Agricultural Monitoring in India
The National Crop Forecasting Centre (NCFC) of the Department of Agriculture & Cooperation
(DAC), of the Government of India was established in 1998, with a mandate to develop a
framework for providing crop production forecasts at district, state and national levels. In
addition, to support the high-level decision making and planning it is responsible for providing
information on crop sowing progress, crop condition throughout the growing period, and on the
effect of episodic events such as floods, drought, hail storms, pests, disease etc. on crop
production. Use of remote sensing has been an important consideration by the DAC which
sponsored the Crop Acreage and Production Estimation (CAPE) project. The Space Applications
Centre (SAC) of the Indian Space Research Organization (ISRO) has led the project in
developing: i) a remote sensing based procedure for crop acreage estimation at district level, ii)
spectral and weather models for yield forecasting, iii) semi-automatic s/w package CAPEMAN
(later renamed CAPEWORKS) for analysis of RS data, iv) technology transfer to teams across
the country that use these procedures and make in-season crop production forecasts. LISS-III
data from the Indian Remote Sensing satellites (IRS) are being regularly used to make crop
production forecasts.
To address the DAC requirement of multiple in-season, national level assessments of crops and
production forecasting, the concept of Forecasting Agricultural output using Space,
Agrometeorology and Land based observations (FASAL) has been developed by SAC (Fig.X).
FASAL envisages providing information on crop prospects at the beginning of the crop season
with econometric models, followed by weather based models to forecast crop acreage early in
the season, and later on yield. Moderate spatial resolution remote sensing data from
WiFS/AWiFS will be used to provide area estimates under crops about 6-8 weeks after sowing.
By the middle of the crop growing season, higher spatial resolution data like AWiFS and LISSIII will be used to provide area estimates under selected crops. Crop condition and crop area
estimates will be repeated about a month before crop maturity. Weather based models will be
implemented independently as well as with spectral data to provide crop yield forecasts at
different crop stages. Use of crop growth simulation models with spatial coverage and
parameters derived from remote sensing data is also planned.
As a part of FASAL, national level multiple assessments of wheat and Kharif (Monsoon) rice
acreage estimates are being made using AWiFS and Radarsat ScanSAR Narrow Beam-2
temporal data, respectively. An example of use of temporal AWiFS data from Resourcesat for
crop area estimation is given in Fig. XX. Weather models have been developed and are used for
production forecasting at the state and national level. Winter-potato acreage estimation is
performed using data from LISS-III and AWiFS, weather and crop growth simulation models are
used for yield forecasting. Besides providing crop statistics, changes in crop area due to low soil
moisture and rainfall and changes in cropping pattern are also mapped.
Procedures for estimation of Leaf Area Index (LAI), NDVI, insolation, albedo, and LST are
under development using IRS (AWiFS) and INSAT/Kalpana (AVHRR and CCD) data.
Validation of these products with the support of well distributed in-situ field measurements has
been performed. Crop growth simulation models such as WTGROWS and WOFOST have been
adapted with a spatial framework to use the remote sensing derived parameters along with other
data.
Cropping system analysis of the Indo-gangetic Plains region of India has been done. First a gross
crop rotation mapping was done using SPOT-VGT data. Subsequently seasonal cropping
patterns for Kharif, winter and summer seasons have been mapped using AWiFS and Radarsat
ScanSAR Narrow Beam-2 data at larger scale. Crop rotation maps have been generated using the
cropping pattern data. Field survey has been carried out to identify and characterise the cropping
systems of the region. The example of multi-scale and region coverage with data of different
spatial resolutions is shown in Fig. XXX A comprehensive data base of cropping systems and
associated parameters has been created at a 50m pixel size. A cropping system simulation model
(Cropsyst) has been validated with the field and remote sensing data. Cropping system
performance indicators such as the Area Diversity Index (ADI), Cultivated Land Utilisation
Index (CLUI) and Multiple Cropping Index (MCI) have been developed.
Figure: An example of use of multi-temporal Resourcesat AWiFS data for area estimation in
India.
Figure: Use of Multi-spatial resolution data for crop rotation mapping at varying scales and
coverage in Indo-Gangetic Plains Region of India.