基于遥感和积温的冬小麦生育期提取方法
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摘要
为了给监测作物长势和产量预测提供重要的基础数据,以河北、河南、山东三省冬小麦为研究对象,利用中等分辨率成像光谱仪(Moderate-resolution imaging spectroradiometer,MODIS)的叶面积指数(Leaf area index,LAI)产品,采用Savitzky-Golay上包络线滤波重构2015年MODIS LAI时间序列,提取抽穗期;基于Logistic函数拟合LAI时间序列提取返青期;根据提取的2015年返青期和抽穗期,基于多年历史积温法分别提取当年拔节期和开花期。利用研究区域内64个农业气象站点(简称农气站点)的生育期观测值对提取值进行验证,结果表明,采用农气站点观测值验证,提取的生育期精度较高,返青期、拔节期、抽穗期和开花期的平均误差分别为7. 4、4. 5、4. 4、3. 8 d。二阶导数的方法对混合像元及Logistic函数拟合准确度敏感,对拔节期、抽穗期、开花期的提取精度较高。研究表明,基于时间序列MODIS LAI数据,采用Logistic函数拟合提取大面积冬小麦生育期具有很好的可行性。
Phenological information is vital for dynamic monitoring of crop growth and precision field management. Accurate extraction of phenology benefits a rational analysis of crop's inter-annual changes in time and space and provides the fundamental data for monitoring of crop growth and crop yield forecasting. Winter wheat planting areas in Hebei,Henan,and Shandong Provinces were taken as study locations. Firstly,the LAI time series in 2015 was smoothed with Savitzky-Golay filtering algorithm,and the day of the maximum value from time series of LAI was taken as heading period. The Savitzky-Golay filtered MODIS LAI was fitted by using the double Logistic function. The day corresponding to the largest second derivative of Logistic LAI curve was considered as the green-up period. Jointing and flowering periods of winter wheat were extracted based on effective accumulated temperature from the existing green-up and heading stages from 2012 to 2014. The method was validated by the observations of phenology at agrometeorological stations in 2015 and the results showed that the phenology agreed well with the observational data of winter wheat. The second derivative method was quite sensitive to mixed pixel and accuracy of Logistic function'fitting of MODIS LAI. Generally,the extracted green-up stage was delayed,while the extraction of the jointing,heading and flowering stages achieved a high accuracy.
Phenological information is vital for dynamic monitoring of crop growth and precision field management. Accurate extraction of phenology benefits a rational analysis of crop's inter-annual changes in time and space and provides the fundamental data for monitoring of crop growth and crop yield forecasting. Winter wheat planting areas in Hebei,Henan,and Shandong Provinces were taken as study locations. Firstly,the LAI time series in 2015 was smoothed with Savitzky-Golay filtering algorithm,and the day of the maximum value from time series of LAI was taken as heading period. The Savitzky-Golay filtered MODIS LAI was fitted by using the double Logistic function. The day corresponding to the largest second derivative of Logistic LAI curve was considered as the green-up period. Jointing and flowering periods of winter wheat were extracted based on effective accumulated temperature from the existing green-up and heading stages from 2012 to 2014. The method was validated by the observations of phenology at agrometeorological stations in 2015 and the results showed that the phenology agreed well with the observational data of winter wheat. The second derivative method was quite sensitive to mixed pixel and accuracy of Logistic function'fitting of MODIS LAI. Generally,the extracted green-up stage was delayed,while the extraction of the jointing,heading and flowering stages achieved a high accuracy.
引文
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[3] LOBELL D B,BURKE M B,TEBALDI C,et al. Prioritizing climate change adaptation needs for food security in 2030[J].Science,2008,319(5863):607-610.
[4] BOLTON D K,FRIEDL M A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics[J].Agricultural&Forest Meteorology,2013,173:74-84.
