The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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2000 and 2003. 
2) Simulated datasets by computer simulation model 
Because we don’t have the experiment measured data for 
grass and forest, in this study, we use computer simulation 
model-Radiosity model, which can simulate the 3D realistic 
scenes of vegetation (such as crops and forest) in the whole 
growing period, and get their radiation regime(Song Jinling 
2007(a); Song Jinling 2007(b)). We can product the look up 
table of the vegetation structure parameters, sun direction, view 
geometry and the corresponding simulated spectrum. And then 
the datasets of grass and forest are built by using the simulated 
data. 
2.1.2 Satellite Data Used in This Study 
1) Beijing-1 Microsatellite Multispectral images: 
Beijing-l(BJ-l) microsatellite is an applied earth observing 
microsatellite of China. It combines SSTL’s standard Disaster 
Monitoring Constellation (DMC) multi-spectral camera with a 
high resolution panchromatic imager, which can achieve the 
32m spatial resolution multi-spectral images and the 4m 
resolution panchromatic images synchronously. Beijing-1 
images have the same characteristics and capabilities as TM 
image at 2> 3^ 4 bands. In addition, BJ-1 remote sending data 
can also give us the good data of short cycle time and wider 
coverage. So it is necessary to generate the quantitative product 
of BJ-1 remote sensing data. 
2) MODIS LAI product: The eight-day composites of the 
LAI/fAPAR products(MOD15A2) version 4 pertaining to study 
sites were downloaded from the Land Processes-Distributed 
Achieve Center(LP-DAAC) Internet site. The product was 
composited over an eight-day period, where the selected value 
in a compositing period is that with the highest corresponding 
Fapar. The products are projected on the integerized sinusoidal 
and sinusoidal lOo grid, respectively. 
3) MODIS land cover product: the MODIS land cover product 
(MOD12Q1) version 4 was used (Table 1.) to investigate the 
biome assigned to the region of the study sites. The latest 
available MOD12Q1 product for our study period was 2004 
and the product was used with the assumption that the biome 
distribution does not change with a year. 
2,2 METHOD 
2.2.1 Beijing-1 LAI Map Generation 
In order to get the LAI distribution map at high spatial and high 
temporal resolution of Beijing-1 image, works were done 
shown in the flow chart (fig. 1) :l)the spatial matchment of the 
Beijing image and MODIS products; 2) Generation of 
Beijing-1 LAI maps; 3) MODIS Products Processing; 4) The 
generation of time serials LAI of the whole year at Beijing-1 
spatial resolution. 
1) The image registration of Beijing image and MODIS 
products 
Because MODIS products project is SIN project, which is 
different form BJ-l’s projection, in order to match with BJ-1 
image, MOIS products (MOD15A2 and MOD12Q1) are 
reprojected to the same projection with BJ-1 image, UTM 
projection. In this paper, we use the HDF-EOS and GCTP 
library in the actual dataset, which can provide coordinate 
transformation function, so can complete the conversion 
between the different projections easily. 
Based on the coverage of the scene of Beijing-1 image 
containing Beijing experiment sites, we select 4 tiles MODIS 
products (h26v04, h26v05, h27v04 and h27v05), accomplish 
the images Mosaic and then reproject them to UTM projection. 
Fig.2 is the reprojection image of MOD12Q1 product. 
According to MODIS PFT classification standard, the map is 
classified to the following classes: evergreen needleleaf trees, 
evergreen broadleaf trees, deciduous needleleaf trees, 
deciduous broadleaf trees, Shrub, Grass, Cereal crop, Broadleaf 
crop, Urban and built up and water. In this study, evergreen 
needleleaf trees, evergreen broadleaf trees, deciduous 
needleleaf trees and deciduous broadleaf trees are classified to 
one class, tree in the BJ-1 classification map(shown in fig.3), so 
in MOD12Q1 image, trees class represents evergreen 
needleleaf trees, evergreen broadleaf trees, deciduous 
needleleaf trees and deciduous broadleaf trees. Fig.3 is the 
Beijing-1 classification map, which is classified to six classes: 
broadleaf crop, cereal crop, forest, grass, urban and water. 
Fig. IThe flow chart of fusing MODIS and Beijing-1 image