rice identification and change detection using terrasar-x data
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This article was downloaded by: [Gazi University]On: 18 August 2014, At: 13:09Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
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Rice identification and change detection usingTerraSAR-X dataZhiyuan Peia, Songling Zhanga, Lin Guoa, Heather McNairnb, Jiali Shangb & Xianfeng Jiaob
a Chinese Academy of Agriculture Engineering, Ministry of Agriculture of China, Beijing100125, Chinab Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON KIA OC6, CanadaPublished online: 02 Jun 2014.
To cite this article: Zhiyuan Pei, Songling Zhang, Lin Guo, Heather McNairn, Jiali Shang & Xianfeng Jiao (2011) Riceidentification and change detection using TerraSAR-X data, Canadian Journal of Remote Sensing: Journal canadien detélédétection, 37:1, 151-156, DOI: 10.5589/m11-025
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Rice identification and change detection usingTerraSAR-X data
Zhiyuan Pei, Songling Zhang, Lin Guo, Heather McNairn, Jiali Shang, and Xianfeng Jiao
Abstract. Rice is the staple grain in China and accounts for about 42% of the nation’s food production. Most of China’s
paddy rice production is located in the southern provinces of the country where cloud cover and frequent rain severely
limit opportunities for optical satellite acquisitions. The small field sizes, typical of paddy rice, also challenge the
exploitation of satellite data for monitoring rice production. Synthetic aperture radar (SAR) sensors are able to
successfully acquire data under most atmospheric conditions, and the change in backscatter, from rice emergence through
to crop maturity and harvest, permits the detection of rice fields using SAR imagery. Recently launched sensors, including
TerraSAR-X, can provide data at spatial resolutions suitable for rice monitoring in southern China. The objective of this
study was to assess TerraSAR-X imagery for identification of late rice and to develop a change detection methodology to
quantify changes in rice acreages. The lowlands of the Xuwen study site in Guangdong Province are dominated by rice
paddies. Results of this analysis revealed that the TerraSAR-X data were able to identify rice paddies with a 96% accuracy
and acreage change to an accuracy of 99%.
Resume. Le riz est la cereale de base en Chine et represente 42 % de la production alimentaire du pays. La majorite de la
production de riz est localisee dans les provinces du sud du pays, ou le couvert nuageux et les pluies frequentes limitent
considerablement les possibilites d’acquisition de donnees optiques satellitaires. La dimension reduite typique des rizieres
pose egalement un defi a l’exploitation des donnees satellite pour le suivi de la production du riz. Les capteurs radar
a synthese d’ouverture (RSO) permettent d’acquerir des donnees dans la majorite des conditions atmospheriques et le
changement dans la retrodiffusion du riz, du stade d’emergence jusqu’au stade de maturite et de la recolte, permet de
detecter les rizieres a l’aide des donnees radar. Les capteurs lances recemment, incluant TerraSAR-X, peuvent fournir des
donnees a des resolutions spatiales propices pour le suivi du riz dans le sud de la Chine. L’objectif de cette etude
etait d’evaluer les images de TerraSAR-X pour l’identification du riz tardif et de developper une methodologie de
detection du changement permettant de quantifier les changements dans les superficies rizicoles. Les terres basses du
site d’etude de Xuwen, dans la province de Guangdong, sont dominees par des rizieres. Les resultats de cette analyse ont
revele que les donnees de TerraSAR-X permettaient d’identifier les rizieres avec une precision de 96 % et le changement
dans les surfaces rizicoles avec une precision de 99 %.
[Traduit par la Redaction]
Introduction
Although rice has accounted for less than 30% of the
grain-planted acreage in China over the past decade, this
crop has contributed to more than 40% of grain production.
Rice has the highest production and acreage of all grain
crops (available (in Chinese) from http://www.zzys.gov.cn).
