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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
identified them into four integration methods: the direct use of a
driving variable estimated from remote sensing information; the
updating of a state variable of the model; the re-initialization of
the model; and the re-calibration of the model. The temporal
resolution of remote sensing data is so difficult to reach the time
step requirement of crop models (from daily to weekly) that the
direct use of a driving variable method is rarely the case. But
the other three methods have been tested. Reynolds et al. (2000)
developed an operational crop yield model in Kenya by
introducing real-time satellite imagery into a GIS and the Crop
Specific Water Balance (CSWB) model of FAO. Clevers et al.
(1996) used ground and airborne radiometric measurements
over sugar beet fields to calibrate the SUCROS model. The
adjusted parameters and initial conditions were sowing date, a
erowth rate, light use efficiency and maximum leaf area. More
current researches were focused on estimating LAI from optical
remote sensing data, because LAI is the key variable during the
whole yield simulation in most of the mechanistic models.
Guerif et al. (2000) coupled the radiative transfer model SAIL
with the crop model SUCROS to re-estimate crop stand
establishment parameters and initial conditions for sugar beet
crops. Paul et al. (2003) used the SAIL model to link the EPIC
model with satellite data in the spring wheat yield estimation of
North Dakota. It was evident that how to measure or estimate
the input parameters describing crop canopy characteristics in
regional level for radiative transfer model is the key factor for
coupling radiative transfer model to crop models. If the standard
LAI product acquired by the Moderate Resolution Imaging
Spectroradiometer (MODIS) can be validated and be proved to
be suit for integration. of crop productivity models, the
operational yield assessment in regional level will be available.
The objective of this study is to develop one operational crop
yield model in regional level (North China) that integrates the
USDA (United States Department of Agriculture) EPIC
(Erosion Productivity Impact Calculator) model with NASA
MODIS LAI product, ground-based ancillary data, and a
Geographical Information System (GIS).
2. STUDY AREA
The study area is North China, which includes Beijing and
Tianjin, the two municipalities, Hebei, Shanxi, Shandong and
Henan Provinces (Figure 1). The area studied is 110^ E-123"E
longitude by 30" N- 43^N latitude and about 0.69 Million km”.
ses
Figure 1. The study region of North China covers a 6.9 X 10?
km" area (110 E-123 E longitude and 30 N-43 N latitude)
The North China lies in semi-arid and semi-humid zone with an
annual temperature sum of some 4800°C (>0°C), a spatially
and temporally strongly variable annual precipitation sum of
some 600 mm and cumulative annual radiation around
5200MJ/m°. The area is one of the most important grain
production bases of China and plays an important role in the
national food security. The population, cultivated land and crop
production in 2000 have been reached to 24%, 22% and 25% of
the national total respectively. The most widely distributed
crops in North China are wheat in winter and corn in summer.
3. METHODOLOGY
In this section, the crop productivity model (EPIC model) used
in the analysis, the method for integrating GIS with EPIC model,
and the processing method of combining MODIS LAI product
to spatial EPIC model are described.
3.4 EPIC Model
EPIC operates on a daily time step to simulate
evapotranspiration, soil temperature, crop potential growth,
growth constraints (water stress, stress due to high or low
temperature, nitrogen and phosphorus stress) and yield. EPIC
uses a single model for simulating all crops, each crop has
unique values for model parameters, which can be adjusted or
created by the user as needed. The crop growth model uses
radiation-use efficiency in calculating photosynthetic
production of biomass. The potential biomass is adjusted daily
for stress from the following factors: water, temperature,
nutrients (nitrogen and phosphorus), aeration and radiation.
Crop yields are estimated using the harvest index concept.
Harvest index are calculated from accumulated Leaf Area Index
(LAI), which increases as a non-linear function of heat units
from zero at the planting stage to the maximum value and then
declines from the maximum value to the low value or zero at
maturity. The harvest index may be reduced by high
temperature, low solar radiation, or water stress during critical
crop stages. Therefore, LAI is one of the most important
variables in EPIC model, which influences the simulation for
Photosynthetic Active Radiation (PAR); Potential Biomass
Increase; Harvest Index and Crop Yield.
3.2 Integrating GIS with EPIC Model
In order to facilitate the storage, manipulation, and handling of
complex EPIC spatial information, it is necessary to input all
raw spatial data into a geographical information system. The
data handling and analysis, which involves data editing,
conversion, interpolation, and overlay, can lead to the
application of GIS. With the aid of GIS, it is possible for the
EPIC to simulate crop yields efficiently at regional scale, and to
allow a flexible presentation of results according to the user's
needs.
There are several different approaches to integrate GIS with
simulation models, such as the embedding method, loose
coupling, and the tight coupling method. In this study, the loose
coupling approach was used to integrate GIS with the EPIC.
This approach uses two different packages directly. One is a
standard GIS package (Arcview GIS3.2) and another is EPIC
program (EPIC version 8120). They are integrated by
combining various data layers on the physical aspects of
agricultural environments such as soil, landform, and climate,
via data exchange using either ASCII or binary data format
