同化遥感信息与WheatSM模型的冬小麦估产
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摘要
小麦生长模型WheatSM是针对中国不同冬小麦品种类型研发的,已在科研和业务中得到应用。将遥感信息与作物生长模型耦合,进行区域大范围作物长势监测与估产具有重要的应用价值。以位于中国冬小麦主产区的河南省鹤壁市为研究区,分别利用SCE-UA优化同化法和EnKF同化法,将经优化重建的2013—2017年MODIS LAI时间序列数据和小麦生长模型WheatSM进行耦合,在站点和区域两种尺度开展冬小麦估产研究。结果表明:单站点运行时,在小麦模型参数严格标定的前提下,引入本身带有不确定性的遥感数据,并不能提高模型模拟结果的精度;遥感观测数据的质量对EnKF算法同化结果的影响大于对SCE-UA优化算法同化结果的影响;在区域尺度运行时,SCE-UA优化同化和EnKF同化模拟单产精度较同化前均有明显提高,分县模拟单产与统计单产相比,RMSE由2036.0kg·hm~(-2)分别降低到1641.6 kg·hm~(-2)和1587.7 kg·hm~(-2),分别降低19.4%和22.0%,且EnKF同化法的运行效率显著优于SCE-UA优化同化法。研究结果可为WheatSM小麦生长模型与遥感信息同化策略的选择提供依据。
WheatSM,a wheat growth model developed for different types of winter wheat in China,is applied in scientific research and public service. The coupling of remote sensing information with crop growth model has important application value in crop growth monitoring and yield estimation in large area. Hebi City in Henan Province is a main winter wheat producing area of China. With Hebi as the study area,the optimized reconstructed time series MODIS LAI data from 2013 to 2017 was coupled with WheatSM model with both SCE-UA optimal assimilation and EnKF assimilation methods to estimate the yield of winter wheat at both site and regional scales.The results showed that the introduction of remote sensing data with uncertainties did not improve the simulation accuracy of the crop model on the premise of strictly calibrating the parameters of WheatSM at site scale. The quality of remote sensing observation data had greater effects on the results of EnKF assimilation method than that of SCE-UA optimal assimilation method. At regional scale,the accuracy of data assimilation results with both SCE-UA and EnKF methods were higher than that without data assimilation. RMSE between simulated yield and statistical yield decreased from 2036.0 kg ·hm~(-2) to 1641 kg ·hm~(-2) with SCE-UA method and to 1587. 7 kg · hm~(-2) with EnKF method,with a reduction of 19. 4% and 22. 0%,respectively. The efficiency of EnKF assimilation method was higher than that of SCE-UA optimal assimilation method. Our results could provide a basis for the selection of data assimilation strategies coupling WheatSM with remote sensing data.
WheatSM,a wheat growth model developed for different types of winter wheat in China,is applied in scientific research and public service. The coupling of remote sensing information with crop growth model has important application value in crop growth monitoring and yield estimation in large area. Hebi City in Henan Province is a main winter wheat producing area of China. With Hebi as the study area,the optimized reconstructed time series MODIS LAI data from 2013 to 2017 was coupled with WheatSM model with both SCE-UA optimal assimilation and EnKF assimilation methods to estimate the yield of winter wheat at both site and regional scales.The results showed that the introduction of remote sensing data with uncertainties did not improve the simulation accuracy of the crop model on the premise of strictly calibrating the parameters of WheatSM at site scale. The quality of remote sensing observation data had greater effects on the results of EnKF assimilation method than that of SCE-UA optimal assimilation method. At regional scale,the accuracy of data assimilation results with both SCE-UA and EnKF methods were higher than that without data assimilation. RMSE between simulated yield and statistical yield decreased from 2036.0 kg ·hm~(-2) to 1641 kg ·hm~(-2) with SCE-UA method and to 1587. 7 kg · hm~(-2) with EnKF method,with a reduction of 19. 4% and 22. 0%,respectively. The efficiency of EnKF assimilation method was higher than that of SCE-UA optimal assimilation method. Our results could provide a basis for the selection of data assimilation strategies coupling WheatSM with remote sensing data.
