The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B 7. Beijing 2008 
resolution ASTER data can be applied to fine resolution 
ASTER to produce ASTER surface reflectance at a target date 
(e.g. MODIS acquisition date). 
Figure 1 illustrates a processing flow from mid-resolution 
sensor digital number or radiance to MODIS-like surface 
reflectance at MODIS acquisition date. In this process, the mid 
resolution data are first precision registered and orthorectified, 
reprojected and resampled to MODIS coarse resolution. An 
unsupervised classification is applied to the mid-resolution data. 
A majority spectral cluster type of each MODIS pixel based on 
ASTER pixels is computed and also used as criteria to 
determine homogeneity of MODIS pixel. Relationships 
between ASTER data and MODIS surface reflectance for each 
cluster are then built using “pure” coarse-resolution 
homogeneous pixels. The “pure” pixel at MODIS resolution for 
each cluster type is determined by the percentage of majority 
cluster (> defined threshold). Only cloud-free pixels on both 
MODIS and ASTER image can be selected as samples. The 
MODIS-like surface reflectance is then produced at fine 
resolution using the resulting relationship from “pure” samples 
for the same cluster type. For small clusters that have not 
enough good samples to work on, a global relation regardless of 
cluster type will be used as backup algorithm. 
The GERM approach processes ASTER DN to MODIS-like 
surface reflectance band by band. ASTER and MODIS have 
similar bandwidths as Landsat ETM+. As shown in Table 1, 
ASTER instrument characteristics are nearly identical to 
Landsat ETM+ on band 2 to 5 except for a narrower swath 
width. The MODIS land bands have bandwidths corresponding 
to the ASTER and Landsat ETM+ sensor except that they are 
somewhat narrower than either. Many studies have shown that 
MODIS and ETM+ surface reflectance are very consistent and 
directly comparable (Vermote et al., 2002; Masek et ah, 2006). 
Therefore it is feasible to make consistent ASTER and ETM+ 
surface reflectance through high temporal MODIS observations. 
MODIS surface reflectance product is an appropriate data 
source as a reference data set not only because of the similar 
bandwidth but also because: 1) MODIS provides daily global 
coverage; 2) MODIS products have been partially validated and 
provide associated quality control flags and 3) MODIS products 
are freely available on-line and easy to access. 
3. APPLICATION EXAMPLE 
Using GERM approach, several ASTER images from different 
acquisition dates can be normalized to any clear MODIS 
acquisition date in MODIS-like surface reflectance. Figure 2 
illustrates the processing result over central Virginia. In the test, 
we used ASTER scenes that were acquired from Fall 2005 to 
Spring 2006 (10/23/05, 11/10/05, 1/27/06 and 4/10/06) and a 
MODIS image acquired on 4/10/2006. Figure 2(a) shows map 
stitched from original LIB ASTER data with different 
acquisition dates (b). The differences of seasonality/BRDF are 
obvious on this map. However, those differences have been 
reduced in the mosaic map (d) of the BRDF/seasonality 
corrected ASTER images by using MODIS surface reflectance 
(c) as a correction reference. The remaining differences 
between adjacent ASTER paths may reflect diverging land 
cover conditions through the growing season (e.g., same land 
cover on the ASTER acquisition date but different on the 
MODIS acquisition date thus causing a “l:n” non-function 
relationship) or heterogeneous aerosol loading within an 
ASTER scene (MODIS aerosol information was not used in this 
example). Results can be improved by including MODIS 
aerosol information in normalization and using additional 
ASTER images to distinguish land cover types and avoid “l:n” 
relationship in models. 
The original GERM approach doesn’t distinguish among 
different land cover types. It can normalize mid-resolution data 
using MODIS data with close acquisition dates such that land 
cover changes can be neglected during a short period. Figure 3 a 
shows the subset of normalized ASTER surface reflectance 
form original GERM approach. The right part of image from 
scene boundary (dash red line) is the adjusted ASTER data of 
April 10, 2006 using same day MODIS surface reflectance. 
Figure 3b is the subset of normalized ASTER surface 
reflectance from the improved GERM approach in this paper. 
The right part of image (ASTER scene on April 10) in Figure 
4b shows almost identical values to Figure 3b. However, the 
left part of mosaic is the ASTER scene acquired on January 27, 
2006 but normalized to the April 10, 2006 target date. The 
improved GERM approach (Figure 3b) shows more consistent 
results in the mosaic ASTER image. The lower reflectance such 
as river from original GERM approach seems too high in Figure 
3a, which may due to the inappropriate global relations from all 
samples without distinguishing different land cover types. 
Figure 4 shows the supervised classification (maximum 
likelihood) results from original DN image and the normalized 
MODIS-like surface reflectance using same training samples. 
Figure 4a is the classification result from Figure 2a and Figure 
4b is the classification map from Figure 2d. Figure 4b is 
obviously more reasonable than Figure 4a. The differences 
between different ASTER scenes have been greatly reduced in 
Figure 4b. 
4. CONCLUSION 
The improved GERM approach allows computation of surface 
reflectance from satellite digital number directly despite 
different acquisition dates between the mid-resolution data and 
MODIS data. It corrects atmospheric, BRDF and phenology 
effects through one step processing. ASTER images acquired 
from different dates can be normalized to a “standard” date by 
using MODIS data as reference. Therefore, the mosaiced 
ASTER surface reflectance can be used for a large area land 
cover classification. In the meanwhile, the resulting MODIS- 
like surface reflectance is also comparable to other mid 
resolution MODIS-like surface reflectance produced from this 
approach. This will allow the further land cover changes study 
and time-series analysis using data from multiple mid 
resolution sensors. 
There are two major advantages for GERM model using 
MODIS surface reflectance as a reference data set. First, the 
MODIS-like surface reflectance provides a way to standardize 
surface reflectance from different mid-resolution sensors to one 
“standard” and thus data from different sensors will be 
consistent and comparable. Second, in theory, the MODIS-like 
mid-resolution surface reflectance data may be used to retrieve 
mid-resolution biophysical parameters by using MODIS 
algorithm directly. 
The approach is better for ASTER images that were acquired 
during same season to the MODIS acquisition (target) such that 
land cover and phenology changes are all point to one direction