Scientia Agricultura Sinica ›› 2013, Vol. 46 ›› Issue (14): 2880-2893.doi: 10.3864/j.issn.0578-1752.2013.14.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Study on Maize Yield Estimation and Accuracy Assessment Based on PyWOFOST Crop Model in Northeast China

 CHEN  Si-Ning-123, ZHAO  Yan-Xia-2, SHEN  Shuang-He-3, LI  Zhen-Fa-1   

  1. 1.Tianjin Climate Center, Tianjin 300074
    2.Chinese Academy of Meteorological Sciences, Beijing 100081
    3.College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044
  • Received:2012-12-06 Online:2013-07-15 Published:2013-04-18

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Abstract: 【Objective】 The objective of this study is to construct a reasonable crop yield estimation scheme and improve the accuracy of crop yield estimation. 【Method】Based on the PyWOFOST crop model developed according to Ensemble Kalman Filter(EnKF) which combined remote sensing information with crop model, the PyWOFOST model was modified and improved to take LAI as the joint point of the crop model(WOFOST) and remote sensing information and be applicable to the maize-growing area in Northeast China. MODIS LAI data were used as external assimilation data to simulate maize LAI, yield and development stage with the PyWOFOST model at agro-meteorological stations. The impact of uncertainty(i.e. random error) of remote sensing observations(MODIS LAI) and model parameter(thermal time from emergence to anthesis, TSUM1) on the assimilation simulation results was analyzed deeply. Finally, the PyWOFOST model was used to estimate maize yield on a regional scale. 【Result】The result shows that, the modeled maize yield after assimilating MODIS LAI was closer to the observed values by comparing with the results modeled by WOFOST, the errors of maize production before and after assimilation at different uncertainty levels of TSUM1(0, 10, 20, 30℃) of 20 agro-meteorological stations without the impact of meteorological disasters were14.04%, 12.71%, 11.91%, 10.44% and 10.48%, respectively. LAI modeled by PyWOFOST after assimilation which more in line with the change trend of maize LAI was generally closer to the observed LAI than LAI modeled by WOFOST. The mean error of development stage between modeled value of WOFOST and observed value was 3.4 d; the mean errors of development stage between modeled value of PyWOFOST at different uncertainty levels of TSUM1（0, 10, 20, 30℃） and observed value were 3.5, 4.3, 5.0 and 5.5 d, respectively. The model results on the regional scale showed that, the errors of maize yield with assimilation were less than 15% in 58.82% of the study area; the coefficient of determination between modeled yield with assimilation and statistical yield was 0.806. 【Conclusion】Overall, the simulation results on the stations and regional scales both revealed the advantages of the crop yield estimation based on EnKF assimilated remote sensing information into crop models.

Key words: Ensemble Kalman Filter , pywofost crop model , remote sensing , yield estimation , accuracy assessment

[1]郭建茂. 基于遥感与作物生长模型的冬小麦生长模拟研究[D]. 南京: 南京信息工程大学, 2007: 10.

Guo J M. Study on winter wheat development simulation based remote sensing and crop growth model[D]. Nanjing: Nanjing University of Information Science and Technology, 2007: 10. (in Chinese)

[2]Maas S J. Use of remotely-sensed information in agricultural crop growth models. Ecological Modelling, 1988, 41(3-4): 274-268.

[3]Delecolle R, Guerif M. Introducing spectral data into a plant process model for improving its prediction ability//Proceedings of the 4th International Colloquium Signatures Spectrals d'Objets en Teledetection, Aussois, France, 1988: 125-127.

[4]Karvonen T, Laurila H, Meemola J. Estimation of Agricultural Crop Production Using Satellite Information. Husbandry: University of Helsinki, Department of Crop, 1991.

[5]Matsushita B, Tamura M. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment, 2002, 81(1): 58-66.

[6]Maas S J. Using satellite data to improve model estimates of crop yield. Agronomy Journal, 1988, 80: 655-662.

[7]Maas S J. Use of remotely-sensed information in plant growth simulation models. Advances in Agronomy, 1991, 1: 17-26.

[8]Maas S J. Parameterized model of gramineous crop growth: II. Within season simulation calibration. Agronomy Journal, 1993, 85: 354-358.

[9]Clevers J G P W. A simplified approach for yield prediction of sugar beet on optical remote sensing data. Remote Sensing of Environment, 1997, 61(2): 221-228.

[10]Herke B, Wouter V, Karl S. Coupling remote sensing observation models and a growth model for improved retrieval of (geo)biophysical information from optical remote sensing data. Remote Sensing for Agriculture, Ecosystems, and Hydrology, 2000, 4171: 1-11.

[11]Nouvellon Y, Moran S M, Seen D L. Coupling a grassland ecosystem model with landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sensing of Environment, 2001,78: 131-149.

[12]Fang H L, Liang S L, Hoogenboom G, Teasdale J, Cavigelli M. Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing, 2008, 29(10): 3011- 3032.

