IAPRS & SIS. Vol.34, Part 7, "Resource. and Environmental Monitoring", Hyderabad, India,2002
with and without use of district-wise N fertilizer application
rate computed from district-wise fertilizer consumption
statistics. This study was carried out for northern Indian state of
Haryana for the crop season 2000-01.
2. METHODOLOGY
2.1 CGMS Prototype
The CGMS developed for this study consisted of four
components, namely, (a) inputs assimilated in GIS, (b) a
relation database management system (RDBMS), (c) a two-way
linking shell between RDBMS and crop model and (d) crop
simulation model WTGROWS. The framework has been
implemented on MS Windows NTTM platform on a personal
computer. The MS ACCESS!M software has been used as
RDBMS while AGROMATM IP/GIS software has been used for
image processing and GIS functions. All the spatial layers for
the study area were geo-referenced in UTM projection Zone 43
North with Indian Datum. The generation of input spatial layers
and CGMS sub-system functions are described below.
2.1.1 Grid Layer: A 5'X5' polygon vector grid layer was
generated for the state of Haryana in GIS. Each grid cell
represents one simulation model run and hence all the other
inputs were assimilated/aggregated at grid cell level in the
RDBMS though they were having information at different
spatial scales. The serial number i.e. identifier, area and central
latitude of each grid cell was generated and stored in RDBMS.
2.1.2 Administrative Boundary Layer: Vector layer of
district boundaries were digitized from 1:250,000 scale Survey
of India (SOI) maps. The district boundary layer was overlaid
on grid layer and each grid was assigned a district code
depending on the maximum district area in the grid.
2.1.3 Soil Properties Layers: The soil resource map of
Haryana produced by NBSS&LUP (Sachdev et al., 1995) at
1:250,000 scale was digitized. Soil depth and soil texture layers
were generated after reclassifying the soil-mapping units
according their attributes of depth and particle size,
respectively. Each grid cell was assigned average soil depth and
dominant soil texture class. To account for soil fertility, soil
organic carbon raster map was produced by interpolating from
72 point data collected from literature using inverse square
distance interpolation. An average soil organic carbon content
was calculated for each grid cell and stored in RDBMS.
2.14 Weather Surfaces: The daily weather data (Rainfall,
Maximum and minimum temperature, Wind speed, and
Relative Humidity) of 21 surface observatories in and around
Haryana State were entered as table in RDBMS. A weather data
interpolation program was written in "Visual Basic TM", The
program read daily weather data of observatories with their
locations from the database tables and generated daily surface
of each weather parameter at 5'X5' resolution using inverse
square distance interpolation. Boring through the daily weather
surfaces resulted in grid-wise daily weather data file in the
format of WTGROWS. Due to the non-availability of daily
solar radiation for most of the observatories, Hergreave's
method of estimating daily solar radiation from temperature
range was adopted. Nain and Dadhwal (2001) have derived
coefficients of Hergreave's equation for various stations in
wheat belt of India. Using the geographical coordinates of such
stations in and around Haryana State, a thiessen polygon
surface was generated and each grid cell was assigned dominant
polygon's Hergreave's coefficients.
2.1.8 Crop Model: The WTGROWS (Aggarwal et al.,
1994) is a detailed production level-3 mechanistic model, which
simulate the potential production, phenology, soil water
balance, soil and plant nitrogen balance and water and nitrogen
stress on plant growth and development. It has limitation that it
does not simulate the effect of biotic stresses (pests and
diseases) on crop growth and development. It requires inputs on
site data, daily weather data, soil characteristics and crop
management data. The model has been well calibrated for
Indian wheat cultivars. In this study, the standard values of
genetic constants for a semi-dwarf medium duration high
yielding wheat cultivar were adopted (Aggarwal et al., 1994).
The model, written in PCSMP (Personal Computer Continuous
System Modeling Program by IBM, 1975), runs on IBM
compatible PC under MS-DOS.
2.1.6 CGMS Shell: For interfacing the spatial inputs
generated as grid attribute table to WTGROWS model, the
“linking” strategy described by Hartkemp et al. (1999) was
adopted. The linking shell was written in C language, which
read the grid attribute table and generated the required input
parameters for the model for each grid having wheat area. It
also copied the daily weather file for each grid as the current
weather file. Pedo-transfer functions were incorporated into the
shell to generate volumetric soil-water constants from the grid
cell textural class. The pedo-transfer function coefficients were
generated from the experimental soil dataset of twenty locations
in Haryana published by Komos et al. (1979). The organic
nitrogen in soil was initialized for each grid cell from organic
carbon content by assuming a C:N ratio of 10:1. The shell also
initialized soil moisture at sowing as 75 percent of field
capacity to simulate a pre-sown irrigation which is common
adopted practice in the State. The shell ran the model for each
of the grid and model outputs were written back into the grid
attribute table in the RDBMS to be visualized as grain yield and
biomass maps in GIS. The error trapping was also built into the
shell to know if model simulation could not be accomplished
for any of the grid cell.
2.2 RS-data Analysis
224 Wheat distribution Layer: Eleven IRS-WiFS
images acquired between 28-Oct-2000 and 22-Apr-2001 were
registered, georeferenced and radiometrically normalized.
Hierarchical decision rule based classification (Oza et al., 1996)
was carried out to discriminate wheat from other categories
resulting in wheat distribution map. Fraction of wheat area to
total grid area was computed for each grid cell and stored in
grid attribute table. This fraction was used as weight in
computing weighted average district yield from grid values.
2.2.2 Estimating Wheat Phenology: The technique for
estimating wheat phenology including dates of sowing is based
on the premise that in a season, date of peak NDVI is very
distinctive and corresponds to the date of peak leaf area index
(LAI) of the crop for a given set of soil, weather and cultivar
type. So, if we iteratively vary only date of sowing and simulate
such a LAI profile by crop model whose date of peak LAI
matches with the date of peak NDVI, then that is the
represented value of date of sowing. Using wheat distribution
image and district boundary vector, district-wise mean wheat
NDVI was computed for each date. A functional form of
