基于PyWOFOST作物模型的东北玉米估产及精度评估

doi:10.3864/j.issn.0578-1752.2013.14.004

摘要/Abstract

摘要： 【目的】构建合理的作物估产方案，提高作物估产精度。【方法】本文以基于集合卡尔曼滤波（Ensemble Kalman Filter，EnKF）构建的遥感信息-作物模型结合模型（PyWOFOST）为基础，建立了以LAI为结合点，适用于中国东北地区玉米的同化模拟模型，并使用MODIS LAI数据作为外部同化数据进行同化模拟，重点分析了遥感观测（MODIS LAI）和模型参数（出苗-开花期所需积温，TSUM1）的不确定性（即随机误差）对同化模拟结果的影响。最后，利用PyWOFOST模型实现了区域尺度上的玉米估产。【结果】同化外部观测数据后的玉米模拟产量较未同化外部数据的模拟产量有明显改善，20个未受灾害影响的农气站玉米产量同化前的模拟误差及在TSUM1的不确定性为0、10、20、30℃时的同化后模拟误差分别为14.04%、12.71%、11.91%、10.44%及10.48%；同化后的模拟LAI普遍较同化前的模拟LAI更接近实测LAI，更符合玉米LAI的变化趋势；同化前模拟发育期与实测发育期平均绝对误差为3.4 d，而同化后在TSUM1的不确定性为0、10、20、30℃时模拟发育期与实测发育期的平均误差分别为3.5、4.3、5.0、5.5 d。区域尺度上玉米估产结果表明，58.82%的区域玉米估产误差在15%以内，同化产量和统计产量的确定系数为0.806。【结论】基于集合卡尔曼滤波同化遥感信息进行作物估产是可行的。

关键词: 集合卡尔曼滤波 , PyWOFOST作物模型 , 遥感 , 估产 , 精度评估

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

陈思宁123, 赵艳霞2, 申双和3, 黎贞发1. 基于PyWOFOST作物模型的东北玉米估产及精度评估[J]. 中国农业科学, 2013, 46(14): 2880-2893.

CHEN Si-Ning-123, ZHAO Yan-Xia-2, SHEN Shuang-He-3, LI Zhen-Fa-1. Study on Maize Yield Estimation and Accuracy Assessment Based on PyWOFOST Crop Model in Northeast China[J]. Scientia Agricultura Sinica, 2013, 46(14): 2880-2893.

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