62 
yield modelling has a number of sources of errors such as 
(a) acquisition date, (b) spatial resolution of sensor (vis-a- 
vis field size and crop proportion), (c) the spectral indices 
used, (d) atmosphere introduced uncertainties, and (e) 
sensor-to-sensor calibration differences and their long term 
spectral response. 
The coarse resolution NOAA AVHRR data has been 
investigated for yield modelling as its repetivity provides 
matching data sets over large regions for such an 
application. The major analysis steps in use of this data are 
geometric correction, and multitemporal composing 
(weekly/fortnightly) and NDVI images are generated. 
Their further use for yield modelling could take through 
any of the following parameters : Peak NDVI, NOAA- 
AVHRR data has been used different approaches, such as 
(i) regression at critical date with all crops (Bullock, 1992), 
(ii) Cumulative NDVI with linear decomposition for crop 
separation (Quarmby et al.-, 1993), (iii) Multi-year 
comparison with normalisation through computation of 
Vegetation Condition Ind$x (VCI) 
VCI = 100 x (NDVI - NDVImin) / 
(NDVImax - NDVImin) 
where NDVImin and NDVImax are the minimum and 
maximum NDVI for that week from all the years of data. 
Physical Models of Crop Growth 
Crop growth models simulate growth and develop 
ment of agricultural crops based on an understanding of 
underlying physical and physiological variables (Bouman, 
1995). The most simplest crop growth model is Monte ith’s 
(1981) light interception model, which is given by, 
Wd = e.S.f.dt 
where, Wd = dry weight of the crop (g m-2) 
e = light use efficiency factor (g J-1) 
S = incoming global irradiation (Jm-2d-l) 
f = fraction light interception 
t = time (d) 
Fraction light interception can be estimated using the 
relationship, 
f= {1 -exp(-K.LAI)} 
where, K is the extinction coefficient for visible radiation 
(0.5 - 0.8, depending on crop type). Hence the problem of 
estimating crop production reduces to quantifying LAI, i 
and K values, i and K values have to be derived from field 
experiments, whereas remote sensing techniques can be 
used to estimate LAI. 
RS observations can be integrated with crop growth 
models in two ways: direct data input and ‘steering’ 
simulation results (Bouman, 1995). In direct data input 
time-series of LAI values estimated from some Vis (e.g. 
WDVI) are used to replace subroutines that simulate the 
development of LAI from environmental variables. This 
use of measured LAI as ‘forcing function’ generally 
increases the accuracy of crop growth simulation models. 
In another approach RS information can be used to ‘steer’ 
the crop growth models by adopting the model parameters 
so that the simulated values match with RS estimated 
values of LAI. This is done when the number of RS 
observations is limited and/or initial conditions are not 
known. This approach is used to optimise simulation 
models. Clevers and van Leeuwen (1995) have linked 
RS information with SUCROS (Simplified and 
Universal Crop Growth Simulator) crop growth model 
for yield prediction of sugar beet. 
RS in Post-harvest Yield Estimation 
The conventibnal procedures for crop yield 
estimation involve Crop Cutting Experiments (CCE) 
conducted at harvest in the plots selected using a pre 
designed sampling scheme using available ground data. 
In these procedures, crop vigour is not used for 
stratification. Use of RS data could improve confidence 
level in estimated yield (Singh et al., 1992; Murthy et 
al., 1996). 
For remote sensing based yield assessment a 
combination of radiation interception models and 
canopy temperature models seems to be most desirable 
(Horie et al., 1992). 
RS-BASED EMPIRICAL-STATISTICAL MODELS 
INDIAN EXPERIENCE 
Over the past decade, a number of crop yield 
forecasting models using RS inputs have been developed 
and used in making crop forecasts in CAPE project. 
These have been reviewed by Dadhwal and Ray (1999). 
These include (a) Single date RS-based models for 
wheat (Bihar, Haryana, MP, Punjab, UP), kharif rice 
(Bihar, Orissa, WB), mustard (Assam, Gujarat), 
sorghum (Karnataka, Maharashtra) and cotton (Gujarat, 
Maharashtra). The yield variability explained by single 
date RS data based models range from 6.0 per cent for 
sorghum in Karnataka to 83.0 per cent for wheat in MP, 
(b) The multidate approach using crop profile fits an 
analytical growth curve to spectral data and derives the 
following parameters: (i) time of peak, (ii) value of peak 
(iii) area under the curve, (iv) full width at half 
maximum of the profile, (v) average slope of the post 
heading phase. These parameters are then related to 
yield. Spectral profile related growth parameters derived