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

2011-7-29 Jianqiang REN 1,2 , Zhongxin CHEN 1,2 , Huajun TANG 1,2 , Fushui YU 1,2 , Qing HUANG 1,2 Simulation of regional winter wheat yield by combining EPIC model and remotely sensed LAI based on 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

Outline 2/24 Introduction 1 Study area 2 Method 3 3 Data preparation 4 4 Results and analysis 5 Conclusions and future work 6

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 4/24

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) 5/24

2. Study area Study area E115.19 °– 116.53 °, N37.09 °– 38.36 ° includes 11 counties covering about 8815 km 2 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 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. 7/24 Flowchart of this research

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 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; B AG is the aboveground biomass in T/Ha for crop; HI is the harvest index 8/24

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 LAI simi was simulated LAI; LAI obsi was remotely sensed LAI; n was the number of EMU. 9/24

3. Method 3.3. Model parameters calibration 10/24

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 11/24

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. 12/24

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 (R 2 ) Relative Error and Absolute Error 13/24

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 3 rd 10-day of September, 2007 ---2 nd 10-day of June, 2008 S oil 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 14/24

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. 15/24

4. Data preparation 4.3. LAI retrieved from MODIS NDVI Basic data 250m 16 day MODIS NDVI data downloaded from NASA website (273 rd day,2007 to 161 st 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) 16/24

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 17/24

5. Results and analysis 5.1. Result of simulated sowing date of winter wheat (2007) Regional average simulated sowing date was the 290 th day (Oct. 17, 2007) Average field-investigated sowing date was the 289 th day (Oct. 16, 2007). Absolute error was only 1 day. 18/24

5. Results and analysis 5.2. Result of simulated plant density of winter wheat (2008) Regional investigated plant density was 460.2 plants/m 2 Average simulated plant density was 423.6 plants/m 2 Average relative error of simulated plant density was -7.95% 19/24

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

5. Results and analysis 5.4. Result of simulated winter wheat yield (2008) 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

6. Conclusions and future work 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. 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. 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 22/24

6. Conclusions and future work To expand application of the research method in a larger region or the whole China 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 ) 23/24

谢谢！ Thanks for your attention! [email_address] 24/24

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

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