Rice acreages fluctuate annually because of agricultural
policies, rice markets, and environmental impacts such as
water resource limitations. Accurate annual statistics on rice
acreages are vital for sound agricultural decisions and
environmental protection.
Multispectral optical satellite imagery is capable of provi-
ding rice acreage information. Landsat Thematic Mapper
(TM) and Satellite pour l’Observation de la Terre (SPOT)
data have classified rice crops at accuracies above 80%
(Fang, 1998; Turner and Congalton, 1998). But with 85%
of rice production located in southern China, the high
frequency of cloud cover and heavy precipitation impede
the use of optical imagery for annual monitoring of rice
production. In an attempt to overcome these challenges,
some studies have assessed sensors, such as Advanced Very
High Resolution Radiometer (AVHRR), Moderate Resolu-
tion Imaging Spectroradiometer (MODIS), and SPOT-
VEGETATION, that provide daily observations (Huang,
1998; Kamthonkiat et al., 2005; Xiao et al., 2005). However,
the coarse resolution of this class of sensors negatively
impacts accuracies given the small dimensions of rice paddies
typical of southern China.With their ability to acquire data regardless of cloud
cover, synthetic aperture radar (SAR) sensors are well
suited for rice mapping in regions such as southern China.
Received 21 June 2010. Accepted 6 October 2010. Published on the Web at http://pubs.casi.ca/journal/cjrs on 4 October 2011.
Z. Pei1, S. Zhang, and L. Guo. Chinese Academy of Agriculture Engineering, Ministry of Agriculture of China, Beijing 100125, China.
H. McNairn, J. Shang, and X. Jiao. Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON KIA OC6, Canada.
1Corresponding author (e-mail: [email protected]).
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The sensitivity of radar backscatter to the phenology of
rice growth has been well documented, in particular with
C-band SAR sensors. Le Toan et al. (1997) established that
rice fields can be accurately mapped based on the temporal
variation in radar backscatter using ERS-1 data. Ribbes
(1999) demonstrated similar results using RADARSAT-1
data. Shao et al. (2001) used multitemporal RADARSAT-1
data and a neural network classifier to map rice in China.
The accuracy of the rice classification was 91% and then
was 97% after postclassification filtering. Li et al. (2003)
successfully mapped land cover over a site in China with
a supervised classification of a RADARSAT-1 ScanSAR
image. The average accuracy for identifying rice fields
was 90%. Choudhury and Chakraborty (2006), used multi-
temporal RADARSAT ScanSAR (SCNB) data and a
knowledge-based decision rule classifier to achieve �98%
accuracy in mapping rice. Chen and McNairn (2006) used
multitemporal RADARSAT-1 fine mode images, with an
integrated change detection and neural network approach, to
delineate rice production areas. A minimum mapping
accuracy of 96% was achieved.
Other recently launched SAR sensors provide data with
polarization diversity. Chen et al. (2007) used Envisat
Advanced Synthetic Aperture Radar (ASAR) HH and HV
polarization data to map rice fields in southern China with
an accuracy of 81%. Yang et al. (2008) chose ASAR dual
like-polarization (VV/HH) data with a threshold classifica-
tion method to achieve an accuracy of 84%
for rice identification. While most research has evaluated
C-band satellite data, some results have been reported
with X- and L-band data. Inoue et al. (2002), using a
ground-based scatterometer, found that lower frequency
(L-band) microwaves provided deeper penetration into the
rice canopy. High frequency (X-band) data were signifi-
cantly correlated with the weight of rice heads and were
sensitive enough to detect thin rice seedlings just after
transplanting. Kim et al. (2008) also analyzed scatterometer
data and reported that biomass was correlated with L-HH
backscatter at a large incident angle. Leaf area index was
highly correlated with C-band HH and HV polarizations,
while grain weight was correlated with X-VV backscatter.
Zhang et al. (2009) used Advanced Land Observa-
tion Satellite (ALOS) L-band synthetic aperture radar
(PALSAR) images and a support vector machine classifier
to map rice and other land uses at user’s and producer’s
accuracies of 90% and 76%, respectively.
