Analysis shows strong correlations for these two crops. 
The coefficient of determination (R?) for wheat for four 
different correlations (LAIG - NDVIgz,4, LAIG - NDVIg, 
LAIgG - NDVly; s and LAIG — NDVlIys) were found to be 
0.70, 0.69, 0.72 and 0.71, respectively. In contrast, R° for 
rice for these relationships were poor and were found to 
be 0.02, 0.08, 0.2 and 0.1, respectively. 
4. CONCLUSION 
The models for estimation of LAIg from freely available 
Landsat 5 TM were developed for the conditions in 
Australia. The resolution of satellite images are 
reasonably good to correlate point data measured at 
sample points in different farms. Developed models can 
be applied with any satellite images which are having 
thermal bands (e.g. NOAA-AVHRR, ASTER, MODIS) 
All models developed for corn and wheat have very 
promising co-relations for the derivation of LAIg. These 
strong correlations allow for the potential use of 
developed models to estimate ground based LAI and they 
can be used to address various agricultural landscape 
issues within irrigated agriculture of Murray Darling 
Basin in Australia. However the corresponding 
relationships for rice are weak, most probably this is due 
to the mixed spectral reflectance of plant-water-soil, as 
rice crop is grown in flooded fields throughout the 
cropping season. The possible reasons for weak 
relationships for the rice crop are currently being 
investigated using various modelling techniques and field 
investigations which will be reported separately in the 
future research. 
Among all models, the atmospherically corrected 
relationships are higher in accuracy. Moreover, the 
correction based MLS model showed the highest 
coefficient of determination. However, any model could 
be used based on the availability of data and requirement. 
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