XXIX-B8, 2012 
een | Red 
126] 138 
1.53] 1.52 
1.86] 1.87 
07] 1.89 
3.00] 2.65 
3.58] 3.08 
57] 2.05 
1ger 
m Mean 
3.231 2.49 
46] 2.00 
84] 2.36 
123] 2.87 
90] 3.18 
26] 3.67 
74] 2.56 
  
  
  
the observed and 
binations 
    
ase 2 
ase 4 
ase 6 
he MESMA of 
data 
a for those dates 
Observed data in 
ian the blended 
Es larger than the 
EM models. For 
ita reached higher 
the blended data, 
ch 5, 2005 and 
igh no significant 
ixels was found 
ifferences in the 
) were confirmed 
| August 12 and 
differences were 
ifferent dates and 
ance). 
1.00 — 
   
  
= 
2 
= 
E 0.75 
g 
&. 
= 0.50 
X 
0.25 - * 
2004/10/28 2004/11/29 2004/12/15 2005/3/5 
Target date 
1.00 « 
5 j 8S Casel 
9 i 
£ 0.73 # Case 2 
e 
= # Case 3 
& 0.50 
3 S B Case4 
B Case 5 
0,23 E Case 6 
2005/8/12 2005/9/13 2005/9/29 
Target date 
Figure 3. Kappa coefficients in EM class accuracy assessment 
6.2.2 Class accuracy assessment: Figure 3 illustrates the 
kappa coefficients in the accuracy assessment of the dominant 
EM classes in the LCF images derived from the observed and 
blended reflectance data with different EM libraries. Kappa 
coefficients were calculated in the same manner as in the 
accuracy assessment of hard-labeled classification maps using 
confusion matrices. Low kappa coefficients were chiefly 
brought by the unmodeled pixels, unmixing of the blended 
reflectance with unrealistic EM combinations, confusion 
between two water classes, and overestimation and 
underestimation of the LCFs from the blended reflectance due 
to the overestimation and under-estimation of reflectance 
values. Although Case 5 (observed EM library for observed 
data) achieved kappa coefficients higher than 0.86 on all seven 
dates, kappa coefficients for other five case varied on different 
dates. Kappa coefficients for the LCFs derived from observed 
data was generally higher than those derived from blended data 
in Comparisons 1 and 3. Low kappa coefficients for Case 1 on 
September 13, 2005 and those for Cases 2, 3, 4, and 6 on March 
5, August 12, September 13, and September 29 in 2005 implied 
the difficulty in collecting the candidate image EM spectra 
comprehensively in these seasons. Case 3 had lower kappa 
coefficients than Case 1 on all dates except for October 28, 
2004, indicating that the inclusion of too many candidate 
observed EM spectra on different dates that had true physical 
values led to the reduction in the representativeness of the 
optimal EM spectra. In contrast, kappa coefficients for Cases 4 
and 6 were as similar as or higher than those for Case 2 on most 
dates, implying that the observed and blended data had large 
difference in their spectral characteristics. 
6.2.3 Average absolute difference: The AADs between the 
LCFs derived from the observed and blended data were 
Summarized in Table 5. Pixels which were unmodeled in either 
case of the comparisons were excluded from the calculation. 
W2 had the highest AADs of LCFs on all seven dates in the 
three comparisons, obtaining higher than 22% of fractions. 
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 
    
(a) Comparison 1 (Case 1 vs. Case 2) 
Date GV | N/SA | W1 | W2 |Mean: class 
  
  
  
  
  
  
  
  
  
2004/10/28| 14.7 174] T1 3] 181 15.3 
2004/11/29 9.6 13.4| 10.9| 14.5 12.1 
2004/12/15 8.2 16.8| 15.1| 26.9 16.8 
2005/3/5 13.6 19.7] 13.31. 17.1 15.9 
2005/8/12 | 16.1 7.71 11.77] 24.4 15.0 
2005/9/13 | 11.1 8.6] 26.4| 32.6 19.7 
2005/9129 9.0 132] 46.6] 25.6 16.1 
Mean:date| 11.7 13:3] 15.1] 227 15.8 
  
(b) Comparison 2 (Case 3 vs. Case 4) 
Date GV | N/ST| W1 | W2 |Mean: class 
  
  
  
  
  
  
  
  
  
2004/10/28| 15.8|[ 18.2] 14.01 19.9 17.0 
2004/11/29} 11.1 13.5] 124} 16.5 13.4 
2004/12/15] 10.4| 17.4] 13.5] 204 15.4 
2005/3/5 19.5] 20.0] 13.6] 20.4 18.4 
2005/8/12 | 19.0] 11.7| 17.6] 320 20.1 
2005/93 | 11.5 9.0| 27.4| 35.9 20.9 
2005/00 T 16.8{ 13.51 17.91 279 19.0 
Mean: date| 14.9| 14.8} 16.6] 24.7 17.7 
  
(c) Comparison 3 (Case 5 vs. Case 6) 
Date GV | N/ST | W1 | W2 |Mean: class 
  
  
  
  
  
  
  
  
  
2004/10/28 | 15.81 17.7} 12.2} 23.5 17.3 
2004/11/29] 9.5| -136| 117] 169 129 
2004/12415] 0.3] -162] 162] 162 14.6 
2005/3/5 10,5] 19-11 203) 337 20.9 
2005/8412] 156] 130} 182) 350 20.4 
2005/913 | 11.0 911 260] 285 18.6 
20080291 1397 13.2] 19.0] 250 17.8 
Mean: date| 12.3 14.5] 17.6] 25.5 17.5 
  
  
  
  
  
  
  
  
* Unit: % of land cover fraction 
Table 5. Average absolute differences between the LCFs 
derived from the observed and blended reflectance data 
Mean values of the AADs for each EM class over time revealed 
the large difference (larger than 1196) between LCFs derived 
from observed reflectance and those derived from blended 
reflectance. The temporal variations of the AADs for the two 
ground EM land cover types (GV and N/S/I) were smaller than 
those for the two water land cover types (W1 and W2) in all 
comparisons. In Comparisons 2 and 3, the mean values of the 
AADs over the land cover classes on the four dates in 2005 
were smaller than those on the three days in 2004, suggesting 
that the optimal EM spectra chosen from blended candidate 
spectra were not so representative as those chosen from 
observed candidate spectra. When mean values of the AADs of 
LCFs for all EM land cover classes were compared, 
Comparison 1 using the EM spectra collected from the observed 
data on the prior and posterior dates achieved the highest values 
of the three comparisons. In summary, the LCFs derived from 
the blended data achieved strongest agreement with those 
derived from the observed reflectance data when the optimal 
EM spectra were chosen from the candidate spectra collected 
from the observed reflectance data on prior and posterior dates 
of the target dates. 
7. CONCLUSIONS 
This study investigated the applicability of MESMA to the 
blended reflectance data generated with ESTARFM utilizing