Mapping Direct Seeded Rice in
Raichur District of Karnataka, India
Murail Krishna Gumma, Deepika Uppala, Irshad A. Mohammed, Anthony M. Whitbread, and Ismail Ra Mohammed
Across South Asia, the cost of rice cultivation has increased
due to labor shortage. Direct seeding of rice is widely pro-
moted in order to reduce labor demand during crop establish-
ment stage, and to beneﬁt poor farmers. To facilitate planning
and to track farming practice changes, this study presents
techniques to spatially distinguish between direct seeded and
transplanted rice ﬁelds using multiple-sensor remote sensing
imagery. The District of Raichur, a major region in northeast
Karnataka, Central India, where irrigated rice is grown and
direct seeded rice has been widely promoted since 2000, was
selected as a case study. The extent of cropland was mapped
using Landsat-8, Moderate Resolution Imaging Spectroradi-
ometer (MODIS) 16-day normalized difference vegetation index
(NDVI) time-series data and the cultivation practice delineated
using RISAT-1 data for the year 2014. Areas grown to rice were
mapped based on the length of the growing period detected
using spectral characteristics and intensive ﬁeld observa-
tions. The high resolution imagery of Landsat-8 was useful to
classify the rice growing areas. The accuracy of land-use/land-
cover (LULC) classes varied from 84 percent to 98 percent. The
results clearly demonstrated the usefulness of multiple-sensor
imagery from MOD09Q1, Landsat-8, and RISAT-1 in mapping the
rice area and practices accurately, routinely, and consistently.
The low cost of imagery backed by ground survey, as dem-
onstrated in this paper, can also be used across rice growing
countries to identify different rice systems.
Agriculture is an important sector in India, contributing about
17.9 percent of the gross domestic product (GDP) (2014 ﬁgures)
(CIA, 2015). About 68 percent of the country’s population lives
in rural areas (FAOSTAT, 2013). India is one of the largest rice
growing nations accounting for one quarter of the global rice
area and produces around 125 million tons/year, with low
yields of around 2.85 t/ha (Siddiq, 2000). Given that rice is a
staple food crop in India and that the country has a popula-
tion that is growing at 1.2 percent per annum, a substantial
increase in rice production is the only way to guarantee food
security and to keep food prices low. Labor and fertilizer are
the two major input costs that are incurred while growing rice
when there is assured water availability (Savary et al., 2005).
Migration from rural to urban areas in search of work has led
to labor shortages and higher costs of agricultural operations
(Hossain, 1998; Savary et al., 2005). To overcome these con-
straints, promoting direct seeding in lieu of the traditional and
labor-intensive transplanting (De Datta, 1986; Naylor, 1992) is
being seen as a feasible alternative. However, there is a trad-
eoff with lower yields in rice that is direct seeded rather than
transplanted; so efforts are on to bridge this gap (Khush, 1995).
Accurate and timely information on rice systems, cropped
area, and yields are very important for sustainable food secu-
rity. Several studies have used remote sensing data to monitor
rice crop. Remote sensing technology provides a time saving
approach to estimate cropped area, intensity and other LULC
changes in a country (Badhwar, 1984; Gumma et al., 2011a;
Gumma et al., 2015; Lobell et al., 2003; Thenkabail et al.,
2009a; Thenkabail, 2010; Thiruvengadachari and Sakthiva-
divel, 1997). Many studies have reported the use of spatial-
temporal data to map irrigated areas, land-use, land-cover,
and crop type (Dheeravath et al., 2010; Goetz et al., 2004;
Gumma et al., 2014; Knight et al., 2006; Thenkabail et al.,
2005; Varlyguin et al., 2001; Velpuri et al., 2009) using MODIS
NDVI time-series data to map both agricultural areas (Biggs et
al., 2006; Gaur et al., 2008; Gumma et al., 2011c) and seasonal
crop area (Sakamoto et al., 2005). There are also studies that
have used Synthetic Aperture Radar (SAR) data to monitor
rice areas, particularly irrigated rice. (Le Toan et al., 1997)
used ERS-1 data as input to crop growth simulation models to
estimate production for study sites in Indonesia and Japan.
