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REMOTE SENSING AND GIS FOR AGRICULTURAL CROP ACREAGE
AND YIELD ESTIMATION
Vinay K. Dadhwal
Space Applications Centre, Ahmedabad
ABSTRACT
The use of satellite remote sensing data (RS) for operational crop assessment and forecasting at regional scales is of great significance for
food security and policy decisions. Improvements in sensor capability and analysis techniques now allow accurate crop discrimination.
Through use of GIS, regional sampling strategy using land cover, crop distribution and administrative boundary information, large area
crop inventory is carried out. The use of RS data for yield forecasting, with high and coarse resolution and single or multiple acquisitions
has generally followed empirical regression approach. The information on soil and weather can be integrated for yield modelling through
use of crop simulation models. GIS plays an important role in converting the input data to a common format for use in the model. The
recent Indian experience in developing techniques for estimation of
summarised.
REMOTE SENSING IN CROP ACREAGE
ESTIMATION
The use of spaceborne RS data for large area crop
survey was first explored under CITARS Project in USA
and was followed by an attempt to forecast wheat for
major growing regions of the world under LACIE
(Large Area Crop Inventory Experiment, 1974-1977)
(MacDonald and Hall, 1980). Since then, large scale
methodology development-cum-demonstration studies
for crop statistics have been carried out in Africa and
Europe as well as in a number of other countries
(Argentina, Australia, Brazil, Canada, Japan etc.).
Currently major programs are underway in Europe under
MARS (Monitoring Agriculture through Remote
Sensing).
Remote Sensing (RS) can be used for crop acreage
estimation in either for making area sampling frame
(crop inventory being done by field survey), or for crop
discrimination. Current regional crop inventory studies
use RS for both applications, GIS-based sampling and
digital image processing for crop discrimination. In case,
the accuracy of RS-based discrimination is not
acceptable, it has to be combined with field survey
information.
RS-BASED ACREAGE : INDIAN EXPERIENCE
CAPE PROJECT
Systematic studies on district-level crop inventory
were taken up for single crop dominated districts
(Karnal, Haryana for wheat, Dadhwal and Parihar, 1985)
and promising results led to state level wheat acreage
estimation in Haryana and Punjab using a sample
crop area and forecast of likely yield using both RS and GIS is
segment approach. It used 10 x 10 km segments, a
stratified sample design and 10 percent sampling
fraction and a single acquisition at optimal bio-window
(Dadhwal, 1986). This approach has been followed in
Crop Acreage and Production Estimation (CAPE) Project,
which is sponsored by Ministry of Agriculture and covered
six major crops in 15 States (Navalgund et al., 1991). The
study areas for these crops are summarised in Table 1. A
summary of results of CAPE project for 1997-98 crop
season are provided in Table 2. A procedure using multi
date WiFS data and GIS for sample design, developed for
national-level multiple wheat assessments (Oza et al,
1996) is described below.
Improving the accuracy of RS-based crop inventory
The CAPE procedure is being continuously revised
and upgraded to improve upon accuracy and timeliness of
crop estimates. These efforts are related to improving a)
Sample design, b) Ground truth data collection, c)
Optimising date of data acquisition, d) Including data from
additional spectral regions in the digital analysis, e) Multi
date data analysis, f) Use of higher spatial resolution data,
g) Adopting different classification procedures, and h) Use
of microwave data for crop inventory in kharif season.
Significant results from these studies are summarised
below.
Sample Design
Studies carried out on rice for Orissa and wheat for
Haryana have clearly shown that two-step stratification
based on agro-physical and crop distribution improves
efficiency of stratification (Panigrahy et al., 1991). Size of
the sample segments and sampling fraction are two