引文
陈怀亮,李颖,田宏伟,等.2018.利用亚像元尺度信息改进区域冬小麦生长的模拟.生态学杂志,37(7):2221-2228.
陈劲松,黄健熙,林珲,等.2010.基于遥感信息和作物生长模型同化的水稻估产方法研究.中国科学:信息科学,40(S1):173-183.
陈思宁,赵艳霞,申双和,等.2013.基于PyWOFOST作物模型的东北玉米估产及精度评估.中国农业科学,46(14):2880-2893.
冯利平.1995.小麦生长发育模拟模型(WHEATSM)的研究(博士学位论文).南京:南京农业大学.
郭其乐,李颖,田宏伟.2017.小麦生长发育模型Wheat SM参数优化及适用性分析.麦类作物学报,37(12):1571-1580.
黄健熙,李昕璐,刘帝佑,等.2015a.顺序同化不同时空分辨率LAI的冬小麦估产对比研究.农业机械学报,46(1):240-248.
黄健熙,马鸿元,田丽燕,等.2015b.基于时间序列LAI和ET同化的冬小麦遥感估产方法比较.农业工程学报,31(4):197-203.
黄敬峰,王秀珍,王福民.2013.水稻卫星遥感不确定性研究.杭州:浙江大学出版社
解毅,王鹏新,刘峻明,等.2015.基于四维变分和集合卡尔曼滤波同化方法的冬小麦单产估测.农业工程学报,31(1):187-195.
解毅,王鹏新,张树誉,等.2017.基于粒子滤波和多变量权重的冬小麦估产研究.农业机械学报,48(10):148-155.
李宁.2015.小麦生长模拟模型(Wheat SM)V4.0版软件设计(硕士学位论文).北京:中国农业大学.
李颖,李耀辉,王金鑫.2016.夏玉米生长过程曲线重建研究-以鹤壁市为例.气象与环境科学,39(4):7-13.
申双和,杨沈斌,李炳柏.2009.基于ENVISAT ASAR数据的水稻估产方案.中国科学:地球科学,39(6):763-773.
孙宁,冯利平.2006.小麦生长发育模拟模型在华北冬麦区适用性验证.中国生态农业学报,14(1):71-72.
吴伶,刘湘南,周博天,等.2012.多源遥感与作物模型同化模拟作物生长参数时空域连续变化.遥感学报,16(6):1173-1191.
邢会敏,李振海,徐新刚,等.2017.基于遥感和Aqua Crop作物模型的多同化算法比较.农业工程学报,33(13):183-192.
De Wit A,Duveiller G,Defourny P.2012.Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations.Agricultural and Forest Meteorology,164:39-52.
Dente L,Satalino G,Mattia F,et al.2008.Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield.Remote Sensing of Environment,112:1395-1407.
Duan Q.1988.A global optimization strategy for efficient and effective calibration of hydrologic models.International Archives of Allergy&Applied Immunology,86:176-82.
Duveiller G,Defourny P.2010.A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing.Remote Sensing of Environment,114:2637-2650.
Fang H,Liang S,Hoogenboom G.2011.Integration of MODISLAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation.International Journal of Remote Sensing,32:1039-1065.
Iizumi T,Sakurai G,Yokozawa M.2014.Contributions of historical changes in sowing date and climate to US maize yield trend:An evaluation using large-area crop modeling and data assimilation.Journal of Agricultural Meteorology,70:73-90.
Jin X,Kumar L,Li Z,et al.2016.Estimation of winter wheat biomass and yield by combining the Aqua Crop model and field hyperspectral data.Remote Sensing,8:972.
Lipper L,Thornton P,Campbell BM,et al.2014.Climatesmart agriculture for food security.Nature Climate Change,4:1068-1072.
Thorp KR,Wang G,West AL,et al.2012.Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models.Remote Sensing of Environment,124:224-233.
Wu L,Feng L,Zhang Y,et al.2017.Comparison of five wheat models simulating phenology under different sowing dates and varieties.Agronomy Journal,109:1280-1293.