[5]马鸿元,黄健熙,黄海,等.基于历史气象资料和WOFOST模型的区域产量集合预报[J/OL].农业机械学报,2018,49(9):257-266.MA Hongyuan,HUANG Jianxi,HUANG Hai,et al. Ensemble forecasting of regional yield of winter wheat based on WOFOSTmodel using historical metrological dataset[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(9):257-266. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20180930&flag=1. DOI:10. 6041/j.issn. 1000-1298. 2018. 09. 030.(in Chinese)
[6]黄健熙,黄海,马鸿元,等.基于MCMC方法的WOFOST模型参数标定与不确定性分析[J].农业工程学报,2018,34(16):113-119.HUANG Jianxi,HUANG Hai,MA Hongyuan,et al. Markov chain monte carlo based WOFOST model parameters calibrationand uncertainty analysis[J]. Transactions of the CSAE,2018,34(16):113-119.(in Chinese)
[7]黄健熙,马鸿元,田丽燕,等.基于时间序列LAI和ET同化的冬小麦遥感估产方法比较[J].农业工程学报,2015,31(4):197-203.HUANG Jianxi,MA Hongyuan,TIAN Liyan,et al. Comparison of remote sensing yield estimation methods for winter wheatbased on assimilating time-sequence LAI and ET[J]. Transactions of the CSAE,2015,31(4):197-203.(in Chinese)
[8]黄健熙,李昕璐,刘帝佑,等.顺序同化不同时空分辨率LAI的冬小麦估产对比研究[J/OL].农业机械学报,2015,46(1):240-248.HUANG Jianxi,LI Xinlu,LIU Diyou,et al. Comparison of winter wheat yield estimation by sequential assimilation of differentspatio temporal resolution remotely sensed LAI datasets[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(1):240-248. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20150134&flag=1. DOI:10. 6041/j. issn. 1000-1298. 2015. 01. 034.(in Chinese)
[9] PALOSUO T,KERSEBAUM K C,ANGULO C,et al. Simulation of winter wheat yield and its variability in different climates ofEurope:a comparison of eight crop growth models[J]. European Journal of Agronomy,2011,35(3):103-114.
[10]刘峻明,李曼曼,王鹏新,等.基于LAI时间序列重构数据的冬小麦物候监测[J].农业工程学报,2013,29(19):120-129.LIU Junming,LI Manman,WANG Pengxin,et al. Monitoring of phenology by reconstructing LAI time series data for winterwheat[J]. Transactions of the CSAE,2013,29(19):120-129.(in Chinese)
[11] CHU L,HUANG C,LIU Q,et al. Estimation of winter wheat phenology under the influence of cumulative temperature andsoil salinity in the Yellow River Delta,China,using MODIS time-series data[J]. International Journal of Remote Sensing,2016,37(10):2039-2060.
[12]司文才,刘峻明.冬小麦关键物候空间分布遥感监测方法研究[J].中国农业科技导报,2011,13(6):82-89.SI Wencai,LIU Junming. Studies on remote sensing monitoring method for spatial distribution of winter wheat critical phenology[J]. Journal of Agricultural Science and Technology,2011,13(6):82-89.(in Chinese)
[13] CHEN J,JNSSON P,TAMURA M,et al. A simple method for reconstructing a high-quality NDVI time-series data set basedon the Savitzky-Golayfilter[J]. Remote Sensing of Environment,2004,91(3):332-344.
[14]夏传福,李静,柳钦火.植被物候遥感监测研究进展[J].遥感学报,2013,17(1):1-16.XIA Chuanfu,LI Jing,LIU Qinhuo. Review of advances in vegetation phenology monitoring by remotesensing[J]. Journal ofRemote Sensing,2013,17(1):1-16.(in Chinese)
[15] SAKAMOTO T,YOKOZAWA M,TORITANI H,et al. A crop phenology detection method using time-series MODIS data[J].Remote Sensing of Environment,2005,96(3):366-374.
[16]李铮,柏延臣,何亚倩.遥感叶面积指数产品提取自然植被物候期对比[J].遥感技术与应用,2015,30(6):1103-1112.LI Zheng,BO Yanchen,HE Yaqian. Comparison ofnatural vegetation phenology metrics from remote sensing LAI products[J]. Remote Sensing Technology and Application,2015,30(6):1103-1112.(in Chinese)
[17] JONSSON P,EKLUNDH L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEETransactions on Geoscience&Remote Sensing,2002,40(8):1824-1832.
[18]侯学会,隋学艳,梁守真,等.几种物候提取方法的小麦物候提取[J].遥感信息,2017,32(6):65-70.HOU Xuehui,SUI Xueyan,LIANG Shouzhen,et al. Comparison of five methods for phenology extraction of winter wheat[J].Remote Sensing Information,2017,32(6):65-70.(in Chinese)
[19] HUANG J,TIAN L,LIANG S,et al. Improving winter wheat yield estimation by assimilation of the leaf area index fromLandsat TM and MODIS data into the WOFOST model[J]. Agricultural&Forest Meteorology,2015,204:106-121.
