Chintapalli, S.M. 
  
Table 1 Coefficient of variation 
in crop intensity 
  
3.3.4 Tail-Head Ratio of NDVI 
Normalized Difference Vegetation Index (NDVI) which is a 
  
  
  
  
  
  
  
  
  
Water | Total Head Tail mathematical combination of the reflectance values of near infrared 
course | water | Reach | Reach and red bands of electromagnetic spectrum is found to be a potential 
no. course indicator of density and health of vegetation. THR jy, of a water 
1 1? 15 15 course is the ratio between the Tail Reach Area NDVI and Head 
5 9 10 13 Reach Area NDVI of the water course. The observed deviations of 
3 18 17 19 NDVI from head to tail reaches can be attributed to disparities in 
4 10 10 16 water distribution since the other factors contributing to crop 
S 17 1 27 condition such as soil characteristics, plant varieties, agronomic 
6 10 9 18 practices, meteorological parameters could be assumed to be uniform 
  
  
  
  
  
at micro level i.e., at water course level. THR yy; values, close to 
one, indicate better performance of water courses with less disparities in crop condition between Tail and Head regions. 
In the present study area, the disparities between Tail and Head regions of water courses were in general not significant 
as indicated by the THR ypy, values closer to 1. Location of water course in the parent canal and age of lining and extent 
of lining did not have any impact on crop condition disparities in Tail and Head regions. 
3.3.5 NDVI variability in Tail and Head Regions 
NDVI variability expressed as coefficient of variation reflects the variability in crop condition in a given area. High 
variability may be due to the differences in the resources available to crop such as soil, meteorological factors, agronomic 
practices, water etc. The NDVI variability can also be attributed to some extent to the degree of water availability. It is 
a common phenomenon happening in Indian irrigation systems that the tail reach farmers suffer from insufficient canal 
water supplies. In the present study area, the CV yy; Was more or less same in both head and tail regions. The CV yy, 
has decreased significantly over time from 1987. The CV y, values were mostly around 0.1 i.e., 10 per cent in all the 
water courses. 
4 IRRIGATION SYSTEM PERFORMANCE ASSESSMENT IN BHADRA IRRIGATION SYSTEM 
The Bhadra irrigation system has a command area of about 100,000 ha under Right Bank Canal served by 128 distributaries 
in the three canal divisions of Bhadravathi, Malebennur and Davangere. During the rabi (post-monsoon) season, rice is 
a dominant crop followed by sugarcane, irrigated dry crops and garden crops. There is no significant rainfall in the rabi 
season. Remote sensing inventory involved the analysis of 1986-87, 1988-89, 1989-90, 1992-93, 1993-94 and 1994-95 
rabi seasons satellite data for generating primary data on agricultural situation, for evaluating improvement in system 
performance through the years, and to diagnose problem pockets within the command area. Multi date satellite data during 
each rabi season were used to classify rice, dry foot crops and garden crops. Rice areas could be classified with an accuracy 
of more than 92 per cent, while the overall classification accuracy is 90 per cent. The actual cropping pattern derived from 
satellite data differed significantly from planned/recommenced pattern. The crop area has increased by 21724 ha and 
moreover it is dominated by high water consuming crops like paddy. The spatial variability in paddy intensity among the 
distributaries indicate that in most of the distributaries paddy is predominant. As a result, the water supply rate 
recommended in the plan was not sufficient to the real cropping pattern. In the absence of supplementary source of 
irrigation, the situation leads to unbalanced water supply which would be reflected through productivity. The satellite 
inventory also involved many methodological improvements in regard to spatial mapping of staggered rice transplantation, 
spectral modeling of rice yield and improved statistical design for ground sampling of rice yield, use of GIS and radiometric 
normalization of multi satellite-sensor-date data. The rice yield prediction model based on 1992-93 rabi harvested rice yield 
in sample plots and the corresponding satellite derived vegetation index, has been validated with farmer enquiries (Murthy 
et al. 1995). The spatial variability in rice transplantation period was taken care off by time compositing of satellite data 
over the appropriate calendar period before use in rice yield model. 
4.1 Indicators of irrigation system performance 
The satellite derived agricultural information along with relevant ground data was used to compute the irrigation system 
performance parameters as shown in table 2. The spatial and temporal analysis of disaggregated statistics indicated that 
overall irrigation intensity had increased by 15 per cent, paddy area by 13 per cent and paddy productivity by 26 per cent 
between 1986-87 and 1994-95 rabi seasons. The increase in paddy productivity was seen all over the command area. Paddy 
  
268 International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part B7. Amsterdam 2000.