The TerraSAR-X satellite, launched in 2007, provides
data at spatial resolutions suitable for monitoring China’s
southern rice-producing regions. This satellite acquires data
at X-band (3.1 cm) in one of three modes including Spot-
Light (1 m pixel spacing), StripMap (3 m spacing) and
ScanSAR (18 m spacing). Scatterometer studies have
demonstrated the sensitivity of X-band data to rice even at
this crop’s early developmental stages. The objective of this
study was to assess TerraSAR-X imagery for identification
of late rice and to develop a change detection methodology
to quantify changes in rice acreages. This approach, which
quantifies annual changes in rice acreage, would support the
activities of China’s Ministry of Agriculture for operational
monitoring of rice production.
Site description
The Xuwen study site is located close to Leizhou City,
Guangdong Province, China (20.888N, 110.78E). The re-
search study site covered approximately 15 km�15 km,
and could be imaged using a single TerraSAR-X stripmap
acquisition. Topographic relief in this region is low with
an elevation of B25 m (above mean sea level). Xuwen falls
in the tropical ocean monsoon climate zone with a mean
annual temperature of 238C and annual precipitation of1400�1700 mm. The rainy season spans from May to
October with heavy storms occurring mainly in September.
The climate of the Xuwen region supports two rice
growing seasons per year. Although rice production dom-
inates, other crops are also present including sugar cane,
corn, legume, vegetables, and some aquatic plants. The early
rice season begins in late March and ends in mid June. For
late rice, seeding takes place in early August with harvestingapproximately 3 months later in early November. During
the rainy season, rice paddies experience periodic flooding
followed by periods of drying. Seeding methods vary among
paddies and include machine sowing as well as manual
approaches such as direct hand planting and manual broad-
casting of seeds. Nonrice crops are often planted along
with rice in the same field. The availability of water and
nutrients will result in variable rice growth among fields. Ricepaddies also vary in size, but within this study site the field
dimensions were on average approximately 30 m�100 m.
This field size limits the suitability of some SAR sensors for
rice mapping in this region of China.
Data acquisition
Two TerraSAR-X acquisitions were programmed for
the late rice growing season, the first acquisition on
21 September 2008 and the second on 19 September 2009.
Timing of these acquisitions coincided with the period ofpeak growth, with rice crops at the heading and flowering
stage. Data were collected in stripmap dual-polarization
mode with VV�VH polarizations. The acquisitions were
planned as exact repeats with an incidence angle of 358in an ascending orbit. Nominal spatial resolution of the
stripmap product is 6 m. This resolution is well suited to the
dimensions of the paddy fields typical of this study site.
Approximately 15 000 ha of rice paddies are found with-in the Xuwen study site. Ground surveys were conducted
close to the satellite acquisition dates. Sixty-one fields
were surveyed in 2008, and 65 fields were surveyed in
2009. Details of the field distributions between rice and
nonrice crops are listed in Table 1. The location of each
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field was recorded using a differential GPS with submeteraccuracy. These ground control points were required to
support ortho-correction of the imagery. Crop type was
recorded for each field surveyed. For rice paddies, a
measurement of crop height was taken and the phenological
growth stage was also recorded.
The field survey information was used to train image
interpreters to visually classify crop types from satellite
imagery. Visual interpretation of optical imagery is astandard approach of The Ministry of Agriculture of China
to support annual reporting of crop acreages. For the 2008
and 2009 late rice season, no cloud-free optical imagery was
available over the Xuwen study site. Therefore TerraSAR-X
images were used and the result of this visual interpretation
was a 2008 and 2009 thematic map of rice paddies for the
entire study site. These maps were used as reference for
assessing the accuracy of the rice acreage change analysisfrom the TerraSAR-X data.