[20]鹿琳琳,郭华东.基于SPOT/VEGETATION时间序列的冬小麦物候提取方法[J].农业工程学报,2009,25(6):174-179.LU Linlin,GUO Huadong. Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[J].Transactions of the CSAE,2009,25(6):174-179.(in Chinese)
[21] WANG S,MO X,LIU Z,et al. Understanding long-term(1982—2013)patterns and trends in winter wheat spring green-update over the North China Plain[J]. International Journal of Applied Earth Observation&Geoinformation,2017,57:235-244.
[22] DUVEILLER G,BARET F,DEFOURNY P. Using thermal time and pixel purity for enhancing biophysical variable timeseries:an interproduct comparison[J]. IEEE Transactions on Geoscience&Remote Sensing,2013,51(4):2119-2127.
[23]黄健熙,牛文豪,马鸿元,等.卫星遥感和积温—辐射模型预测区域冬小麦成熟期[J].农业工程学报,2016,32(7):152-157.HUANG Jianxi,NIU Wenhao,MA Hongyuan,et al. Predicting maturity period for winter wheat using remote sensing andeffective accumulated temperature-solar radiation model[J]. Transactions of the CSAE,2016,32(7):152-157.(inChinese)
[24]胡乔玲,刘峻明,王春艳,等.冬小麦拔节期遥感监测方法研究[J].中国农业科技导报,2013,15(6):152-157.HU Qiaoling,LIU Junming,WANG Chunyan,et al. Studies on remote sensing monitoring method for jointing stage of winterwheat[J]. Journal of Agricultural Science and Technology,2013,15(6):152-157.(in Chinese)
[25] FENSHOLT R,SANDHOLT I,RASMUSSEN M S. Evaluation of MODIS LAI,f APAR and the relation between f APAR andNDVI in a semi-arid environment using in situ measurements[J]. Remote Sensing of Environment,2004,91(3-4):490-507.
[26] MA G,HUANG J,WU W,et al. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheatyield[J]. Mathematical&Computer Modelling,2013,58(3-4):634-643.
[27] FANG Hongliang,LIANG Shunlin,HOOGENBOOM G. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation[J]. International Journal of Remote Sensing,2011,32(4):1039-1065.
[28] CHEN Y,YANG K,HE J,et al. Improving land surface temperature modeling for dry land of China[J]. Journal ofGeophysical Research:Atmospheres,2011,116(D2O):D20104.
[29]何杰,阳坤.中国区域高时空分辨率地面气象要素驱动数据集[Z].中国科学院寒区旱区科学数据中心,2011,DOI:10. 3972/westdc. 3002. 2014. db.
[30] SAVITZKY A,GOLAY M J E. Smoothing and differentiation of data by simplified least squares procedures[J]. AnalyticalChemistry,1964,36(8):1627-1639.
[31] STEINIER J,TERMONIA Y,DELTOUR J,et al. Smoothing and differentiation of data by simplified least square procedure[J]. Analytical Chemistry,1972,44(11):1906-1909.
[32] ZHANG X,FRIEDL M A,SCHAAF C B,et al. Monitoring vegetation phenology using MODIS[J]. Remote Sensing ofEnvironment,2003,84(3):471-475.
[33]夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,29(3):139-147.XIA Tian,WU Wenbin,ZHOU Qingbo,et al. Comparison of two inversion methods for winter wheat leaf area index based onhyperspectral remote sensing[J]. Transactions of the CSAE,2013,29(3):139-147.(in Chinese)
[34]杨飞,孙九林,张柏,等.基于PROSAIL模型及TM与实测数据的MODIS LAI精度评价[J].农业工程学报,2010,26(4):192-197.YANG Fei,SUN Jiulin,ZHANG Bo,et al. Assessment of MODIS LAI product accuracy based on the PROSAIL model,TMand field measurements[J]. Transactions of the CSAE,2010,26(4):192-197.(in Chinese)
[2] FENSHOLT R,PROUD S R. Evaluation of earth observation based global long term vegetation trends—comparing GIMMS andMODIS global NDVI time series[J]. Remote Sensing of Environment,2012,119:131-147.
[3] LOBELL D B,BURKE M B,TEBALDI C,et al. Prioritizing climate change adaptation needs for food security in 2030[J].Science,2008,319(5863):607-610.
[4] BOLTON D K,FRIEDL M A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics[J].Agricultural&Forest Meteorology,2013,173:74-84.