Similarly, (Shao et al., 2001) mapped rice areas using tempo-
ral RADARSAT data (1996 and 1997) for production estimates
in Zhaoqing in China. (Imhoff and Gesch, 1990) derived
sub-canopy digital terrain models of ﬂooded forest using SAR.
(Kasischke and Bourgeau-Chavez, 1997) monitored the wet-
lands of South Florida using ERS-1 SAR imagery. (Leckie, 1990)
distinguished forest type using SAR and optical data. (Robin-
son et al., 2000) delineated drainage ﬂow directions using SAR
data. (Townsend, 2001) mapped seasonal ﬂooding in forested
wetlands using temporal RADARSAT SAR imagery. (Bouvet and
Le Toan, 2011) demonstrated how lower resolution wide-
swath images from advanced SAR (ASAR) data could be used to
map rice over larger areas. (Uppala et al., 2015) mapped rice
areas using single data hybrid polarimetric data from RISAT-1
SAR data. The advantage of rice mapping with SAR is that it
can overcome pervasive cloud cover across Asia during the
rainy season months when rice is cultivated. However, its
high cost inhibits its large-scale application.
A comprehensive plan to increase agricultural productivity
and manage labor can be drawn up based on near real-time
monitoring of rice systems along with weather and crop data
acquired from an analysis of remote sensing imagery. Remote
sensing platforms have proved to be ideal to take resource
inventories and monitor agriculture. Interdisciplinary ap-
proaches and methods have made it easier to analyze imagery
and extract information in ways unknown a few decades ago.
The goal of this study was to develop a method using mul-
tiple-sensor imagery (Landsat-8, RISAT-1 and MODIS time-series
data) to map the spatial extent of two different rice cultiva-
tion practices (direct seeding and transplanting) in Raichur
district, Karnataka State, India.
International Crops Research Institute for the Semi-Arid
Tropics (ICRISAT), Patancheru, Telangana, India (m.gumma@
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 11, November 2015, pp. 873–880.
© 2015 American Society for Photogrammetry
and Remote Sensing
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Study Area and Data Sets
Raichur is one of largest and most productive rice growing
district in Karnataka state in southern India (Figure 1). The
district lies between 15°42'57" and 16°26'39" latitude and
76°14'47" and 77°35'39" longitude, with a land mass of 835,843
ha (cropped area 695,000 ha) (Figure 1) and bound in the
north and south by the rivers Krishna and Tungabhadra. Agro-
climatically, it falls under the Northeast Dry Zone of Karnataka
(semi-arid eco-subregion) with a long term average rainfall of
just above 600 mm distributed over kharif and rabi seasons.
Much of the district is well irrigated by the Tungabhadra Dam
on the Tungabhadra River, and the Narayanpura Dam on the
Krishna River, covering an irrigated area of 185,000 ha, 72.2
percent of which is from canals. The arable rainfed area occu-
pies 405,000 ha. The major crops grown in the district include
paddy, sorghum, groundnut, sunﬂower, cotton, and pulses
(chickpea and pigeonpea). The rice-rice cropping system is
dominant in most irrigated areas. The soils of the district are
mainly deep black clayey (47 percent) and red (34 percent).
Four Landsat-8 tiles were downloaded from the Earth Ex-
plorer, global land cover facility website (http://earthexplorer.
usgs.gov/). Images from the 2014 monsoon season and their
spectral characteristics are shown in Table 1. All the Landsat-8
tiles were converted into reﬂectance (http://landsat.usgs.gov/
documents/Landsat8DataUsersHandbook.pdf) to normalize
the multi-date effect (Markham and Barker, 1986; Thenkabail
et al., 2004) using the spatial modeler in ERDAS Imagine® (ER-
DAS, 2007). The 250-m, two-band MODIS data (centered at 648
nm and 858 nm; Table 1) collection 5 (MOD09Q1) were acquired
for every eight days during the crop-growing seasons from
January 2014 through December 2014. The data was acquired
in 12-bit (0 to 4,096 levels) and stretched to 16-bit (0 to 65,536
levels). Further processing steps are described in (Gumma et
al., 2015; Gumma et al., 2011b; Thenkabail et al., 2005).
