SM% 5 Derived LAI 
Figure 2. Model fit of LAI and soil moisture to entropy 
3.3 Model Inversion 
The results of the LUT inversion are provided in Figure 3. Here 
LAI estimates from RADARSAT-2 entropy are compared to 
LAI derived from the optical data. Some scatter is present but a 
strong relationship between estimated and derived LAI is 
present. An underestimation is observed for lower LAI values 
while the reverse is true at high leaf area. 
  
oo 
  
E] 
9 = + 
6 ® > > 
s .* ++ 
3 # t Qt 
ee 7^ 7 
Inverted LAI 
uw A 
t 
  
  
  
  
  
Derived LAI 
  
Figure 3. Comparison of LAI inverted from RADARSAT-2 
entropy and LAI derived from optical imagery 
An LAI map derived from entropy using the Water Cloude 
Model with the LUT inversion approach is displayed in Figure 
4. 
RS2 derived LAI map on June 24th 
Legend 
LAL 
  
Crop Type 
{Barley 
iios 
Spring Wheat 
Figure 4. Map of LAI from RADARSAT-2 
4. CONCLUSIONS 
Leaf area index (LAI) is an important parameter for use in 
monitoring of crop condition and productivity. Although this 
crop parameter can be derived from optical sensors, reliability 
in crop monitoring is challenging with these sensors due to 
cloud cover interference. Thus monitoring activities will require 
the integration of data from radar sensors. 
During the 2009 AgriSAR campaign, RADARSAT-2 
polarimetric data were acquired over a site in Canada with 
extensive small grain production. Results from a sensitivity 
analysis were consistent with previously published results on 
corn and soybean LAI. Parameters indicative of characteristics 
of volume scattering were strongly correlated with LAI for 
wheat and oats. These included HV intensity backscatter and 
decomposition parameters derived from the Cloude-Pottier 
(entropy) and Freeman-Durden (volume scattering) 
decompositions. Variability in growth conditions for barley led 
to weaker, although still statistically significant, correlations. 
RADARSAT-2 entropy provided the greatest sensitivity to LAL 
A modified Water Cloud Model (WCM) was subsequently used 
to model entropy, LAI and soil moisture. A look up table 
approach was taken to invert the WCM using the entropy 
response, producing a pixel level map of LAI for the entire 
RADARSAT-2 image. 
These results are consistent with those found with corn and 
soybean crops (Jiao et al, 2011) and demonstrate the 
contribution that SAR can provide for monitoring crop 
condition. Agriculture and Agri-Food Canada has gathered 
additional data sets and these will be added to the analysis 
presented here to ensure robustness of this approach to crop 
monitoring. 
5. REFERENCES 
Attema, E.P.W. and F.T. Ulaby. 1978. Vegetation modelled as a 
water cloud. Radio Sci., 13, pp. 357-364. 
Jiao, X., McNairn, H., Shang, J., Pattey, E., Liu, J., and 
Champagne, C. 2011. The sensitivity of RADARSAT-2 
polarimetric SAR data to corn and soybean Leaf Area Index 
(LAI), Can. J. Rem. Sens., 37, pp. 69-81. 
  
  
  
  
  
  
   
  
  
  
   
    
   
   
   
   
  
   
    
    
  
  
   
   
  
   
  
  
   
   
  
  
   
   
   
   
  
  
  
  
  
  
   
     
  
  
    
  
   
  
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