Journal of Geographic Information System, 2011, 3, 351-356
doi:10.4236/jgis.2011.34033 Published Online October 2011 (
Copyright © 2011 SciRes. JGIS
Identification of Paddy Planted Area Using ALOS
Rizatus Shofiyati1, Ishak Hanafiah Ismullah2, Dan Dudung Muhally Hakim2
1Indonesian Center for Agricultural Land Resources Research and Development (ICALRD),
Indonesian Ministry of Agriculture, Bogor, Indonesia
2Bandung Institute of Technology (ITB)
Received June 23, 2011; revised August 2, 2011, accepted A u gust 15, 2011
Agricultural land has a strategic function as the primary food provider for the people of Indonesia. Various
methods of agricultural production estimation, particularly food crops, provide different information. It can
be a source of error in decision making. Satellite data, provides information periodically, wide coverage area,
can be used as a source of information on the condition of agricultural lands and even remote areas. The
advantages of SAR data that does not depend on sunlight and can penetrate of clouds and fog can fill the lack
of optical data. ALOS PALSAR data has been used for analysis and ALOS AVNIR-2 is for checking of land
cover visually, with acquisition date on 10 May 2007. Sampling of each rice crop growth period used several
of rice field conditions in each period, on one scene data. Results showed a possibility to use soil moisture
conditions derived from ALOS PALSAR for estimating rice planting area. On a scatter diagram between
backscatter of ALOS PALSAR and near infrared of ALOS AVNIR-2 showed a specific pattern for each
growing period of paddy. The results of the analysis produce distribution maps of the rice planting area
Subang area, West Java Province. However, validation of the method used remains to be done. Remote
sensing results of this study are expected to provide better information and can contribute in the planning of
higher quality agricultural land.
Keywords: Rice Planting Area, Moisture Content, ALOS PALSAR
1. Introduction
Paddy field has a strategic function, because it is a main
food supplier for people in Indonesia. Current estimation
methods of agricultural production provide various data
and information, so its reliability is questionable. One
source of error lies in information on acreage of paddy
fields, which resulted in calculation of planting area and
yields are not true. According to official data from Cen-
ter Bureau Statistic, an area of rice fields in Indonesia is
8.1 million hectares [1], but there are several data
sources provide different information of rice field area.
Until now there is no uniformity in the data area of wet-
land that is used by different parties is the main cause of
differences in data collection methods and years of ex-
pansion. It could be argued that the rate of change
(transfer function) of wetland is much faster than the
period of data collection statistics. According to refer-
ence [2], alleged that the decline of the agricultural land
area in Indonesia reached 27,000 ha/year, which is
generally in the form of wetland and dry land. The
changes are so great, of course, very difficult to follow by
the rate of data collection statistics.
Indonesia has very wide coverage area that consists of
thousands of islands with a various geographic conditions.
It causes difficulties to do field data collection activities.
Cost for inventory, monitoring and updating of land use
in the conventional way through a field survey is very
high, so the method can not be implemented in a
relatively short time. Remote sensing technology that
record periodically earth surface can be used as an
alternative to support of field research mainly to changes
in land use, including the planting period in the paddy
field. Hence, coverage of satellite data provides
information of agricultural land condition even in remote
Each sensor has ability to analyze data and can fill one
another. Multi spectral optical data can identify green-
ness and wetness which is related to biomass, while SAR
data can detect soil moisture, leaf water content, and
plant height. SAR data has a larger wavelength has
ability to penetrate clouds and atmospheric disturbances
caused by dust, smoke and fog, is expected to fill
weakness of multi spectral optical data which has shorter
wavelengths. Combination of multi spectral optical and
SAR data is expected to improve analysis quality. Main
parameters can be observed is correlated with changes in
of plants in different crop growth periods such as age,
height, biomass, and that correlated with water
conditions such as soil moisture and plant water content.
Advanced Land Observing Satellite (ALOS) is an
earth observation satellite which not only has optical
devices, but also equipped with a set of L-band Synthetic
Aperture Radar [3-4]. It also has multi-polarization, that
is effectively used to obtain information on global and
regional vegetation, distinguishing appearance on the
earth's surface, land use classification, and others [5].
