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and Guntur districts of Andhra Pradesh (Navalgund, et al. 
1996). 
The procedures are also developed and are operational to 
estimate rice cropped area using microwave data from 
RADARSAT (Chakraborty et al, 1997) to overcome the 
problem of non-availability of cloud free optical satellite data 
during the kharif season. National level wheat production 
forecast using multi date WiFS data are operational under the 
Forecasting Agricultural Output using Space, Agrometeoro- 
logy and Land based Observations (FASAL) project (SAC, 
1999). Multi temporal IRS-WiFS data of kharif (October, 1999) 
and rabi (January, 2000) were used in the study with the 
following objective. 
2. OBJECTIVE 
To identify, delineate, estimate acreages and provide maps 
showing spatial distribution of kharif rice crop and the post 
kharif rice fallow lands in India, Pakistan, Nepal and 
Bangladesh countries of the South Asia using temporal WiFS 
data 
3. STUDY AREA 
All the major kharif rice cultivating States / Provinces of the 
four countries viz., India, Nepal, Pakistan and Bangladesh 
constituted the study area. Eighteen major rice growing States 
of India were selected, contributing to about 98% of kharif rice 
acreage of the country. 
4. METHODOLOGY 
The methodology essentially consisted of the selection of the 
datasets, processing of the satellite data, incorporation of 
ground information, analysis of the satellite data and generation 
of the output products in hard and soft formats. 
4.1 Selection of the satellite datasets 
After the harvest of kharif rice, the land will be either left 
fallow or cultivated with a suitable crop in the following rabi 
season. The time gap between the harvest of the kharif rice and 
the cultivation of the rabi crop depends upon the suitability of 
the prevailing weather, availability of water etc. Satellite data 
of the period immediately after the harvest of kharif rice crop 
will depict large area under fallows though these lands are sown 
with rabi crop because of poor manifestation on the image 
leading to in over estimation of the fallow lands. In order to 
properly estimate the post kharif rice fallows, satellite data of 
rabi period was selected based upon the prevailing cropping 
pattern of the region coinciding with the maximum vegetative 
stage of the dominant crop and manifested crop is clearly 
discernable on the satellite data. 
IRS-WiFS data corresponding to the peak vegetative stage of 
rice of the kharif 1999 season ( October) and the other data of 
the rabi 2000 (January to March), post kharif period were 
selected after extensive data browsing. The state specific crop 
calendar information on the staggering in the transplantation 
operations of the paddy crop was utilised in the selection of the 
appropriate satellite data. Information on variations in crop 
calendar across the states within the kharif rice season was also 
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002 
245 
essential, as the period of post kharif season would vary as 
influenced by the actual rice-growing period during the kharif 
season. 
Forty three IRS-WiFS scenes were analysed in ths study. 
Efforts were also made to have a uniform database across the 
study area to obtain the standardised estimates as well as to 
optimise the number of satellite datasets required for the 
investigation. In case of States wider than the 800 km swath of 
the WiFS sensor, multiple (2 to 3) satellite datasets were used. 
Subsets of the satellite data, to the extent of the State coverage, 
were extracted and the analysis was carried out at individual 
State level. The corresponding data sets were mosaicked and 
the State administrative boundaries were overlaid. 
4.2 Geo-referencing of the datasets 
Survey of India generated toposheets on 1: 1000000 scale were 
used in the study, which were geo-referenced to polyconic 
everest projection and subsequently used for geometric 
correction of the satellite datasets. The first date satellite data, 
of kharif or rabi seasons, were registered with these projected 
toposheets using affine transformation technique followed by 
re-sampling to obtain the geo-referenced image, which was 
considered as the master image. The satellite data of the 
subsequent dates were co-registered with this master image. 
ERDAS - IMAGINE 8.0v was used in geo-referencing of the 
data. Polyconic projection was applied uniformly to all the 
datasets with graticular (latitude / longitude) information in 
degrees. Common central meridian points and origins were 
used in developing models for geo-referencing wider States, 
which were not covered in a single swath of WiFS, to enable 
efficient georeferencing while mosaicking. 
4.3 Overlaying of the administrative boundaries and forest 
masks 
The administrative boundaries of the countries viz., Pakistan, 
Nepal and Bangladesh obtained from ICRISAT were overlaid 
on the satellite data to generate country level information and to 
develop the output products. In case of India, statewise 
boundaries were overlaid. Forest masks were also generated 
and were excluded in the classification of satellite data. 
4.4 Ground truth information 
This information was highly essential right from the selection 
of the data to the verification of the results at various 
intermediate steps such as defining the training areas, 
generation of spectral signatures, testing the separability and 
classification of the satellite data. Since the analysis was carried 
out post season, real time ground truth was not used in this 
project. Hence, ground information as available in the form of 
published literature was used to derive the information on the 
growing period and distribution of rice crop in India. Ground 
information corresponding to Pakistan and Bangladesh 
countries provided by the ICRISAT were used in the analysis. 
In case of Nepal, the ground information of Uttar Pradesh State 
of India was extrapolated because of the spatial contiguity of 
these two regions. 
4.5 Analysis of the satellite data 
Digital data analysis techniques viz., delineation of the training 
areas, generation of spectral signatures, checking for the 
spectral separability of different agricultural land covers, 
classification of the datasets, were applied in the analysis and