RISAT-1, C-band (5.35 GHz) HH polarization, Medium Resolu-
tion SAR (MRS) temporal data sets were used to distinguish
direct seeded rice from transplanted rice. Data was acquired
every 25 days at a 37° angle of incidence spaced phenologi-
cally appropriate to identify rice ﬁelds and provide informa-
tion on data sets (Table 1).
Ground Survey Data
A ground survey was conducted during the monsoon season
for the crop year 2014 to 2015. Data was collected at 238 loca-
tions covering the major cropland areas in the study area. All
location-speciﬁc data were collected from 250 × 250 meter
plots and consisted of location coordinates (latitude, longi-
tude), land-use categories, land-use (percent), cropping systems
during the monsoon season (through farmer interviews), crop
types, and watering method (irrigated/rainfed). Samples were
within large contiguous areas of a speciﬁc land-use/land-cover.
The locations were chosen based on pre-classiﬁcation classes
and local expertise. The local experts also provided information
on cropping systems, cropping patterns and cropping inten-
sity (single or double crop), irrigation application, and canopy
cover ( percent) for the previous years for these locations from
their recorded data. Overall, 116 spatially well-distributed data
points (Figure 1) were used for class identiﬁcation and labeling;
of these, 46 data points were used for ideal spectra generation
and an additional 122 were used to assess accuracy.
Figure 1. Raichur District, Karnataka, India.
Table 1. CharaCTeriSTiCS of MulTiPle SaTelliTe SenSor DaTa uSeD in The STuDy
Sensor Dates Spatial
(m) Bands Band range
Landsat-8 07 & 14
Nov 2014 30
28 Jun 2014;
23 Jul 2014;
17 Aug 2014;
06 Oct 2014
18 1 HH polarization
(16 days) 250
NDVI - 1 to + 1
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Figure 2 presents an overview of the comprehensive meth-
odology used to map direct seeded and transplanted rice
cultivation practices using time series imagery from MOD09Q1
(spatial resolution 250 m) 16-day time-series NDVI, Landsat-8
(spatial resolution 30 m), and RISAT-1 (spatial resolution 18 m).
Rice growing areas were delineated initially with MODIS time-
series NDVI using spectral matching techniques (Gumma et
al., 2011b; Gumma et al., 2014; Thenkabail et al., 2007a). This
was used as a mask to clip Landsat-8 time-series imagery. The
subset of Landsat-8 was reclassiﬁed to separate rice from non-
rice areas due to the difference in resolution between MODIS
and Landsat-8. The Landsat-8 output provided a more ac-
curate delineation of rice growing area. This subset was again
used as a mask to clip out RISAT-1 temporal imagery.
Land-Use / Land-Cover Classication
The procedure began with image normalization of Landsat-8
data converted to top of atmosphere (TOA) reﬂectance using a
reﬂectance model implemented in ERDAS Imagine (http://land-
The (Operational Land Imager) OLI band data can be con-
verted to TOA planetary reﬂectance using Reﬂectance rescaling
coefﬁcients provided in the product metadata ﬁle. The follow-
ing equation was used to convert DN values to TOA planetary
reﬂectance for OLI data:
ρλ′ = Mρ Qcal + Aρ (1)
where: ρλ′ = TOA planetary reﬂectance (without correction of
solar angle), Mρ = Band speciﬁc multiplicative rescaling factor
from the metadata, Aρ = Band speciﬁc additive rescaling factor
from the metadata, and Qcal = the quantized and calibrated
standard product pixel values (DN).