With the acquisition period in every 46 days, large
enough spatial resolution, wavelength L-band, and multi
polarimetri, ALOS data will be used to identify the
cropping pattern in paddy field.
Spatial resolution of ALOS can be applied to
agricultural land in Indonesia, especially Java, which land
ownership of less than 0.5 hectare [6-7]. In fact, the Java
Island is center of national rice production. Although area
of Java is 7% of total area of Indonesia, the island has
contribution to national rice production that is estimated
never less than 50% [8]. Productivity of paddy and dry
fields in Java 1996-2000 year was an average 50.14
quintal per hectare compared to 43% higher outside Java
productivity average of only 35.05 quintal per hectare [9].
Therefore, condition of agricultural land located in Java
should be a consideration in selecting satellite data used.
Use of remote sensing technology is expected to give
more correct information and have contributed in
planning of agricultural land, and more qualified agricul-
tural development policies.
This paper explains results of study on soil moisture
content condition in paddy fields using SAR data to
identify paddy planted area.
2. Material and Methods
2.1. Study Area
Research has conducted on Subang area, West Java
Province. The location is situated between coordinates
106˚22' - 107˚55' East Longitude dan 5˚55' - 6˚31' South
Latitude. Map of research location is presented in Figure
2.2. Data Used
ALOS PALSAR has been analyzed to get water content
data and ALOS AVNIR-2 for visually comparation. The
description of data used is in Table 1. Date of acquisition
of both data is 10 May, 2007. Other data used is Land
Use Map of West Java Province produced by reference
2.3. Research Method
Backscatter value of ALOS PALSAR data were obtained
by using the following calculation [11]:
0 10*log10DN2 CF (1)
0 = backscatter coefficient (dB); DN = digital
number of PALSAR image; CF = factor calibration =
83.0 dB ( 0.64 dB).
To calculate soil water content of backscatter (dB)
used a simple linear equation refers some research results
[12-14] as follows:
VSM = a
0 + b (2)
A study conducted by [15], on relatively plain lowland
areas, value of a is 0.99 and b is 43.8, so the equation
obtained is:
Figure 1. Map of study area.
Copyright © 2011 SciRes. JGIS
Table 1. Description of ALOS images used.
No. Scene ID Path Frame Mode Orbit Direction Off NadirResolution Polarization/ Cloud cover
1 ALPSRP068787060 432 7060 PLS Ascending 21,5˚ 25 m HH, HV, VV, VH
2 ALAV2A068713730 110 3730 OBS Descending 10 m 11% - 20%
VSM = 0.99
0 + 43.8 (3)
while for not flat area, value of a is 1.37 and b is 29.1, so
equation used is as follows:
VSM = 1.37
0 + 29.1 (4)
where: VSM = volume soil moisture (%); a and b = co-
efficient that is depended on backscatter and polarization;
0 = backscatter coefficient;
0 = backscatter coeffi-
cient that has reduced by roughness effect.
The study site that is located in northern coastal area,
has large lowland paddy fields. Assuming that the study
site is flat area, Equation (3) is used for calculation of
moisture content.
3. Results and Discussion
3.1. Backscatter
The result showed that L-band of HV polarization gives
clearly pattern for different paddy growing period, i.e.
flooded, vegetative, and bare. Similarly, in another land
cover, such as tea plantation and village, has. According
to reference [15], in period of maturity, HH polarization
provides higher backscatter coefficient than VV.
Back-scatter pattern of each growing period of paddy is
presented in Figure 2.
3.2. Soil Mositure
According to reference [16], soil moisture conditions in
paddy fields during paddy growing period, generally
about 40%. While at the time of inundation or can be
referred in water saturated conditions, soil moisture is
100%. Similarly, research results conducted by [12],
volume of soil moisture (VSM) values on agricultural
land of planting, growing, and harvesting ranges from 20
to 40%. Based on these conditions, equation 3 gives
more prevalent calculation results compared to equation
4 which include influence of surface roughness. HH and
VV polarization provides a more appropriate value of
VMS in paddy fields. The diagram of VMS is presented
in Figure 3.
