Discussion
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
transplanting rice.
Conclusions
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
cultivation.
Acknowledgments
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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