[13]Dente L, Satalino G, Mattia F, Rinaldi M. Assimilation of leaf area index derived from ASAR and MER IS data intoCERES- Wheat model to map wheat yield. Remote Sensing of Environment, 2008, 112(4) : 1395-1407.

[14]Launay M, Guerif M. Remote sensing data assimilation in a sugar beet growth model as a tool for spatial crop development variability forecast and diagnosis//8th International Symposium of Physical Measurements and Signatures in Remote Sensing, Aussois, France, 2001.

[15]Launay M, Guerif M. Assimilating remote sensing data into a cropmodel to improve predictive performance for spatial applications. Agriculture, Ecosystems and Environment, 2005, 111: 321-339.

[16]Fang H L, Liang S L, Gerrit H. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International Journal of Remote Sensing, 2011, 32(4): 1039-1065.

[17]de Wit A J W, van Diepen C A. Crop model data assimilation with the Ensemble Kalman Filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology, 2007, 146: 38-56.

[18]Curnel Y, de Wit A J M, Duveiller G. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment. Agricultural and Forest Meteorology, 2011, 151: 1843-1855.

[19]胡庆芳, 杨大文, 王银堂, 杨汉波. Angstrom公式参数对ET0的影响及FAO建议值适用性评价. 水科学进展, 2010, 21(5): 644-652.

Hu Q F, Yang D W, Wang Y T, Yang H B. Effects of Angstrom coefficients on ET0 estimation and the applicability of FAO recommended coefficient values in China. Advances in Water Science, 2010, 21(5): 644-652. (in Chinese)

[20]张建平, 赵艳霞, 王春乙, 杨晓光, 何勇. 气候变化情景下东北地区玉米产量变化模拟. 中国生态农业学报, 2008, 16(6): 1448-1452.

Zhang J P, Zhao Y X, Wang C Y, Yang X G, He Y. Simulation of maize production under climate change scenario in Northeast China. Chinese Journal of Eco-Agriculture, 2008, 16(6):1448-1452. (in Chinese)

[21]Zhang M W, Zhou Q B, Chen Z X, Liu J, Zhou Y, Cai C F. Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. International Journal of Applied Earth Observation and Geoinformation, 2008, 10: 476-485.

[22]Chen J, Jönsson P, Tamura M, Gu Z H, Matsushita B, Eklundh L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 2004, 91: 332-344.

[23]Chen Y, Oliver D S, Zhang D X. Data assimilation for nonlinear problems by Ensemble Kalman Filter with reparameterization. Journal of Petroleum Science and Engineering, 2009, 66:1-14.

[24]Evensen G. The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 2003, 53: 343-367.

[25]陈思宁, 赵艳霞, 申双和. 基于集合卡尔曼滤波的PyWOFOST模型在东北玉米估产中的适用性验证. 中国农业气象, 2012, 33(2): 245-253.

Chen S N, Zhao Y X, Shen S H. Applicability of PyWOFOST model based on ensemble kalman filter in simulating maize yield in Northeast China. Chinese Journal of Agrometeorology, 2012, 33(2): 245-253. (in Chinese)

[26]陈思宁, 申双和, 赵艳霞. 基于集合卡尔曼滤波的遥感信息和作物模型结合研究—以东北地区玉米估产为例[D]. 南京: 南京信息工程大学, 2012: 1-117.

Chen S N. Study on integration of remote sensing information and crop model based on ensemble kalman filter_A case study of maize yield estimation in Northeast China[D]. Nanjing: Nanjing University of Information Science and Technology, 2012: 1-117. (in Chinese)

[27]陈思宁, 赵艳霞, 申双和. 基于波谱分析技术的遥感作物分类方 法.农业工程学报, 2012, 28(5): 154-160.

Chen S N, Zhao Y X, Shen S H. Crop classification by remote sensing based on spectral analysis. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(5): 154-160. (in Chinese)

[28]王培娟, 梁宏, 李祎君, 张佳华. 气候变暖对东北三省玉米关键发育期及种植布局的影响. 资源科学, 2011, 33(10): 1976-1983.

Wang P J, Liang H, Li Y J, Zhang J H. Influences of climate warming on key growth stages and cultivated patterns of spring maize in Northeast China. Resources Science, 2011, 33(10): 1976-1983. (in Chinese)

[29]Fang H L, Wei S S, Liang S L. Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment, 2012, 119: 43-54.

[30]Fang H L, Wei S S, Jiang C Y, Klaus S. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sensing of Environment, 2012, 124: 610-621.

[31]王东伟. 遥感数据与作物生长模型同化方法及其应用研究[D]. 北京: 北京师范大学, 2008: 1-141.

Wang D W. Methodology and application of assimilating remote sensing data and crop growth model [D]. Beijing: Beijing Normal University, 2008: 1-141. (in Chinese)

[32]刘明光. 中国自然地理图集. 北京: 中国地图出版社, 1997: 113.

Liu G M. Natural Geographic Atlas of China. Beijing: China Cartographic Publishing House, 1997: 113. (in Chinese)
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