International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
between these two packages, which do not have a common user
interface. The advantage of this approach is that redundant
programming can be avoided. Map input, data handling, spatial
analysis, and map output capabilities of GIS are used for the
preparation of the land resource database required by the EPIC.
The EPIC processing is outside of the GIS.
3.3 Combining Spatial EPIC Model and MODIS LAI
Product
The integration of MODIS LAI product with the spatial EPIC
model is achieved by using two distinct methods. The first
method is the updating of LAI value in EPIC model. The LAI
value simulated by EPIC model in some key stage of crop
growth is updated by using the MODIS LAI product directly. In
the second method, the time series of MODIS LAI product is
used to calibrate the spatial EPIC model. Calibration was
performed to adjust some model parameters: the maximum
potential LAI of the crop; the leaf area decline rate (RLAD);
and the time when green LAI begins to decline (DLAI).
4. MATERIALS AND DATA REQUIREMENTS
The MODIS LAI product and some important input data for
EPIC model, such as weather, soil, and management data, are
introduced here.
4.1 Weather Data
EPIC uses a stochastic weather generator to generate daily
weather from monthly climatic parameters. The basic data set
needed for each site is a record of monthly maximum and
minimum temperatures, precipitation, Standard Deviation (S.D.)
of maximum daily air temperature, S.D. of minimum daily air
temperature, S.D. of daily precipitation, skew coefficient for
daily precipitation, probability of wet day after dry day, and
probability of wet day after wet day. The weather data from year
1981 to 1990 in this study is from Global Daily Summary
produced by National Climatic Data Center from 256 available
terrestrial stations in China. This history weather data were used
to validate the spatial EPIC model. When the MODIS LAI
product is integrated with the spatial EPIC model in order to
estimate winter wheat yield of North China in 2003, data from
more than 200 weather stations of North China will be applied.
Kriging method aided by climatologically and topographically
interpolation with quality control model is applied (Tan et al.
2002).
4.2 Soil Data
EPIC can accept up to 20 parameters for 10 soil layers.
However, only a minimum of seven parameters is required:
depth, percent sand, percent silt, bulk density, PH, percent
organic carbon, and percent calcium carbonate. Other soil
parameters can be estimated by EPIC itself. Therefore only
these seven parameters in four layers are applied in this study.
The soil-depth intervals are 0-0.1, 0.1-10, 10-30, 30-50, and
50-80 cm. All soil databases are provided by the Global Soil
Task cooperated by the Data and Information System (DIS)
framework activity of the International Geosphere-Biosphere
Programme (IGBP) (Scholes et al. 1995). The highest spatial
resolution of this database is 5 min x 5 min (about 6km x 6km).
4.3 Management Data
EPIC requires detailed descriptions of management practices,
These descriptions must specify the timing of individual
operations either by date or by fraction of the growth period (ie,
by heat units). EPIC allows the user to simulate complex crop
rotations with a variety of irrigation, fertilizer, pesticide, and
tillage control options. There are two options for irrigation and
fertilizer scheduled application in the EPIC program: manually
and automatically. Only the manual option is applied in the
spatial EPIC model. Some parameters of manual mode are
described in details in Table ! (Huang et al. 2001). The land use
map at the scale of 1:1,000,000 is made by the Australian
Center of the Asian Spatial Information and Analysis Network,
Griffith University.
Table 1. The Operation Parameters of Winter Wheat — Summer
Corn Rotation for EPIC Model in North China
SUMMER CORN WINTER WHEAT
Date Operation Volume Date Operation Volume
Jun. 20 Irrigation 40mm Oct. 8 Irrigation 40mm
Jun.20 Fertilizer — 72kg/ha' Oct. 8 Fertilizer 72kg/ha
Jun. 23 Planting Oct. 8 Fertilizer ^ 5Skgha”
Aug.10 Fertilizer — 48kgha | Oct.10 Planting
Sep.28 Harvest Apr.10 Irrigation 100mm
Apr.20 Fertilizer ^ 48kg/ha
Mayl0 Irrigation 100mm
Jun.18 Harvest
152
Note: “*” means the application amount of 10076 nitrogen in chemical fertilizer.
** means the application amount of 10096 phosphorus in chemical fertilizer.
4.4 MODIS LAI Product
The MODIS LAI-FPAR algorithm is based on three-
dimensional radiative transfer theory and developed for
inversion using a look-up table (LUT) approach (Myneni et al.
2002). According to the algorithm, global vegetation is
classified into six canopy architectural types: grasses and cereal
crops, shrubs, broadleaf crops, savannas, broadleaf forests and
needle leaf forests. The structural characters among these
biomes, such as the horizontal (homogeneous vs
heterogeneous) and vertical (single- vs. multi- story)
dimensions, canopy height, leaf type, soil brightness and
climate (precipitation and temperature), are used to define
unique model configurations, including some fixed parameter
values appropriate for the biome characteristics. LUTS are then
generated for each biome by running the model for various
combinations of LAI and soil type. The algorithm ingests
atmospherically corrected bi-directional reflectance factors,
their uncertainties and corresponding sun-view geometries. It
compares the observed reflectances to comparable values
evaluated from model-based entries stored in LUTs and derives
the distribution of all possible solutions. When this method fails
to identify a solution, a back-up method based on relations
between the normalized difference vegetation index (NDVI)
and LAI and FPAR is used.
The current MODIS 1-km LAI-FPAR product is retrieved from
the reflectances of two bands (648 and 858 nm) and on an 8-day
compositing period. The product also includes extensive quality
control (QC) information regarding cloud and data processing
conditions. During each 8-day period, the highest-quality LAI
and FPAR are selected. These data are further composited over
4 (or 3) consecutive 8-day periods to produce monthly data
(Tian et al. 2004). This study uses the MODIS LAI product
from Sep. 2002 to Jul. 2003, which was downloaded from the
home page of Myneni's Climate and Vegetation research group
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