NORMALIZING ASTER DATA USING MODIS PRODUCTS FOR LAND COVER 
CLASSIFICATION 
F. Gao a ’ b ’ *, J. G. Masek a 
a Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA 
b Earth Resources Technology, Inc., 10810 Guilford Road, Annapolis Junction, MD 20701, USA 
KEY WORDS: ASTER, MODIS, Landsat, Surface Reflectance, Radiometric Normalization, Land Cover, Classification 
ABSTRACT: 
ASTER has similar bandwidths and spatial resolution to Landsat and is an important component of the mid-resolution data archive. 
However, the limited duty cycle of ASTER and relatively small scene size has resulted in a “patchwork” archive of global imagery. 
The changes of solar geometries (BRDF) and phenology complicate land cover classification and change detection especially when 
comparing to the historical Landsat data archive. In this paper, we use the improved general empirical relation model (GERM) 
approach to normalize ASTER images acquired from different dates to one reference MODIS data. The resulting MODIS-like 
surface reflectance from different ASTER scenes can be mosaiced for land cover classification. Land cover change detection also 
becomes possible while comparing ASTER images to other mid-resolution data produced from same approach. 
1. INTRODUCTION 
ASTER (Advanced Spacebome Thermal Emission and 
Reflection Radiometer) instrument aboard the Terra platform 
acquires Earth imagery at a resolution of 15-90m resolution 
using 14 VNIR-SWIR-TIR bands (Abrams, 2000). The mission 
has now acquired about 2x global cloud-free coverage since 
launch in December 1999. ASTER is critical for characterizing 
land cover changes during the 2003-2010+ period especially 
while Landsat 7 ETM+ has encountered the Scan-Line 
Corrector problem since May 2003 and the age of Landsat 5 are 
threatening the continuity of Landsat data record before a new 
Landsat mission starts operation in 2011 (Wulder et al., 2008). 
ASTER data among other mid-resolution (10-60m) sensors 
such as the Advanced Wide Field Sensor (AWiFS) and Linear 
Imaging Self-Scanner (LISS) III sensors aboard Indian Remote 
Sensing Satellite (IRS) and the Charge Coupled Device (CCD) 
camera aboard China-Brazil Earth Resources Satellite (CBERS) 
have been considered as a potential substitute for Landsat data 
continuity if Landsat data gap starts (Powell et al., 2007). 
However, the limited duty cycle of ASTER and relative small 
scene size (60kmx 60km) has resulted in a “patchwork” archive 
of imagery acquired from different points in the growing season. 
Images acquired from different dates are affected by changing 
solar geometry (BRDF) and vegetation seasonal changes 
(phenology), which complicates land cover classification and 
change detection. The lack of a blue band limits its abilities in 
retrieving consistent surface reflectance using standard "dense 
dark vegetation" approaches for aerosol extraction, and thus 
limits biophysical estimation. A consistent data stream from 
different mid-resolution satellites (such as ASTER and Landsat) 
is a key to combining data sources for integrated analysis. 
A general empirical relation model (GREM) approach was 
recently developed to correct mid-resolution satellite digital 
number (DN) to surface reflectance using MODIS surface 
reflectance as a reference data set in one step processing (Gao 
et al., 2008). Different from the physical atmospheric correction 
approach, GERM approach is a relative atmospheric correction 
approach and therefore the corrected surface reflectance is a 
MODIS-like surface reflectance. Results from GERM approach 
shows that it can achieve the similar accuracy to the physical 
model approach. Data from Landsat TM/ ETM+, IRS-P6 
AWiFS and TERRA ASTER were integrated in one consistent 
mid-resolution data set for time-series data analysis by using 
MODIS (Moderate Resolution Imaging Spectroradiometer) 
observations acquired from close dates (Gao et al., 2008). 
The GERM approach uses linear transformations between mid 
resolution data and MODIS surface reflectance and requires the 
close acquisition dates between Landsat-like data and MODIS 
data. It needs adjustment if acquisition dates between mid 
resolution data and MODIS data are different especially when 
land cover or phenology changes. A general form of GERM 
approach needs to incorporate different relations based on land 
cover type. Each land cover type may present different changes 
(relations) between mid-resolution data and MODIS surface 
reflectance. 
In this paper, we present an improved GERM approach and 
normalized ASTER data that were acquired from different dates 
and locations to one target date using MODIS data as references, 
thus the ASTER “patchwork” can be mosaiced in a consistent 
MODIS-like surface reflectance for land cover classification. 
2. APPROACH 
The basis of this approach is that homogeneous pixels of the 
same land cover type have the same surface reflectance 
regardless of patch size. Thus the BRDF/seasonality changes of 
homogeneous pixels should also be the same for different patch 
sizes given each land cover type does not split (1 to n, change in 
one direction is fine) during a short period. The relationship of 
each land cover type between acquisition date and target date 
will stay same for different resolution images. Therefore, the 
relationships built on the MODIS data and aggregated coarse 
* Corresponding author. 
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