XIX-B8, 2012 
ake in China, was 
1). Poyang Lake 
n part of Jiangxi 
level throughout 
in this area is an 
migratory birds, 
rable habitat for 
r schistosomiasis 
al changes in the 
> difficult to map 
Is and emergence 
control from the 
icult to formulate 
ges in this region 
ighly pathogenic 
'elieved to be the 
? changes in land 
‚OCESSING 
Level-1 products 
cts (MOD09GA) 
study area were 
ral bands of TM 
nd corresponding 
ed. 
d on October 28, 
ge to a published 
nd control points 
117°0'0"E 
  
117?0'0"E 
  
MODIS 
12 | 2004/8/8 
13 | 2004/9/12 | 
29 | 2004/9730 | 
/31 [2004/10/30 
  
  
  
  
  
(GCPs) and the nearest neighbor resampling produced an 
average RMSE of 0.25 pixel. Each of image-to-image 
registration between the georegistered base image and the other 
eight TM images produced an average RMSE of less than 0.25 
pixel using a first-order polynomial fit with more than 30 GCPs. 
Brightness values of these geometrically corrected TM images 
were converted to the ground reflectance through the 
atmospheric correction using ACORN. The c-correction method 
(Teillet et al., 1982), a semiempirical function primarily based 
on cosines of the incident and reflected illumination angles, was 
applied to minimize the topographic effects. In contrast, only a 
map reprojection was performed to the time-series MODIS data. 
This is because MODO9GA products were geometrically 
accurate when compared with the TM data, and they have 
already been atmospherically corrected. 
Field data of the land cover conditions in the study area were 
collected in December, 2007 and April and May, 2008. In order 
to comprehensively include all land cover types throughout the 
study area, the locations of the field record collection were 
determined with the reference of remotely sensed images in 
Google Earth. We recorded the GPS coordinates at more than 
350 centers of 30 m by 30 m squares covered with single land 
cover types. Land cover types and existence of land cover 
changes were also recorded at each location. The field records 
were classified into four EM land cover classes: (1) green 
vegetation (GV); (2) non-photosynthetic vegetation (NPV), 
soils, and impervious surfaces (N/S/I); (3) bright water (W1); 
and (4) dark water (W2). For each TM image, 240 records 
consisting of 60 records per EM land cover class were selected 
from all field records. We chose the field records for image 
considering the land cover changes by referring the information 
on land cover changes obtained through the interviews with the 
local residents and researchers. Therefore, all sets of field 
records were different for every TM images. These field records 
for each TM image were utilized as the reference in collecting 
the candidate image EM spectra and in the class accuracy 
assessments of the land cover fractions (LCFs) derived through 
the MESMA of blended reflectance. 
5. METHODS 
5.1 Examination of input data combinations for ESTARFM 
The agreement between observed reflectance and the reflectance 
fused with blending techniques is dependent on the combination 
of input data as Watts et al. (2011) demonstrated. In order to 
investigate the input data combinations that could achieve 
highest agreement between the observed and blended 
reflectance, this research tested all of the possible combinations 
of the TM and corresponding MODIS images on the TM 
Observation dates. Testing all possible input combinations 
revealed what data combination was appropriate to blend the 
relectance in which particular season, particularly when the 
water level changed rapidly. 
The agreement was assessed based on the mean values of the 
average absolute difference (AAD) between observed and 
blended reflectance for each band. Since no additional TM data 
acquired during the studied period was available and the 
ESTARFM required a prior and a posterior moderate-resolution 
remotely sensed data, the blended data were generated for the 
seven TM observation dates: October 28, November 29, and 
December 15 in 2004, and March 5, August 12, September 13, 
and September 29 in 2005. The input data combinations 
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 
    
followed the chronological order: the prior data were acquired 
before the target date and the posterior data were acquired after 
the target date. 
5.2 Investigation in the applicability of MESMA to blended 
data 
The applicability of MESMA to the blended reflectance was 
assessed from three perspectives: (1) percentage of modeled 
pixels; (2) agreement of image dominant land cover classes (EM 
classes with highest LCFs in pixels) and reference dominant 
classes; and (3) AAD between the LCFs derived from observed 
TM data and those from blended data. The blended reflectance 
generated with the input data combinations that achieved 
highest agreement between the observed and blended 
reflectance on the seven TM observation dates were utilized as 
the blended input data for MESMA. 
To establish the approach for EM library development, we made 
the three comparisons in unmixing the observed blended 
reflectance using five different EM libraries shown in Table 2. 
These six cases were set in order to examine whether the 
candidate EM spectra collected from blended reflectance data 
need to be included in the candidate spectral library for the 
MESMA of blended reflectance. Comparisons were made 
between Case 1 and 2, Case 3 and 4, and Case 5 and 6 utilizing 
all observed and blended input data on seven target dates. 
We utilized the VIPER Tools, a plug-in software under ITT 
ENVI, in the application of MESMA (Roberts et al., 2007). 40 
spectra for each EM land cover class of every images were 
randomly selected from the image pixels at the locations of 60 
field record. The candidate EM libraries were constructed for 
each input by combining all EM spectra for each case in Table 
2. 20 optimal EM spectra (five per land cover class) were 
chosen from each EM library. using the EM average RMSE 
(EAR) produced by a spectrum that was used to model all other 
EM spectra in the same class (Dennison and Roberts, 2003). 
one spectrum of photogrammetric shade was added to account 
for the spectral variation of reflectance data. When a series of 
SMA models were applied to every pixel of each reflectance 
data, two-, three, and four-EM models were applied in this 
study. The combinations of EM land cover classes used in this 
study are shown in Table 3. Maximum RMSE (2.596 
reflectance), maximum fraction (10546 reflectance), minimum 
fraction (-5% reflectance), and maximum shade fraction (50% 
reflectance) restrictions were applied to the SMA models 
(Michishita et al., 2012). In the selection of optimal models for 
every pixels, we empirically determined the RMSE threshold 
between two- and three-EM models as 0.2%, that between 
three- and four-EM models as 0.4%, and that between two- and 
  
  
  
  
  
  
  
  
Reflectance EM spectra 
Comp. |Case| used for collected from 
unmixing Observed Blended 
1 1 | Observed Prior and posterior - 
2 Blended Prior and posterior - 
2 3 | Observed | Prior, target, and posterior - 
4 Blended Prior and posterior Target 
3 5 | Observed - Target 
6 Blended - Target 
  
  
  
  
  
  
  
Table 2. Case settings in the applicability investigation of 
MESMA