wheat) fields. Total LAI was measured at 52 sample sites using 
an LAI-2000 (Li-Cor, Inc., Lincoln, NE) plant canopy analyser 
under diffuse light conditions. A subset of the satellite data 
were used in the analysis presented here (Table 1). 
RADARSAT- acquisitions which occurred within one week of 
optical and ground data collection were selected for further 
study. One in situ soil moisture station was present in the study 
site, measuring volumetric soil moisture at a depth of 20 cm. 
  
Optical | Optical Field RSAT-2 | Beam 
Data Sensor LAI Data Mode 
2 Jun RapidEye | 1 Jun 3 Jun FQ19 
25 Jun TM 24 Jun 24 Jun FQ14 
17 Jul | RapidEye | 15 Jul 
  
19 Jul ™ 21 Jul FQ19 
25 Jul | RapidEye | 24 Jul 25 Jul FQ2 
10 Aug | RapidEye 14 Aug FQ19 
  
  
  
  
  
  
25 Aug | RapidEye | 25 Aug 
  
Table 1. Data acquired during AgriSAR 2009 
2.2 SAR Data Processing 
The RADARSAT-2 data were processed using PCI Geomatica 
and the SAR Polarimetry Workstation. Prior to extracting the 
polarimetric information, a boxcar filter with a 5 by 5 kernel 
size was applied to the scattering matrix data to suppress SAR 
speckle. After filtering the covariance matrix was converted to a 
symmetrized covariance matrix from which intensity backscatter 
(HH/HV/VV) and intensity ratios (HH/VV, HH/HV, HV/VV) 
were extracted. Polarimetric variables including total power, 
pedestal height and complex correlation coefficient (HH-VV) 
were also extracted from the covariance matrix. Both Cloude— 
Pottier and Freeman-Durden decompositions were performed 
on the complex RADARSAT-2 data. Three parameters are 
derived from the Cloude-Pottier decomposition, namely entropy 
(H), anisotropy (A), and alpha angle (a). Freeman-Durden 
decomposition partitions the total power for each image pixel 
into contributions from three scattering mechanisms: single- 
bounce, double-bounce, and volume scattering. 
Information on the range and azimuth spacing, nadir angle, and 
satellite altitude for SLC format SAR data were obtained from 
the SAR production file. Using this information, all the SAR 
parameters derived above were converted from slant to ground 
range, followed by an ortho-rectification and geo-referencing 
procedure using a set of ground control points and national road 
network vector data. 
2.3 Optical Data Processing 
Atmospheric correction and surface reflectance retrieval of the 
optical data were accomplished using ATCOR2 implemented in 
PCI Geomatica. Images were ortho-rectified using platform 
ephemeris information and models of the internal sensor 
distortion, ground control points (GCPs) and Digital Elevation 
Models. 
LAI was estimated from the Landsat and RapidEye data using 
the Modified Triangular Vegetation Index (MTVI2) and a 
nonlinear curve fitting procedure, as described in Jiao et al. 
(2011). Strong correlations were found between MTVI2 
calculated for near coincident Landsat and RapidEye 
acquisitions (R? of 0.96), as well as between satellite derived 
LAI and ground measured LAI (R? of 0.78). 
An object-based approach was used to compare homogeneous 
zones of LAI derived from optical data, to SAR response within 
these homogeneous objects (Jiao et al, 2011). This spatial 
averaging assists in the reduction of noise inherent in the SAR 
data. Optical LAI maps were segmented using Definiens 
software. 
2.4 The Water Cloud Model 
According to  Attema and Ulaby (1978), the power 
backscattered by the whole canopy (6?) can be represented as 
the incoherent sum of contributions of the vegetation, ( 5» ), 
and the underlying soil, (5?.). The modification of the model 
by Prevot (1993) was selected here as it incorporates LAI as a 
descriptor of vegetation development. In this model, SAR 
backscatter from a canopy at a given incidence angle (O) can be 
written as: 
For the whole canopy: 
soil 
0° 0 2 0 
Gg mg. REG, 
(I) 
where the vegetation contribution is: 
ol AL. cost(1 — 7?) 
(2) 
and the soil contribution can be related to the volumetric soil 
moisture content M, expressed in (76), as: 
o3, -C* DM, G) 
soil 
with 
2 — — 
7“ =exp(-2BL/cos0) (4) 
where 3? is the two-way attenuation through the canopy layer, 
L is the LAI, expressed in (m^m?) , the backscatter coefficients 
o*,g?. and g?^ are expressed in power units. 4,B,C,D and E 
soi veg 
are model coefficients to be defined by experimental data. 4, B 
and E are parameters which depend on canopy type. E is a 
positive value. Parameters C and D are dependent on soil 
moisture. 
Grouping these terms, the model can be expressed as follows: 
(5) 
o? — AI? cos0(1— exp(-2BL/ cos0)) - o? exp( -2BL/cos0) 
soi 
With only one soil moisture station, too few data were available 
to parameterize the soil moisture coefficients in the WCM. 
Consequently the parameterization of C and D, as determined 
by Jiao et al. (2011), were used here. Jiao et al. (2011) found 
that for broadleaf crops, when LAI exceeds 1.0 the SAR 
response is dominated by the vegetation contributions, with 
  
  
   
   
      
    
    
  
  
     
      
     
    
   
     
   
     
   
  
    
  
   
  
  
  
  
  
    
   
    
  
   
    
      
    
  
    
  
   
   
     
     
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