65 
Indian Experience : Use of Physical Models 
Studies have been initiated for integration of Crop 
Simulation Models and RS inputs in a joint program 
between Space Applications Centre and Indian 
Agricultural Research Institute (IARI, New Delhi). 
Initial efforts are to integrate a wheat growth simulation 
model developed at IARI, WTGROWS (Aggarwal and 
Kalra, 1994) with RS inputs for regional level yield 
prediction. During the year 1997-98 field measurements 
of LAI, final grain yield and phenology were made at 25 
farmer’s fields in Alipur Block, Delhi. Information on 
date of sowing and management practices (cultivar, 
fertilizer application and number of irrigations), which 
varied considerably in these fields was also recorded. 
Using this information, simulation of phenology, LAI 
temporal profile and final biomass and yields was 
carried out and results compared with actual 
observations. The simulated and actual LAI temporal 
profiles matched well for sites with different dates of 
sowing except in post-anthesis stages. The simulated 
pre-anthesis duration and total above ground biomass 
were also highly correlated with observed values with 
deviations less than 15 percent. However, significant 
differences in simulated and observed yields were 
noticed. IRS-ID LISS-III data (Feb. 1, 1998) was used 
for identifying farmer's fields 3nd developing LAI- 
NDVI relationship. When satellite-based LAI estimates 
were forced in the model, the simulated yields, which 
were still high, followed the pattern of observed yields, 
indicating improvement in yield forecasting by 
introducing RS-based LAI (Sehgal et al., 1999). This 
suggests need to develop an easy to use LAI retrieval 
procedure from satellite data and is described below. 
Price (1992) treated the interaction of radiation 
with vegetation in a very simple manner, formulating a 
two-stream description of the interaction of radiation 
with the plant canopy and its underlying soil 
background. The predicted reflectance depend on only 
three wavelength dependent parameters besides LAI, the 
reflectance of soil r s , the reflectance of a thick (LAI«,) 
vegetation canopy r m , and the attenuation constant for 
radiation in the canopy. Price (1993) demonstrated the 
approach by application to a data set of the Landsat TM 
data. The procedure suggested by Price (1993) was used 
for the estimation of wheat LAI from IRS-1C, LISS-III 
data over two sites (Karnal and Delhi, India) for crop 
seasons 1996-97 and 1997-98, respectively. The a priori 
crop specific attenuation constants for radiation (c, & c 2 ) 
were computed for wheat crop using published and field 
ground measurements and found to be different from 
those published for corn and sugar beet. Application of 
the model over 36 chosen fields and its comparison with 
ground measurements of LAI indicate a RMSE of 1.28 
and 1.07 for two sites respectively (Rastogi et al., 1999). 
Further studies are planned to cover additional sites and 
seasons and improve the accuracy. 
ACKNOWLEDGEMENTS 
The author expresses his heartfelt thanks to CAPE 
team members who shared their views and results and 
made this review possible. 
REFERENCES 
Aggarwal P.K. and Kalra N. (1994). Simulating the effect of 
climatic factors, genotype and management on productivity of 
wheat of India. IARI, New Delhi. 
Bhagia N.. Oza M.P., Rajak D.R., Singh R.P.. Sehgal V.K., 
Ravi N., Srivastava II.S., Patel J.H., Ray S.S. and Dadhwal 
V.K. (1997). An attempt to make national wheat production 
forecast using multi-date WiFS data for 1996-97 season. Bull. 
National Natural Resources Management System, 
NNRMS(B)-21, 54-58. 
Bouman B.A.M. (1995). Crop modelling and remote sensing 
for yield prediction. Netherlands Journal of Agricultural 
Sciences. 43:143 -161. 
Bullock P.R. (1992). Operational estimates of western 
Canadian grain production using NOAA-AVHRR LAC data. 
Canadian Journal of Remote Sensing, 18( 1 ):23-28. 
Burrill A., Vossen P., van Diepen C.A. (1985). A GIS database 
for crop modelling. In 'European Land Information Systems 
for Agro-Environmental Monitoring' (D King, R.IA Jones. AJ 
Thomasson Eds.), pp. 143-154. Joint Research Centre, 
Luxemburg. 
Carbone G.J., Narumalani S., King M. (1996). Application of 
remote sensing and GIS technologies with physiologic crop 
models. Photogrammetric Engineering & Remote Sensing. 
62(2): 171-179. 
Chakraborty M., Panigrahy S., Sharma S.A. (1997). 
Discrimination of rice crop grown under different cultural 
practices using temporal ERS-1 synthetic aperture radar data. 
ISPRS Journal of Photogrammetry & Remote Sensing, 
52(4): 183-191. 
Clevers J.G.P.W. and van Leeuwen H.J.C. (1995). Linking 
remotely sensed information with crop growth models for yield 
prediction - A case study for sugarbeet. Seminar on Yield 
forecasting, EAO, 24-27 Oct, 1994, France. 
Conese C., Bacci L., Maraachi G., Cappellini V., Carla R. 
(1986). An integrated data bank for agricultural productivity 
by remote sensing. ESA SP 254:1273-1278. 
Dadhwal V.K. (1986). Remote sensing studies for wheat 
inventory and assessment. In 'Proc. 5th Asian Agricultural 
Symposium', (Nov. 19-20. 1986, Kumamoto. Japan), pp. 1-16.