3.3. Paddy Planted Area
Soil or land moisture is calculated using ALOS PALSAR
data backscatter. Analysis result produces a distribution
map of rice planted area in Subang, West Java Province
(Figure 4). Based on the value of VMS, it can be
estimated area of paddy as listed in Table 2.
Estimated paddy panted area at 2nd period of growing
stage at the next 2 months and vegetative 1 between 1
and 1.5 months (25 - 40 days). While the growth period
020 40 6080100120
Nilai digital - Band Infra merah dekat AVNIR-2
Nilai digital (dB) - PALSAR
020 40 6080100120
Nilai digital - Band Infra merah dekat AVNIR-2
Nilai digital (dB) - PALSAR
Vegeta t i f
0 20406080100120
Nilai digital - Band Infra merah dekat AVNIR-2
Nilai digital (dB) - PALSAR
Vege t a ti f
0 20406080100120
Nilai digital - Band Infra merah dekat AVNIR-2
Nilai digital (dB) - PALSAR
Figure 2. Backscatter pattern of ALOS PALSAR on some land cover types.
Copyright © 2011 SciRes. JGIS
0,99 σ˚ + 43,81,37 ∆σ˚ + 29,1
Volume kelembaban tanah (%)
Tergenang/Macak2 Vegetatif Bera
0,99 σ˚ + 43,81,37 ∆σ˚ + 29,1
Volum e kelembaban tanah (%)
Tergenang/Macak2Vegetatif Bera
0,99 σ˚ + 43,81,37 ∆σ˚ + 29,1
Volume kelembaban tanah (%)
Tergenang/Macak2 Vegetatif Bera
0,99 σ˚ + 43,81,37 ∆σ˚ + 29,1
Volume kelembaban tanah (%)
Tergenang/Macak2 Vegetatif Bera
Figure 3. Volume of soil moisture at period of inundated, ve ge tative, and bare.
Figure 4. Map of paddy planted area distribution in Subang, West Java Province.
Copyright © 2011 SciRes. JGIS
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Table 2. Paddy planted area in May 2007.
No Paddy field condition Area (Ha)
1. Bare 24492.7
2. Paddy at 2nd vegetative period 18885.6
3. Paddy at 1st vegetative period (innundated) 15244.1
of rice (lowlands) from planting to harvest about 105
days [17,18]. An estimated 2 months ahead of harvest
area of about 18885.6 ha, and 3 months ahead 15244.1
ha. Assuming that productivity of paddy is 4.5/ha,
referred to national productivity average [7,19], esti-
mated that rice production in the study area at next 2
months is 84985, 2 tons and 68598.5 tons for next 3
months. Estimated conditions of rice plants is done by
visually comparing of appearance of the ALOS satellite
data AVNIR-2 and experience of researchers in
analyzing satellite data for food crops, especially paddy.
4. Conclusions
By using ALOS Image AVNIR-2 to observe diffe-
rences in land cover visually, indicating a specific
pattern of light reflection from ALOS PALSAR
satellite images and can be used to distinguish the
condition of paddy field.
Water content estimation obtained from backscatter
values of ALOS PALSAR has the possibility to be
used to identify rice growing period. Hence, it can be
used to create a distribution map of rice planting area.
5. Acknowledgements
The authors are grateful to the Indonesian Center of Ag-
ricultural Land Resources Research and Development
(ICALRD) of Indonesian Ministry of Agriculture, and
Geodetic and Geoinformatic of Bandung Institute of
Technology (ITB) for providing facilities, data, and en-
couragement for this study. Authors are also thankful to
the Directorate General of Higher Education (DIKTI), of
Indonesian Ministry of National Education for providing
funds for this research activity. Authors thanks also is
conveyed to JAXA for allowing authors to use ALOS
satellite data from Working Group of JAXA ALOS
Project activities for this research.
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