important aspects of sampling design. Smaller sample 
segment sizes of 7.5x7.5 km and 5x5 km and use of larger 
sampling fraction have improved district-level estimates. 
Conventionally, previous season’s FCC prints were 
stratification base and year to year fluctuation were not 
accounted. Digital stratification studies carried out for 
mustard crop reduced the sampling error (coefficient of 
variation of estimate) and improved efficiency of 
stratification for mustard in Rajasthan (Sankhla et al., 
1997). 
Ground truth data collection 
Extension of training signatures collected at a place 
to some other distant areas within the stratum does not 
provide satisfactory results. Locating ground truth sites 
within each segment and using them for the classification 
of that segment only has lead to improvement in accuracy. 
Acquisition Date 
In a site having wheat, gram, mustard and lentil in 
Hisar (Haryana), large variation in classification accuracy 
due to acquisition date has been demonstrated (Dadhwal et 
al., 1989; Dadhwal et al., 1996). Use of early acquisition 
for wheat acreage estimation in comparison to peak 
vegetative growth stage has given underestimation in 
Haryana (Dadhwal and Parihar 1990). The early stage data 
are associated with higher between field as well as within 
field spectral variability (Ruhal et al., 1988), which will 
adversely affect crop discrimination using supervised 
MXL approach. In case of rice acreage estimation in 
Orissa using NOAA-AVHRR data (1989-90 season), as 
acquisition October 21 gave estimate closest to DBS with 
earlier acquisitions having overestimates and later 
acquisitions underestimates. The latter is due to 
progressive ripening and harvest of rice. In case of wheat 
and mustard acreage estimation in Rajasthan, late 
December/early January acquisition are optimal for 
mustard with large errors in wheat estimates while 
February acquisition is optimal for wheat but mustard 
acreage is underestimated (due to ripening) (Purohit et al., 
1997). 
Sensor Characteristics 
The spectral, spatial and radiometric characteristics 
of the sensor determine crop discrimination to a large 
extent. The narrow bands of TM and LISS-I and LISS-II 
can lead to higher classification accuracy than MSS 
(Dadhwal and Parihar, 1988). The low radiometric 
resolution of 6 bit (MOS MESSR) leads to a lower 
classification accuracy than with MSS and LISS-I 
(Dadhwal and Parihar, 1990). 
Table 1. Study crops, areas and geographic area coverage in Crop Acreage and Production Estimation (CAPE) Project Phase I and II 
CROPS 
PHASE! 
PHASE-!! 
State* (No. of districts; F = Full) 
Geog. Area 
(sq km) 
State(No. of districts; F = Full) 
Geog. Area 
(sq km) 
Wheat 
HARyana(F), PUNjab(F), 
Madhya Pradesh(8), 
RAJasthan(8), Uttar Pradesh(24) 
352582 
HAR(F), PUN(F), MP(32), RAJ( 17), UP(F), 
BIHar(27), Himachal Pradesh(5) 
1068692 
Rice(K) 
ORlssa(F), Tamil Nadu(F) 
285469 
ORI(F), TN(F), Andhra Pradesh(12), ASSam(F), 
BIH(F), HAR(9), KARnataka( 12), MP(17), 
PUN(F), UP(39), West Bengal(F) 
1339770 
Sorghum(R) 
MAHarashtra(3) 
47696 
MAH(6), KAR(7), AP(5) 
339006 
Mustard 
GUJarat(2), MP(1), RAJ(6), 
UP(2) 
128822 
GUJ(2), MP(2), RAJ(10), UP(8), ASS(10), HAR(5), 
PUN(3), WB(9) 
272107 
Groundnut(K) 
GUJ(2) 
28570 
GUJ(6), AP(7), KAR(6), MAH(5), TN(11) 
364603 
Cotton 
AP(3), GUJ(l), HAR(2), 
MP(2), MAH(6), PUN(4) 
176039 
AP(7), GUJ(8), HAR(3), KAR(5), MP(4). 
MAH( 12), ORI( 1 ), PUN(5), RAJ(2), TN(5), UP( 1 ) 
512354 
TOTAL 
1019178 
3896532 
: Abbreviation for state name is indicated in capitals