to remove water bodies,
urban, forest and, salt fields/aquaculture areas, so that
only cropped-area will be focus on. For forest area, thecondition of NDVI value is greater than 0.7 for at least
14 (8-day composite) MODIS-NDVI images was set. Thewater bodies and salt fields/aquaculture areas wereremoved using threshold of NDVI lower or equal 0.2 forat least 20 (8-day composite) images. And for urban
areas, the NDVI value with condition 0.1 ≤ NDVI ≤ 0.3
was used to mask out urban pixels. After the image
masking step, the remaining areas is cropped areas and107

this area will be used for cropping system classification.
3.3. Classification method
SVM is a well-known supervised classification
method since it can produce more accurate classification
results compared to other traditional classification(Maximum Likelihood, K-mean, Threshold-based). The
SVM algorithm is as below:(4)s are Lagrange multipliers while
Where
is the number of support vectors which aretraining data for which 0 ≤is a user-definedparameter that controls the trade-off between themachine complexity and the number of non-separable
points. The bias b is a scalar computed by using any
support vector.
The training data were first selected based on NDVI
profile for different type of cropping system in the study
area including single, double, triple rice crop andfruit/perennial plants (Figure 1).
1.2
10.8

NDVI

0.6
0.4
0.2
0-0.2
-0.4

305
329
353
933
57
81
105
129
153
177
201
225
249
273
297
321
345

-0.6
Time (Nov 2013 to Dec 2014)

Figure 1: Smoothed NDVI profiles of training samples
4. RESULTThe crop map 2014 in the Mekong Delta, Vietnam
was generated using time-series MODIS-NDVI data.

Figure 2: Crop map 2014 in Mekong Delta, Vietnam

It is shown that the cropping system in the study
area is dominate by rice crop area with two typical
cropping types area double and triple rice crops (Figure2). The fruit/perennial plants are mainly distributed in
Ben Tre province and near main rivers. The
triple-cropped was particularly allocated along thecentral park of the region between two large rivers, due
to the availability of water for crop irrigation. The
single-cropped is practiced during the rainy season andscattered along coastal areas.
The classification map produced from the 2014 data
was compared with ground reference data and showed aclose agreement between two datasets. The overall
accuracy and kappa coefficients were 83.1% and 0.76,respectively.
5. CONCLUSION
This study aimed to explore the applicability oftime-series MODIS data for mapping crop map in
Mekong Delta, Vietnam. It is indicated that theMODIS-NDVI data can be successfully used for crop
monitoring/mapping at regional scale.
The variations in weather and rice diseases often
damaged rice fields and other crops. Moreover, thesatellite data acquired in the wet season were often
contaminated by significant cloud cover and atmosphericinfluences that contributed to the errors of mapping
results. However, the radar images can overcome thecloud problem inherent to optical sensors by using
microwaves that can easily penetrate clouds will beconsidered as a good data for crop monitoring in tropicalregions like Mekong Delta. The rice crop mapping basedon radar imagery could be done in future works toimproved results.
6. REFERENCES
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