[5]马鸿元,黄健熙,黄海,等.基于历史气象资料和WOFOST模型的区域产量集合预报[J/OL].农业机械学报,2018,49(9):257-266.MA Hongyuan,HUANG Jianxi,HUANG Hai,et al. Ensemble forecasting of regional yield of winter wheat based on WOFOSTmodel using historical metrological dataset[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(9):257-266. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20180930&flag=1. DOI:10. 6041/j.issn. 1000-1298. 2018. 09. 030.(in Chinese)
[6]黄健熙,黄海,马鸿元,等.基于MCMC方法的WOFOST模型参数标定与不确定性分析[J].农业工程学报,2018,34(16):113-119.HUANG Jianxi,HUANG Hai,MA Hongyuan,et al. Markov chain monte carlo based WOFOST model parameters calibrationand uncertainty analysis[J]. Transactions of the CSAE,2018,34(16):113-119.(in Chinese)
[7]黄健熙,马鸿元,田丽燕,等.基于时间序列LAI和ET同化的冬小麦遥感估产方法比较[J].农业工程学报,2015,31(4):197-203.HUANG Jianxi,MA Hongyuan,TIAN Liyan,et al. Comparison of remote sensing yield estimation methods for winter wheatbased on assimilating time-sequence LAI and ET[J]. Transactions of the CSAE,2015,31(4):197-203.(in Chinese)
[8]黄健熙,李昕璐,刘帝佑,等.顺序同化不同时空分辨率LAI的冬小麦估产对比研究[J/OL].农业机械学报,2015,46(1):240-248.HUANG Jianxi,LI Xinlu,LIU Diyou,et al. Comparison of winter wheat yield estimation by sequential assimilation of differentspatio temporal resolution remotely sensed LAI datasets[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(1):240-248. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20150134&flag=1. DOI:10. 6041/j. issn. 1000-1298. 2015. 01. 034.(in Chinese)
[9] PALOSUO T,KERSEBAUM K C,ANGULO C,et al. Simulation of winter wheat yield and its variability in different climates ofEurope:a comparison of eight crop growth models[J]. European Journal of Agronomy,2011,35(3):103-114.
[10]刘峻明,李曼曼,王鹏新,等.基于LAI时间序列重构数据的冬小麦物候监测[J].农业工程学报,2013,29(19):120-129.LIU Junming,LI Manman,WANG Pengxin,et al. Monitoring of phenology by reconstructing LAI time series data for winterwheat[J]. Transactions of the CSAE,2013,29(19):120-129.(in Chinese)
[11] CHU L,HUANG C,LIU Q,et al. Estimation of winter wheat phenology under the influence of cumulative temperature andsoil salinity in the Yellow River Delta,China,using MODIS time-series data[J]. International Journal of Remote Sensing,2016,37(10):2039-2060.
[12]司文才,刘峻明.冬小麦关键物候空间分布遥感监测方法研究[J].中国农业科技导报,2011,13(6):82-89.SI Wencai,LIU Junming. Studies on remote sensing monitoring method for spatial distribution of winter wheat critical phenology[J]. Journal of Agricultural Science and Technology,2011,13(6):82-89.(in Chinese)
[13] CHEN J,JNSSON P,TAMURA M,et al. A simple method for reconstructing a high-quality NDVI time-series data set basedon the Savitzky-Golayfilter[J]. Remote Sensing of Environment,2004,91(3):332-344.
[14]夏传福,李静,柳钦火.植被物候遥感监测研究进展[J].遥感学报,2013,17(1):1-16.XIA Chuanfu,LI Jing,LIU Qinhuo. Review of advances in vegetation phenology monitoring by remotesensing[J]. Journal ofRemote Sensing,2013,17(1):1-16.(in Chinese)
[15] SAKAMOTO T,YOKOZAWA M,TORITANI H,et al. A crop phenology detection method using time-series MODIS data[J].Remote Sensing of Environment,2005,96(3):366-374.
[16]李铮,柏延臣,何亚倩.遥感叶面积指数产品提取自然植被物候期对比[J].遥感技术与应用,2015,30(6):1103-1112.LI Zheng,BO Yanchen,HE Yaqian. Comparison ofnatural vegetation phenology metrics from remote sensing LAI products[J]. Remote Sensing Technology and Application,2015,30(6):1103-1112.(in Chinese)
[17] JONSSON P,EKLUNDH L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEETransactions on Geoscience&Remote Sensing,2002,40(8):1824-1832.
