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Simulation of regional winter wheat yield Simulation of regional winter wheat yield by combining by combining EPIC model and EPIC model and
remotely sensed LAI based on remotely sensed LAI based on global optimization algorithmglobal optimization algorithm
1 Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, China
2 Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences
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Introduction1
Study area2
Method33
Data preparation44
Results and analysis5
Conclusions and future work6
Outline
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1. Introduction
Crop yield information is critical to food security early warning
in a country or a region
Traditional crop yield forecasting methods
• agricultural statistical methods
• agricultural forecasting method
• climate model method
Main remote sensing models for crop yield estimation
• empirical model
• semi-empirical model
• crop growth mechanism model 3/24
1. Introduction
Combining RS data and crop growth model to simulate
crop growth and crop yield has been becoming important
research field
crop growth model: strong mechanism and time continuity
remote sensing: real-time features and spatial continuity
crop growth model + RS: strong mechanism + time/spatial
continuity
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1. Introduction
The way of combining RS data with crop growth model
forcing strategy (Easy)
time series variable of crop model (such as LAI) retrieved from remote
sensing data was input into model directly
initialization/parametrization strategy (Complex)
responding parameters and initial values were optimum
• when the difference between simulated crop parameter and
related remote sensing data reached the minimum value (relative
complex)
• or when the difference between simulated reflectance and remote
sensing reflectance (most complex)
1. Introduction
The choice of optimization algorithm is critical to the
accuracy of simulation results, general methods include:
simulate anneal arithmetic
genetic algorithms
neural networks, etc
SCE-UA (Shuffled Complex Evolution method - University of Arizona)
developed by Q.Y. Duan at University of Arizona (Duan, 1993)
could improve accuracy and efficiency of crop growth
monitoring and yield forecasting (Zhao, 2005; Qin, 2006)
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2. Study area
Study area E115.19 °– 116.53 °, N37.09 °– 38.36 °
includes 11 counties covering about 8815 km2
located in Hengshui City, Hebei Province, which is a
part of Huanghuaihai Plain in North China
Climate
temperate, semiarid, semi-humid and continental
monsoon climate.
Cropping system Winter wheat-summer maize (dominant double
cropping system )
Winter wheat : sowed (3rd 10-day of September----2nd 10-day of October)
mature (1st 10-day of June ----- 2nd 10-day of this month)
Ground survey plots: 75 in the year of 2004 and 2008
29 survey plots (in 2004) and 46 plots (in 2008) 6/24
3. Method
Flowchart of this research
Sensitivity analysis
Calibration of parameters
Elemental mapping unit (EMU)
Preparation of the average data
in each unit
When simulate
optimization object:
the simulated LAI
optimized parameters
planting date of crop, net N
fertilizer application rate and
planting density.
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3. Method
3.1 Crop growth model EPIC (Environmental Policy Integrated Climate)
developed to assess the effect of soil erosion on soil productivity
by USDA in 1984.
Suitable to most of all crop simulation and needs daily climate
data as driver parameters (solar radiation, max. temperature, mini.
temperature and precipitation……)
Basic formula in EPIC model
( ) ( )[ ]ttt LAIRAIPAR ⋅−−⋅⋅= 65.0exp15.0
( ) ( )AGYIELD HI B REG t BE IPAR t dt= ⋅ ⋅ ⋅ ⋅ ⋅∫ Where IPAR is intercepted photosynthetically active radiation; RA is solar radiation; BE is the crop parameter for converting energy to biomass; REG is the value of the minimum crop stress factor; BAG is the aboveground biomass in T/Ha
for crop; HI is the harvest index
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3.2. Global optimization algorithm SCE-UA (Duan, 1994)
3. Method
an efficient and global optimization algorithm not sensitive to parameter initialization value avoids optimization process relying on the prior knowledge the objective function as follows:
Where LAIsimi was simulated LAI; LAIobsi was remotely sensed
LAI; n was the number of EMU.
( )2
1
n
simi obsii
y LAI LAI=
= −∑
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3. Method
3.3. Model parameters calibration
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Parameters impacting the accuracy of simulated yield (Wu,2009)
WA (potential radiation use efficiency)
HI (normal harvest index)
DMLA (maximum potential leaf area index)
DLAI (point in the growing season when leaf area begins to decline due to leaf senescence)
DLP1 (crop parameter control leaf area growth of the crop under non-stressed condition)
DLP2 (crop parameter control leaf area growth of the crop under non-stressed condition)
RLAD (leaf-area-index decline rate parameter)
WA and HI: most key parameters which affected the model
localization and the accuracy of simulated yield (Wu,2009).
Other parameters: strongly influenced by crop varieties and difficult to
obtain in a large region.
3. Method
3.3. Model parameters calibration
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3. Method
3.4. Model assimilation parameters
The accuracy of derived leaf area index had an important impact on crop final estimated yield.
We selected the simulated LAI as the optimized object
The parameters such as DMLA, DLAI, DLP1, DLP2, RLAD, crop planting date, plant density and amount of nitrogen fertilization have significant effects on the change of simulated LAI value (Clevers, 1996)
we selected the above parameters as optimization parameters for leaf area index simulation.
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3. Method
3.5. Validation of results
Simulated crop yield
validated by the statistical crop yield data at county level;
Simulated crop management information
validated by the regional average information coming from each field survey plot because the custom field management was more stable in China.
statistical parameters
Root Mean Square Error (RMSE)
Coefficient of determination (R2)
Relative Error and Absolute Error
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4. Data preparation
4.1. Basic data collection and process
Station climate data
solar radiation, maximum temperature, minimum temperature,
precipitation, relative humidity and wind speed
interpolated at resolution of 250m using Kriging method
3rd 10-day of September, 2007 ---2nd 10-day of June, 2008