TOA reﬂectance with correction for the sun angle is then:
where: ρλ = TOA planetary reﬂectance, ρλ′ = TOA planetary
reﬂectance (without correction of solar angle), and θSE = the
local sun elevation angle provided in the metadata.
The MODIS stacked composite was classiﬁed using unsuper-
vised ISOCLASS cluster K-means classiﬁcation algorithm fol-
lowed by successive generalization (Biggs et al., 2006; Gumma
et al., 2011c; Thenkabail et al., 2005). The unsupervised
classiﬁcation algorithm (in ERDAS Imagine 2010) was applied
on a 12-band NDVI (monthly Maximum Value Composite)
MVC to obtain the initial 100 classes, followed by progressive
generalization (Cihlar et al., 1998). The unsupervised classiﬁ-
cation was set at a maximum of 100 iterations with a conver-
gence threshold of 0.99 (Leica, 2010). Time-series NDVI spectra
were then plotted for each of the 100 classes and compared
with the ideal spectra to identify and label classes (Gumma et
al., 2014). However, the time-series NDVI proﬁle helps gain an
understanding of the growth proﬁle of different crops in addi-
tion to providing information on planting date, discrimination
between rice and other crops, early stage conditions (ﬂooded
pixel showing low values initially), and discrimination be-
tween irrigation sources (e.g., irrigated versus rainfed). Class
identiﬁcation and labeling were performed based on a suite of
methods and ancillary data, such as decision tree algorithms,
spectral matching techniques, Google Earth™ high-resolution
imagery and ground survey data (Gumma et al., 2014; Thenka-
bail et al., 2009b; Thenkabail et al., 2007b). The initial reduc-
tion in classes used a decision tree method (De Fries et al.,
1998) based on the temporal NDVI data. The decision tree is
based on NDVI thresholds at different stages in the season that
deﬁne vegetation growth cycle, and these algorithms help to
identify similar classes. The dates and threshold values were
derived from the ideal temporal proﬁle (Gumma et al., 2014).
Using the ground survey data, Google Earth’s high-resolution
imagery along with spectral proﬁles of rice crops from MODIS
imagery, Landsat-8 imagery was classiﬁed using the super-
vised maximum likelihood classiﬁcation algorithm.
The MODIS-derived rice area was used as the basis of the
maximum possible extent of rice area as one segment and
other LULC areas as another segment. This was used as a mask
to clip Landsat-8 time-series imagery. The subset of Landsat-8
was classiﬁed again to separate rice from non-rice areas. Both
segments were classiﬁed independently (to avoid mixed clas-
siﬁcation) using the protocols mentioned earlier. This led to
a more accurate delineation of rice growing area, but did not
show fragmented direct seeded rice areas. This was mainly
because direct seeded rice was sown in the early monsoon,
when there were no images due to heavy clouds. Landsat-8
rice area was again used as a mask to clip out RISAT-1 temporal
imagery. Separating the two practices of rice cultivation was
possible using RISAT-1 temporal imagery. This is the best com-
plimentary data to monitor croplands during the monsoon
season, and where there are continuous clouds.
Figure 2. Overview of the methodology for mapping different rice growing practices.
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Delineating Direct Seeded Rice (DSR) Cultivation Using SAR Data
The RISAT-1 data was processed using ENVI software. The lev-
el-2, HH polarization data was imported in ENVI raster format
(.hdr). To reduce noise, Enhanced Lee Adaptive Speckle Filter
with kernel size of 5 × 5 was applied (Lopes et al., 1990).
Radiometric calibration for HH polarization amplitude image
was carried out by using the metadata provided along with the
imagery. The scaled values of digital numbers were converted
into backscattering coefﬁcient using the equation given in
(Chakraborty et al., 2013; Laur et al., 2002). The temporal im-
ages were co-registered by ﬁtting a second order polynomial
model to the manual GCPs with 50 well distributed points
throughout the scene. The temporal HH polarization RISAT-1
data was stacked. The rice crop delineated from Landsat-8 was
used to subset the RISAT-1 data stack for further processing.