Methods
In this study, the change detection methodology was aided
by the acquisition of exact repeats of TerraSAR-X, almost
exactly 1 year apart. This approach minimized variances in
crop phenology between these two image acquisitions and
any sensor related differences. Image processing details are
illustrated in Figure 1.
Speckle is an inherent phenomenon for coherent systemssuch as SARs. To suppress speckle, the imagery was filtered
using a 3�3 Lee filter. TerraSAR-X data were then converted
to ß8 (dB). Ortho-correction was performed on both images
using ground control points, DEM, and satellite ephemeris
information. A root mean square error under 1 pixel was
achieved.
The change detection approach used a visual interpreta-
tion of the backscatter response from both the 2008 and2009 images, as well as the difference in backscatter responses
from one year to the next. Such an approach requires the
representation of the maximum amount of information in
a three-channel display. To accomplish this, a principal
component transformation (PCA) was applied to each
image. The first PCA channel (PC-Y1 for 2008 and PC-Y2
for 2009) contained most of the information for both
polarizations (VV and VH). The third channel containedthe difference between the response of the first principal
component of the first (2008) and second (2009) TerraSAR-X
images. For ease of visual display, the PCA images were
scaled to 8-bit. This three-channel composite is displayed in
Figure 2; orange and cyan denote fields where rice was
grown in only one of the two years. Orange-coloured fields
were planted with late rice in 2008 but not in 2009.
Conversely cyan identifies fields where late rice was planted
in 2009 but not in 2008.
An object-based approach was adopted to aid with visual
image classification and to assist with quantitative deter-
mination of acreage change (Benz et al., 2004; Geneletti and
Gorte, 2003). Image segmentation was applied to the three-
channel image in order to clearly demarcate parcels (objects)
of homogeneity. Segmentation was completed using eCogni-
tion (Definiens Imaging, GmbH, Munich, Germany). Next,
within eCognition, decision rules were set to delineate areas
where change took place between 2008 and 2009 and areas
where no change occurred (Figure 3). No change may be
due to the existence of rice in either both years or in neither
year. Where change did occur, decision rules were set to
identify where rice was planted in one year but not the
other. Decision rules (Table 2) consisted of two compo-
nents: feature selection and threshold determination of each
feature. First, spectral feature images (such as brightness,
variance, and hue) and shape feature images (such as
area, length and width, and ellipticity similarity) were
obtained using eCognition. Brightness, variance, maximum
brightness difference, ellipticity similarity, and hue were
selected as classification features through visual comparative
Figure 1. TerraSAR-X data processing flow chart.
Table 1. Field survey distribution among crops in 2008 and 2009
seasons over Xuwen.
Year
No. of
rice fields
Fields of
other crops
Total no. of
fields surveyed
2008 60 1 61
2009 65 3 68
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analysis. Next, based on field survey data, thresholds of
each feature were extracted and decision rules were set
to classify the image. Classification results and field sur-
vey data were analyzed and compared through human�computer interaction.
Results and discussion
The resultant rice change map between 2008 and 2009 is
shown in Figure 4. Accuracies of the change analysis were
tested using two approaches.
Accuracy assessment of rice identification
The crop maps derived from visual interpretation were
validated using field survey data (Table 1). In 2008, a total
of 61 fields were used as test fields. An overall accuracy
of 98.8% was achieved (Table 3). For the 2009 late
rice identification, a total of 68 fields were used as
validation. An overall accuracy of 96.2% was reached
(Table 3). As reported by Inoue et al. (2002), high frequency
X-band microwaves are very sensitive to the rice heads. This
sensitivity may explain the success at identifying rice crops
in Xuwen, at a time when the rice crops were in their head-
ing and flowering growth stage. The high accuracies of
Figure 3. Flow chart of decision rules.
Table 2. Image classification decision rules.
