Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt

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2011-7-29 2011-7-29 Jianqiang REN Jianqiang REN 1,2 1,2 , Zhongxin CHEN , Zhongxin CHEN 1,2 1,2 , Huajun TANG , Huajun TANG 1,2 1,2 , , Fushui YU Fushui YU 1,2 1,2 , Qing HUANG , Qing HUANG 1,2 1,2 不同长势冬小麦乳熟期冠层反射率 2008年5月15日 0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 300 400 500 600 700 800 900 1000 1100 波长(nm) 相对反射率 N0 N1 N2 N3 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 algorithm global 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 1/24

Transcript of Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt

Page 1: Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt

2011-7-292011-7-29

Jianqiang RENJianqiang REN1,21,2, Zhongxin CHEN, Zhongxin CHEN1,21,2, Huajun TANG, Huajun TANG1,21,2, , Fushui YUFushui YU1,21,2, Qing HUANG, Qing HUANG1,21,2

不同长势冬小麦乳熟期冠层反射率2008年5月15日

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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

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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)

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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

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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

Soil map data (1:4,000,000)

soil depth, soil texture, bulk density, soil pH, organic carbon concentration

and calcium carbonate content of soil, etc

Field management data

planting date, harvesting date, fertilizer application rate, irrigation volume

and plant population, etc

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4. Data preparation

4.2. Field observation

75 sampled plots

in the year of 2004 and 2008, more than 500m * 500m.

The number of sample sites was no less than 3 at each sample plot.

LAI measurement

manually at each growth stage. In each plot the average LAI of all

sampling sites was regarded as the final LAI value.

Yield

measured at ripening stage and the average yield of all sampling sites

was the final field-measured yield.

Field management information collection

planting date, plant density, net N fertilization application rate were

collected in each plot.

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4. Data preparation

4.3. LAI retrieved from MODIS NDVI

Basic data

250m 16 day MODIS NDVI data downloaded from NASA website (273rd day,2007 to 161st day, 2008)

field measured LAI in each growth stage

Method

Neural Network method.

Validation

Relative error of simulated LAI was less than 5%

RMSE (0.29~1)

Result of remotely sensed LAI

(2008, 113 rd day)

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4. Data preparation

4.4. Other auxiliary data

Crop map

provided by Key Laboratory of Resources Remote-Sensing &

Digital Agriculture, Ministry of Agriculture of China

Crop statistical yields at county level (2008)

provided by Agricultural Bureau of Hengshui City

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5. Results and analysis

5.1. Result of simulated sowing date of winter wheat (2007)

Regional average simulated sowing date was the 290th day (Oct. 17, 2007)

Average field-investigated sowing date was the 289th day (Oct. 16, 2007).

Absolute error was only 1 day.

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5. Results and analysis

5.2. Result of simulated plant density of winter wheat (2008)

Regional investigated plant density was 460.2 plants/m2

Average simulated plant density was 423.6 plants/m2

Average relative error of simulated plant density was -7.95%19/24

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5. Results and analysis

5.3. Result of simulated N fertilization application rate (2008)

Mean simulated amount of net N fertilization was 270.34 kg/ha

Mean custom amount of ground survey was 296.70 kg/ha

Relative error of simulated net N fertilization application rate was -8.88%. 20/24

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5. Results and analysis

5.4. Result of simulated winter wheat yield (2008)

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Statistic yield of winter wheat(t/ha)

Simulated yield of winter wheat(t/ha)

y = 1.0336x - 0.0903

R2 = 0.6436

RMSE = 0.208t/ha

Mean simulated yield was 5.94 t/ha

Relative error of simulated yield was 1.81%

RMSE of yield estimation was 0.208 t/ha 21/24

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6. Conclusions and future work

(1) Comparing with the statistical data or the investigated data, we got

better simulated results which could meet the need of accuracy of

agricultural remote sensing monitoring.

(2) It was possible and feasible to estimate crop yield and simulate

regional crop growth and field management parameters through

integrating remotely sensed LAI into crop growth model.

(3) These above work had setup good foundation for further use of this

method to predict crop yield at larger region in China.

6.1 Conclusions

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6. Conclusions and future work

(1)To expand application of the research method in a larger

region or the whole China

(2)To carry out research of grid cell size optimization in China

Considered the running efficiency and simulation accuracy, the optimal

grid cell size for provincial or national yield estimation should be studied

further.

(3) To carry out deeply research of other outer assimilation data

LAI was only considered as outer assimilation data, the NDVI, EVI or ET

etc would be used as outer assimilation data for further study.

6.2 Future work ( Discussion )

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谢 谢谢 谢!!Thanks for your attention!Thanks for your attention!

[email protected]

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