In Raichur, direct seeded rice cropping is practiced along with
transplanted rice which is most prevalent. Transplanted rice is
of two types depending on sowing date. The early transplanted
rice is sown in August and the late transplanted rice in Septem-
ber. SAR data is unique since it can distinguish between different
rice sowing dates. The rice crop generates a unique temporal
backscatter proﬁle. It is clear from Figure 3 that during the estab-
lishment stage of transplanted rice, the backscatter is higher than
direct seeded rice due to tilling and low moisture. During trans-
plantation, backscatter is quite low due to standing water which
reﬂects minimal energy in the backscatter direction. Further,
the backscatter increases as the plant grows because of multiple
reﬂections from the crop canopy (Choudhury et al., 2012).
In the establishment phase of direct seeded rice, low
backscatter was observed due to moisture conditions. This
is the key to distinguishing 80 percent of the direct seeded
rice from transplanted ﬁelds. Also, some of the transplanted
rice ﬁelds cannot be distinguished from direct seeded ﬁelds
due to high standard deviation in transplanted ﬁelds because
of roughness. Additionally, early transplanted rice could be
distinguished at the transplantation stage due to very low
backscatter. However, it is difﬁcult to discriminate between
direct seeded rice and late transplantation due to their similar
growth stages. The deviation of backscatter was found to be
low. The decision tree hybrid classiﬁcation algorithm was
used here in order to separate the above mixed classes. June
is the crucial month to separate transplanted rice form direct
seeded rice (80 percent). The early transplanted rice was eas-
ily separated from directed seeded rice by applying decision
rules on the August imagery. This was possible because the
difference in backscatter was more than 2 dB. The late trans-
plantation was not separable from direct seeded rice because
of the low difference in backscatter. In order to resolve these
types of ﬁelds, unsupervised k-means algorithm with a con-
vergence threshold of 0.99 was applied on the remaining area
Figure 3. Temporal changes in backscatter coefcient in different
rice growing practices.
Plate 1. Land-use/land-cover during the 2014 monsoon season (kharif) in Raichur District, with rice classes temporal NDVI proles.
876 November 2015
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which included late transplanted rice and direct seeded rice.
By using this algorithm, 85 percent of late transplanted rice
ﬁelds were separated from direct seeded rice ﬁelds.
Spatial Distribution of Land Use / Land Cover
Ten land-use/land-cover classes were identiﬁed using spectral
matching techniques using MODIS temporal imagery and Land-
sat-8 (Plate 1). With average rainfall in the order of 600 mm/
annum and large areas under rainfed cropping, canal irriga-
tion from Tungabhadra Dam provides insufﬁcient irrigation
to grow rice. The total irrigated area accounts for 287,993 ha
(Table 2). The important rice growing taluks (administrative
units) are Manvi and Sindhanur where a large area is irri-
gated under rice. The adoption of direct rice seeding is due to
shortage of labor, and it is a new phenomenon and expanding
along with transplanted rice cultivation which is essentially
large in extent. The use of temporal RISAT-1 imagery helped
in identifying how rice was planted (direct seeded rice or
transplanted) and also ascertaining time of planting (early or
late transplanting). This information can be used to schedule
irrigation and estimate harvest dates for each type of practice.
It is clear that the head end of the canal has a larger extent
of transplanted rice than the tail end in Manvi and Sindha-
nur with irrigated mixed crops. Rainfed croplands occupy
the largest area of 351,895 ha in Raichur District. Pigeonpea
mixed with a cereal crop like sorghum is one of the important
and large cropping systems in the taluks of Lingsugur and
Raichur. Rangelands and shrublands are comparably large
which can be potentially used for rainfed agriculture.