Xie Y,Wang P,Bai X,et al.2017.Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model.Agricultural and Forest Meteorology,246:194-206.
陈劲松,黄健熙,林珲,等.2010.基于遥感信息和作物生长模型同化的水稻估产方法研究.中国科学:信息科学,40(S1):173-183.
陈思宁,赵艳霞,申双和,等.2013.基于PyWOFOST作物模型的东北玉米估产及精度评估.中国农业科学,46(14):2880-2893.
冯利平.1995.小麦生长发育模拟模型(WHEATSM)的研究(博士学位论文).南京:南京农业大学.
郭其乐,李颖,田宏伟.2017.小麦生长发育模型Wheat SM参数优化及适用性分析.麦类作物学报,37(12):1571-1580.
黄健熙,李昕璐,刘帝佑,等.2015a.顺序同化不同时空分辨率LAI的冬小麦估产对比研究.农业机械学报,46(1):240-248.
黄健熙,马鸿元,田丽燕,等.2015b.基于时间序列LAI和ET同化的冬小麦遥感估产方法比较.农业工程学报,31(4):197-203.
黄敬峰,王秀珍,王福民.2013.水稻卫星遥感不确定性研究.杭州:浙江大学出版社
解毅,王鹏新,刘峻明,等.2015.基于四维变分和集合卡尔曼滤波同化方法的冬小麦单产估测.农业工程学报,31(1):187-195.
解毅,王鹏新,张树誉,等.2017.基于粒子滤波和多变量权重的冬小麦估产研究.农业机械学报,48(10):148-155.
李宁.2015.小麦生长模拟模型(Wheat SM)V4.0版软件设计(硕士学位论文).北京:中国农业大学.
李颖,李耀辉,王金鑫.2016.夏玉米生长过程曲线重建研究-以鹤壁市为例.气象与环境科学,39(4):7-13.
申双和,杨沈斌,李炳柏.2009.基于ENVISAT ASAR数据的水稻估产方案.中国科学:地球科学,39(6):763-773.
孙宁,冯利平.2006.小麦生长发育模拟模型在华北冬麦区适用性验证.中国生态农业学报,14(1):71-72.
吴伶,刘湘南,周博天,等.2012.多源遥感与作物模型同化模拟作物生长参数时空域连续变化.遥感学报,16(6):1173-1191.
邢会敏,李振海,徐新刚,等.2017.基于遥感和Aqua Crop作物模型的多同化算法比较.农业工程学报,33(13):183-192.
De Wit A,Duveiller G,Defourny P.2012.Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations.Agricultural and Forest Meteorology,164:39-52.
Dente L,Satalino G,Mattia F,et al.2008.Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield.Remote Sensing of Environment,112:1395-1407.
Duan Q.1988.A global optimization strategy for efficient and effective calibration of hydrologic models.International Archives of Allergy&Applied Immunology,86:176-82.
Duveiller G,Defourny P.2010.A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing.Remote Sensing of Environment,114:2637-2650.
Fang H,Liang S,Hoogenboom G.2011.Integration of MODISLAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation.International Journal of Remote Sensing,32:1039-1065.
Iizumi T,Sakurai G,Yokozawa M.2014.Contributions of historical changes in sowing date and climate to US maize yield trend:An evaluation using large-area crop modeling and data assimilation.Journal of Agricultural Meteorology,70:73-90.
Jin X,Kumar L,Li Z,et al.2016.Estimation of winter wheat biomass and yield by combining the Aqua Crop model and field hyperspectral data.Remote Sensing,8:972.
Lipper L,Thornton P,Campbell BM,et al.2014.Climatesmart agriculture for food security.Nature Climate Change,4:1068-1072.
Thorp KR,Wang G,West AL,et al.2012.Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models.Remote Sensing of Environment,124:224-233.
Wu L,Feng L,Zhang Y,et al.2017.Comparison of five wheat models simulating phenology under different sowing dates and varieties.Agronomy Journal,109:1280-1293.
Xie Y,Wang P,Bai X,et al.2017.Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model.Agricultural and Forest Meteorology,246:194-206.
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