Full text: Proceedings, XXth congress (Part 7)

7. Istanbul 2004 
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
in the Department of Geography at Boston University (available 
fip://ersa.bu.edu/pub/rmynen i/myneniproducts/datasets/MODIS 
/MODIS BU/C4/). 
5. RESULTS AND DISCUSSION 
5.1 Validation of the Spatial EPIC Model 
The average yield of winter wheat and summer corn in North 
China for 1980s, simulated by the spatial EPIC model, can be 
seen from the Figure 2 and 3. The simulated yields are just 
compared with the statistical yields from the China Statistical 
Yearbook from 1982-1991, due to the lack of the actual yield 
data. The Table 2 shows the comparison results. The differences 
in percentage between simulated and statistical yield are mostly 
under 1096, except the situation in Beijing and Shandong. It is 
evident that crop yield of the area is underestimated by the 
spatial EPIC model, especially for Beijing. The reason is that 
Beijing and Shandong is the developed region in North China. 
The cropland in these regions are applied a very good field 
management with a better irrigation condition, fertilizer 
condition and so on. But only the simple and ordinary field 
operation parameters are inputted into the spatial EPIC model, 
which result in the underestimating situation. If the EPIC crop 
parameters established by USDA can be adjusted to be suitable 
for the application in North China, and the detailed field 
management information, such as the cropping system, 
irrigation schedule, fertilizer schedule and tillage schedule, can 
be obtained and be inputted into the spatial EPIC model. It 
should be possible to improve the simulation accuracy. 
Table 2. Comparison between the simulation yield from spatial 
EPIC model and the statistical yield (Ton/hectare) 
  
SUMMER CORN WINTER WHEAT 
  
Region 
  
Simulated Statistical Error Simulated Statistical Error 
BeiJing 2.979 4.820 38.2% 2.598 3.959 34.4% 
TianJin 3.686 3.864 4.6% 2.812 2.829 0.6% 
HeBei 3.647 3.623 0.7% 2.654 2.965 10.5% 
ShanDong 3.639 4.356 16.5% 2.669 3.388 21.2% 
HeNan 3.706 3323 11.596 3.002 3.322 9.6% 
ShanXi 3.796 3.993 4.9% 2.528 2.576 1.9% 
  
Note: “Simulated” means yield simulated by model; "Statistical" means the 
average statistical yield from the China Statistical Yearbook from 1982-1991. 
    
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Figure 2. The simulated yield per hectare of winter wheat by 
spatial EPIC model in North China 
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Figure 3. The simulated yield per hectare of summer corn by 
spatial EPIC model in North China 
5.2 Validation of Combining the Spatial EPIC Model and 
MODIS LAI Product 
Winter wheat — summer corn rotation is the dominant cropping 
system in North China. According to ground observation data, 
the key crop phenological stages are emergence (October), 
recovering (February), heading (May), maturity (June) of winter 
wheat and emergence (June or July), tasseling (August), 
maturity (October) of summer maize. The maturity of winter 
wheat and the sowing of summer maize usually occur within 20 
days. The leaf area index should reach maximum values during 
the heading (winter wheat) and tasseling (maize) stages. The 
colour-coded images of monthly MODIS LAI product for East 
Asia from year 2002 (September) to year 2003 (August) are 
shown in figure 4. From the consecutive images of monthly LAI 
in one year, it is evident to see the change profile of LAI value. 
But temporal resolution seems to be impossible to retire the 
model parameters to calibrate the spatial EPIC model. The 
higher resolution MODIS LAI product in 8-days or daily in 
some key stage should be obtained for the integration. 
Therefore, the validation of combining the spatial EPIC model 
and MODIS LAI product is not conducted yet. 
6. CONCLUSIONS 
The operational methodology of crop yield assessment in 
regional level was introduced in this study by integrating EPIC 
model with NASA MODIS LAI product, ground-based 
ancillary data, and GIS. The spatial EPIC model was developed 
and validated in North China firstly. The result indicated that 
the spatial EPIC model could simulate crop yield efficiently at 
regional level. But crop management information required by 
model, such as planting time, irrigation schedule and fertilizer 
schedule et al., is crucial for simulation accuracy and is not 
available by field measurement. Satellite remotely sensed data 
cans provide a real-time assessment of the magnitude and 
variation of crop condition parameters. Therefore the 
methodology of combining MODIS LAI product with Spatial 
EPIC model to improve yield simulation accuracy was built 
secondly, but it was not conducted and validated due to be lack 
of the necessary input data set yet. 
 
	        
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