Spatial Extent of Direct Seeded Rice and Transplanted Rice
Rice is a major crop in Raichur District, covering 130,343 ha in
2013, out of which transplanted rice covered 117,401 ha and
direct seeded rice 12,942 ha. The major rice area is located in
Manvi and Sindhanur taluks which are irrigated, depending on
ground water availability and rainfall. Of the total rice area, 52
percent is early transplanted rice, 38 percent is late transplant-
ed rice, and only 10 percent is direct seeded rice (Table 2 and
Plate 1). Variations within the direct seeded ﬁeld are difﬁcult
to identify due to the temporal deﬁcit of the image acquisition.
The classiﬁcation accuracies obtained from the 122 indepen-
dent ground data observation data points for the year 2014
were gathered from the study area (monsoon season). Accuracy
assessment was done using error matrix, a multi-dimensional
table in which the cells contain changes from one land-use
class to another. The statistical approach of accuracy assess-
ment consists of different multi-variate statistical analyses
(Congalton and Green, 1999; Jensen, 1996). The error matrix ac-
curacy varied from 75 percent to 92 percent across six cropland
areas. However, it must be noted that rice classes did not mix
with other classes. Overall, accuracy was 82 percent and kappa
was 0.78. So the uncertainty of about 18 percent is due to the
inter-mix among the other land-use/land-cover classes (Table 3).
Comparison with National Statistics and Other Studies
Statistics on taluk-wise rice area in Raichur District was ob-
tained from the Bureau of Economics and Statistics, Govern-
ment of Karnataka State, India. This data was used for com-
paring RISAT-1, Landsat-8, and MODIS-derived statistics. A high
correlation was found (Figure 4) between the two sources of
information on rice cultivation in the district.
Table 2. eSTiMaTeD area unDer DifferenT lanD uSe/lanD CoVer (lulC)
ClaSSeS in raiChur DiSTriCT
LULC area Area (ha) %
01. Irrigated-conjunctive-DS-rice 12,942 1.5
02. Irrigated-conjunctive-early TP-rice 67,418 8.0
03. Irrigated-SW-late TP-rice 49,983 5.9
04. Irrigated-conjunctive-SC-mixed crops 157,650 18.7
05. Rainfed-SC-pigeonpea/mixed crops 122,778 14.6
06. Rainfed-mixed crops/fallows 229,117 27.2
07. Rangelands/shrublands 152,442 18.1
08. Barren lands/shrublands/forests 37,060 4.4
09. Water bodies 9,241 1.1
10. Settlements 3,695 0.4
Table 3. aCCuraCy aSSeSSMenT uSing grounD SurVey DaTa (Two DiMenSional error MaTrix)
Reference row total
pigeonpea / mixed crops
06. Rainfed-mixed crops/
07. Other LULC
01. Irrigated-conjuctive-DS-rice 3 0 1 0 0 0 0 3 4 100% 75% 74%
02. Irrigated-conjuctive-early TP-rice 0 12 1 0 0 0 0 13 13 92% 92% 91%
03. Irrigated-SW-late TP-rice 0 0 7 1 0 0 0 9 8 78% 88% 87%
04. Irrigated-conjuctive-SC-mix 0 0 0 22 2 1 0 24 25 92% 88% 85%
05. Rainfed-SC-pigeonpea/mixed crops 0 0 0 1 19 2 1 27 23 70% 83% 78%
06. Rainfed-mixed crops/fallows 0 1 0 0 4 30 1 37 36 81% 83% 76%
07. Other LULC 0 0 0 0 2 4 7 9 13 78% 54% 50%
Total 3 13 9 24 27 37 9 122 122 84% 80% 77%
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Microwave remote sensing satellites carrying Synthetic Aper-
ture RADAR (SAR) enable imaging of the features of the Earth’s
surface both during the day and the night under all weather
conditions. These speciﬁc characteristics of the satellite are
useful in identifying crop information under cloudy skies
using standard methods. However, the objective stated in
this study demands a hybrid approach where optical and SAR
imagery are used to distinguish between the two rice growing
practices that exhibit similar backscatter mechanisms, except
at the establishment stage. Studies have been conducted to
come up with a robust classiﬁcation method using X-band
SAR data to map rice crops, including for direct seeding in
different ecosystems and also to examine the relationship be-
tween crop characteristics and backscatter coefﬁcient (Nelson
et al., 2014). However, the inherent property of C-band SAR
data, where the wavelength responds to roughness of soil by
including any background (soil backscatter), makes a differ-
ence by identifying a practice like direct seeded rice. The
basic premise that direct seeded rice requires less labor and
less water, making it economically less demanding option
and also a strategy to cope with monsoon failure, has some
lacunae. This is because the direct seeded rice ﬁelds are more
infested by weeds than transplanted rice ﬁelds (Rao et al.,
2007) as the puddled ﬁelds do not allow weed growth. This
leads to yield losses depending on weed infestation.