[18]侯学会,隋学艳,梁守真,等.几种物候提取方法的小麦物候提取[J].遥感信息,2017,32(6):65-70.HOU Xuehui,SUI Xueyan,LIANG Shouzhen,et al. Comparison of five methods for phenology extraction of winter wheat[J].Remote Sensing Information,2017,32(6):65-70.(in Chinese)
[19] HUANG J,TIAN L,LIANG S,et al. Improving winter wheat yield estimation by assimilation of the leaf area index fromLandsat TM and MODIS data into the WOFOST model[J]. Agricultural&Forest Meteorology,2015,204:106-121.
[20]鹿琳琳,郭华东.基于SPOT/VEGETATION时间序列的冬小麦物候提取方法[J].农业工程学报,2009,25(6):174-179.LU Linlin,GUO Huadong. Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[J].Transactions of the CSAE,2009,25(6):174-179.(in Chinese)
[21] WANG S,MO X,LIU Z,et al. Understanding long-term(1982—2013)patterns and trends in winter wheat spring green-update over the North China Plain[J]. International Journal of Applied Earth Observation&Geoinformation,2017,57:235-244.
[22] DUVEILLER G,BARET F,DEFOURNY P. Using thermal time and pixel purity for enhancing biophysical variable timeseries:an interproduct comparison[J]. IEEE Transactions on Geoscience&Remote Sensing,2013,51(4):2119-2127.
[23]黄健熙,牛文豪,马鸿元,等.卫星遥感和积温—辐射模型预测区域冬小麦成熟期[J].农业工程学报,2016,32(7):152-157.HUANG Jianxi,NIU Wenhao,MA Hongyuan,et al. Predicting maturity period for winter wheat using remote sensing andeffective accumulated temperature-solar radiation model[J]. Transactions of the CSAE,2016,32(7):152-157.(inChinese)
[24]胡乔玲,刘峻明,王春艳,等.冬小麦拔节期遥感监测方法研究[J].中国农业科技导报,2013,15(6):152-157.HU Qiaoling,LIU Junming,WANG Chunyan,et al. Studies on remote sensing monitoring method for jointing stage of winterwheat[J]. Journal of Agricultural Science and Technology,2013,15(6):152-157.(in Chinese)
[25] FENSHOLT R,SANDHOLT I,RASMUSSEN M S. Evaluation of MODIS LAI,f APAR and the relation between f APAR andNDVI in a semi-arid environment using in situ measurements[J]. Remote Sensing of Environment,2004,91(3-4):490-507.
[26] MA G,HUANG J,WU W,et al. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheatyield[J]. Mathematical&Computer Modelling,2013,58(3-4):634-643.
[27] FANG Hongliang,LIANG Shunlin,HOOGENBOOM G. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation[J]. International Journal of Remote Sensing,2011,32(4):1039-1065.
[28] CHEN Y,YANG K,HE J,et al. Improving land surface temperature modeling for dry land of China[J]. Journal ofGeophysical Research:Atmospheres,2011,116(D2O):D20104.
[29]何杰,阳坤.中国区域高时空分辨率地面气象要素驱动数据集[Z].中国科学院寒区旱区科学数据中心,2011,DOI:10. 3972/westdc. 3002. 2014. db.
[30] SAVITZKY A,GOLAY M J E. Smoothing and differentiation of data by simplified least squares procedures[J]. AnalyticalChemistry,1964,36(8):1627-1639.
[31] STEINIER J,TERMONIA Y,DELTOUR J,et al. Smoothing and differentiation of data by simplified least square procedure[J]. Analytical Chemistry,1972,44(11):1906-1909.
[32] ZHANG X,FRIEDL M A,SCHAAF C B,et al. Monitoring vegetation phenology using MODIS[J]. Remote Sensing ofEnvironment,2003,84(3):471-475.
[33]夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,29(3):139-147.XIA Tian,WU Wenbin,ZHOU Qingbo,et al. Comparison of two inversion methods for winter wheat leaf area index based onhyperspectral remote sensing[J]. Transactions of the CSAE,2013,29(3):139-147.(in Chinese)
[34]杨飞,孙九林,张柏,等.基于PROSAIL模型及TM与实测数据的MODIS LAI精度评价[J].农业工程学报,2010,26(4):192-197.YANG Fei,SUN Jiulin,ZHANG Bo,et al. Assessment of MODIS LAI product accuracy based on the PROSAIL model,TMand field measurements[J]. Transactions of the CSAE,2010,26(4):192-197.(in Chinese)