60 
Table 2. Summary of regional/district-level crop forecasts during 1997-98 crop season under CAPE Project. 
Crop 
State 
No. Districts 
Area (‘000ha) 
Production (‘000t) 
Forecast Date 
Rice(K) 
Bihar 
16 
1744.32 
2346.22 
21/11/97 
Rice (R) 
Orissa 
04 
115.17 
285.96 
14/05/98 
Mustard 
Assam 
04 
55.10 
25.90 
18/02/98 
Gujarat 
02 
241.38 
270.39 
05/03/98 
Haryana 
06 
467.67 
670.32 
03/03/98 
Rajasthan 
06 
793.33 
804.04 
05/03/98 
West Bengal 
02 
105.60 
89.20 
18/02/98 
Wheat 
Bihar 
29/ALL 
1679.09/1982.86 
3163.20/3731.51 
19/03/98 
Himachal Pradesh 
05 
241.54 
360.21 
02/04/98 
Haryana 
17 
2022.88 
7658.60 
19/03/98 
Madhya Pradesh 
32/All 
3726.83 / 4099.00 
6691.90/7273.80 
15/04/98 
Punjab 
17 
3187.39 
13174.89 
19/03^98 
Rajasthan 
14/All 
1379.57/2542.83 
3452.39/6723.45 
19/0J/98 
Uttar Pradesh 
51 /All 
7986.11 /8792.14 
19746.82/21305.60 
02/04/98 
Sorghum (R) 
Maharshtra 
06 
2374.43 
1307.33 
02/04/98 
Groundnut (R) 
Orissa 
02 
19.64 
27.28 
19/03/98 
CottonS 
Gujarat 
4 + (3) 
802.74+ (396.16) 
1667.43 + ( )# 
18/02/98 
Maharashtra 
03 
370.36 
321.21 
02/04/98 
Rajasthan 
01 
267.50 
# 
24/11/97 
$: Production in '000 bales; #: Acreage estimate only 
Higher accuracy of acreage estimate with TM in 
comparison to MSS has been noted for rabi sorghum in 
Solapur in 1987-88 season (Potdar et al., 1991). Higher 
inventory accuracy at village level for cotton has been 
noted for LISS-I1 in comparison to LISS-I in Haryana 
(Dutta et al., 1994). Singh et al., (1997) studied IRS LISS- 
I (72.5 mtr), LISS-1I (36.5 mtr), LISS-III (23.5 mtr) and 
WiFS (188 mtr) and have reported Kappa of 0.89, 0.88, 
0.91 and 0.82 in wheat-gram area. The use of higher 
spatial resolution can sometimes lead to lower 
classification accuracy than moderate resolution especially 
when training sites and locations are kept constant due to 
increased variance of training sites (Dadhwal and Parihar, 
1988; Medhavy et al., 1993). Markham and Townshend 
(1981) have shown that two counteracting factors affect 
classification accuracy as a function of spatial resolution. 
With higher spatial resolution, spectral heterogeneity 
increases leading to higher overlap between classes and 
decreased classifi-cation accuracy but proportion of 
boundary pixels reduces which leads to reduced 
m ^classification. The final accuracy obtained is 
‘dependent on relative importance of these two factors but 
cannot be predicted since it is also dependent in a complex 
way on relative location of categories within the feature 
space’ (Markham and Townshend, 1981). 
Inclusion of new spectral region having additional 
information is useful in crop discrimination. Thus, 
classification using MIR bands show higher crop 
separability in wheat, gram and mustard growing region 
(Dadhwal et al., 1989). Similar results have been obtained 
for rice-other vegetation separation in Orissa (Panigrahy 
and Parihar, 1992). Even in MIR region, better crop 
discriminability using TM5 in comparison to TM7 has 
been observed, which could be related to the higher within 
crop variability in TM7 (Dadhwal et al., 1996).