This study investigates the applicability of SMA to the blended 
data generated with the enhanced STARFM (ESTARFM) by 
Zhu et al. (2010) using nine pair time-series imagery of 
Landsat-5 Thematic Mapper (TM) and TERRA Moderate 
Resolution Imaging Spectroradiometer (MODIS) covering the 
Poyang Lake area of China in 2004 and 2005. 
2. BACKGROUND 
2.1 ESTARFM 
ESTARFM (Zhu et al., 2010) utilizes two pairs of moderate- 
and coarse-resolution data on prior and posterior dates and one 
coarse-resolution data on the target date. It predicts the surface 
reflectance of the synthesized moderate-resolution data on the 
target dates using the linear combination of the spectra for 
predefined EM land cover classes in the same manner of linear 
SMA. There are four steps in ESTARFM implementation: 
(1) Two moderate-resolution scenes are used individually to 
search for pixels similar to the central pixel in a moving 
window. 
(2) The weights of all similar pixels are determined by the 
correlation coefficient between moderate- and coarse- 
resolution data (used as a measure of spectral similarity) 
and geographic distance between the target and similar 
pixels. 
(3) The conversion coefficients are calculated from the surface 
reflectance of moderate- and coarse-resolution data through 
linear regression. 
(4) The surface reflectance of moderate-resolution data on the 
target date are calculated using the surface reflectance of 
coarse-resolution data, weights, and conversion coefficients. 
Details in the procedure of ESTARFM is described in Zhu et al. 
(2010). 
2.2 Multiple endmember spectral mixture analysis 
(MESMA) 
Multiple endmember spectral mixture analysis (MESMA), an 
extension of SMA, allows EMSs to vary on a pixel-by-pixel basis 
(Roberts et al, 1998). Consequently, MESMA can reduce 
overall residual error and represent spectral variability in land 
cover more accurately than conventional linear SMA (Dennison 
and Roberts, 2003). MESMA is generally implemented by the 
following procedure: 
(1) An EM library is constructed from candidate EM spectra. 
(2) Optimal EMs are chosen with a EM selection method. 
(3) A series of SMA models using user-defined combinations of 
optimal EMs are applied to every pixel in the image. 
(4) The model with the minimum root mean square error 
(RMSE) is selected as the best one from the models that 
produce physically realistic fractions and meet model 
conditions. 
(5) Fractions produced by the optimal models are utilized to 
map the abundance of EM land cover components. 
(6) Shade fractions are removed through normalization or 
addition treatments. 
(7) EM land cover fractions are validated using higher spatial 
resolution images or field data. 
Roberts et al. (2007) describes more details in MESMA 
implementation. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
3. STUDY AREA 
The Poyang Lake area, the largest freshwater lake in China, was 
selected as the study area in this study (Figure 1). Poyang Lake 
(116° 13' E, 29? 9' N) located in the northern part of Jiangxi 
Province experiences the fluctuation of water level throughout 
year (Guo et al., 2005). Wetland vegetation in this area is an 
important food resource for wintering migratory birds, 
particularly for cranes. It also forms a favorable habitat for 
Oncomelania snails, the intermediate host for schistosomiasis 
(Zhou et al., 2005). The dramatic environmental changes in the 
past decades have consequently made it more difficult to map 
the change in the distribution of migratory birds and emergence 
of schistosomiasis. Efficient schemes for its control from the 
central and provincial government may be difficult to formulate 
because the effects of the environmental changes in this region 
(Chen and Lin, 2004). The emergence of highly pathogenic 
avian influenza, of which migratory birds are believed to be the 
carrier to poultry birds, is deeply related to the changes in land 
use and land cover in this region (Feare, 2007). 
4. DATA COLLECTION AND PREPROCESSING 
Nine time-series pairs of the Landsat-5 TM Level-1 products 
and Terra MODIS Daily Reflectance products (MOD09GA) 
acquired in 2004 and 2005 covering the study area were 
selected in this study (Table 1). All six spectral bands of TM 
images except for the thermal band (band 6) and corresponding 
MODIS bands (bands 1-4, 6, and 7) were utilized. 
Georegistration of the base TM image acquired on October 28, 
2004. was performed by co-registering the image to a published 
map. A first-order polynomial fit using 24 ground control points 
11670'0"E 117°0'0"E 
    
     
AN © 7) -29°0'0"N 
  
15 30 60 km 
rita 
Ï Ï 
116°0'0"E 117°0'0"E 
  
  
    
  
Figure 1. Study area 
  
Code TM MODIS |Code TM MODIS 
A, a | 2004/7/24 | 2004/7/22 | F, f | 2005/8/12 | 2004/8/8 
B, b | 2004/10/28 | 2004/10/29 | G, g | 2005/9/13 | 2004/9/12 
C, ¢ |2004/11/29]2004/11/28 | H, h | 2005/9/29 2004/9/30 | 
D, d |[2004/12/15 | 2004/12/16 | L,i |2005/10/31 | 2004/10/30 
  
  
  
  
  
  
  
  
  
  
  
  
E, e| 2005/35 | 2005/36 
  
Table 1. Input data for ESTARFM 
    
   
   
    
    
     
    
     
    
   
   
  
    
   
   
    
   
    
   
    
    
   
   
     
    
    
      
   
   
      
    
   
  
   
   
   
    
   
  
  
    
     
     
   
     
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