RISAT-1 SAR imagery was a viable and useful option as it
overcomes cloud cover as well as provides a convenient
temporal coverage to distinguish between these two practices
right at establishment stage. Mixed classes were resolved
based on extensive ground information collected during ﬁeld
trips and tools like Google Earth Explorer™. A multi-sensor
approach was a useful strategy to fulﬁll the objective of this
study, especially to arrive at accurate estimates of the prac-
tice of growing direct seeded rice in Raichur District. The
MODIS, 16-day NDVI data was primarily used to delineate the
rice cropped areas taking advantage of its temporal availabil-
ity. The spectral proﬁles from MODIS temporal data provide
important information on different aspects of the crop, such
as its source of water, phenological stage, and stress. The role
of Landsat-8 imagery was to generate a high accuracy rice
cropped area map to match the resolution of RISAT-1 imagery
and in turn transfer information from MODIS to RISAT-1.
Karnataka State’s sub-district divisions are large and few.
For instance, Raichur has only ﬁve taluks. Statistics gener-
ated from this study on taluk-wise rice cropped area were
compared with those from the government department. A
high correlation was observed between these two sources of
information (Figure 4). Statistics on direct seeded rice was not
available from the government departments since it is a new
practice in this district. However, this study was successful
in identifying direct seeded rice because of the difference in
crop establishment dates compared to the other practice of
Mapping the cropped area of direct seeded rice using a multi-
sensor approach, speciﬁcally the use of RISAT-1 imagery, is
important as it can penetrate cloud cover during the rainy
season. Optical remote sensing methods may not be success-
ful in discriminating land-use classes based on management
practices. Hybrid approaches have always proved useful in
atypical cases such as this study. Even though there are three
types of direct seeded rice cultivation, an attempt was made
to map only the dry seeded sowing practice prevalent in
Raichur District. Sindhanur and Manvi are the two taluks in
the district where the practice is followed. Using the temporal
SAR data along with optical data such as MODIS, a similar
approach can be used to map direct seeded rice cultivation
during post-rainy (rabi) season as well. The spatial distribu-
tion and quantiﬁcation of the extent of direct seeded rice
will not only help breeders to understand the locale-speciﬁc
constraints to yields such as weeds, but also provide remedies
to bridge the yield gaps. In turn, this will help in overcoming
the labor shortage and also help in conserving water for other
uses. In this context, it is proposed to study and examine how
we can differentiate between rice establishment practices not
only at the time of establishment using SAR imagery, but also
when there is a large outbreak of weeds in direct seeded rice
This research was supported by the CGIAR Research Program
Water, Land and Ecosystems. The authors would like to thank
Smitha Sitaraman and Amit Chakravarty, science editors/pub-
lisher at ICRISAT for his assistance with editing. The authors
also thank Dr. AN Rao (IRRI) and Dr. Gajanan Sawargaonkar
for their valuable feedback of the rice classiﬁcation system
and sub-district wise statistics.
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