Classes Decision Rules
Area with change
in rice planting
Rice planted in
2008 No rice
planted in 2009
Rule no. 1:
Brightness: PC-Y1 B150,
jPY1-PY2j B80
Variance: 0.7�3.0
Ellipticity similarity:
0.2�1.0
Rice planted in
2009
No rice
planted in
2008
Rule no. 2:
Brightness: PC-Y2 �54,
150 BjPY1-PY2j B239
Variance: 0.38�1.0
Ellipticity similarity:
0.2�1.0
Area with
no change
Rice planted in
both years
Rule no. 3:
Difference: 0�0.66
Hue: 0.39�0.62
No rice planted
in either year
Other
Figure 4. Changes in the regions of late rice grown between the
2008 and 2009 seasons.
Figure 2. Colour composite constructed using 2008 and 2009
PCA channels where red represents PC-Y2, green represents PC-
Y1, and blue represents the difference channel (PCY1 � PCY2).
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the two reference maps provided reliable bases for evaluating
the late rice acreage change detection between the 2 years.
Accuracy assessment of rice change analysis
The visually interpreted theme maps were used as refer-
ence datasets; rice acreage and acreage change information
were extracted (Table 4). Rice acreage change statistics were
also calculated from the change detection map (Figure 4)
derived directly from TerraSAR-X images using the inte-
grated PCA and object-oriented approach. These statistics
were compared and accuracies are reported in Table 5.
Areas of change in rice between 2008 and 2009 are
clearly identified in Figure 4. Areas in red grew rice in 2008
but not in 2009. Green areas had rice in 2009 and no rice
in 2008. Rice was planted in the vast majority of the region
in both years, and this area is delineated in dark grey in
Figure 4.
Table 5 reveals that rice change analysis using the 2-year
PCA approach achieved an overall accuracy of 99.0%. For
rice acreage change detection, a user’s accuracy of 95.6%
was achieved. This analysis revealed that of the almost
15 000 ha of rice in the Xuwen study site about 10% of this
land experienced change. Rice acreage change in this area
has been interpreted as driven mainly by market demand.
When market prices of other cash crops such as peanut
and watermelon are high some of the rice paddies are
drained to plant cash crops. Conversely, when the price
of rice increases, some nonrice crop land is converted to
rice paddies. The annual fluctuation of rice acreage has a
significant impact on local and regional food security.
Ongoing monitoring and reporting of rice acreage change
are required at both regional and national levels. The Xuwen
site was used as a pilot study to develop a methodology
for rice change detection using SAR data as the lowland
areas are dominated by rice production. This method can
be applied to other rice growing regions in southern China.
Conclusions
This study demonstrated that TerraSAR-X dual-
polarization (VV�VH) stripmap data are capable of provid-
ing accurate late rice acreage change statistics. Using
TerraSAR-X data acquired in late September 2008 and
2009, a three-channel data set was constructed using the
first principal component channel from each year of Terra-
SAR-X data. The third channel represented the difference
in the response of the first principal component of each
year. With these three channels of information, rice acreage
change between the 2 years was visually apparent. This
approach is a simple and effective method to identify changes
in rice acreages. In China’s southern rice growing regions,
where rice paddies are small in dimension, TerraSAR-X
clearly provides a viable option to support operational
requirements to report changes in rice acreages. This paper
presented research findings based upon a limited data set.
Thus, more extensive validation of this methodology and
assessment of the repeatability of these classification accura-
cies are required, particularly in regions with more varied
land cover. As well, acquisition and integration of a more
comprehensive data set, which would include multiple dates
of TerraSAR-X, will likely permit the identification of other
land covers and monitoring of minor crops in the Xuwen
region. Acquisition and integration of multiple dates of
TerraSAR-X data will likely permit the identification of
other land covers and monitoring of minor crops in the
Xuwen region.
Acknowledgement
The authors would like to acknowledge the support of the
German Space Agency (DLR). The TerraSAR-X data were
provided by DLR under scientific project LAN0337.
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