基于最佳植被指数组合的水稻鲜生物量估测
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摘要
目前已经开展了大量基于单一植被指数的水稻生物量遥感监测研究。但由于生物量随着水稻生长是一个动态变化的过程,单一植被指数只能反映某一时期水稻冠层和背景信息而不能精确地用于整个生育期生物量的监测。为此,采用40个植被指数,利用具有正交特性的逐步回归法,建立了一个彼此间包含较少冗余信息的植被指数组合,该组合中不同的植被指数适应于水稻不同生育期生物量的估算。运用具有非线性预测能力的支持向量机对植被指数组合估测效果进行验证,R~2达到0.81,RMSE达到0.51 kg/m~2。结果表明此植被指数组合相较于单一植被指数更能反应水稻生物量的动态变化过程,具有较好的生物量估算能力,为水稻全生期育生物量估算提供了一个有效工具。
A large number of crop biomass remote sensing monitoring studies have been carried out which based on an individual vegetation index(IVR). However, because of the rice show the different canopy structures and background differences in different growth stages, the IVR can only reflect the rice canopy structures and background information of a certain period and can not be used for monitoring the fresh biomass of rice during its whole growth period. Elect 40 vegetation index to establish a vegetation index combination with less redundant information by using the method of stepwise regression analysis with orthogonal, The optimal vegetation index combination was contained seven vegetation index variable and the different variables in this combination are adapted to biomass estimation at different growth stages of rice.Using the support vector machine to verify the effect of vegetation index combination. It is found that the prediction accuracy has been further improved. The sample R~2 reached 0.80, RMSE reached 0.51(kg/m~2). The results shows that the combined has better biomass estimation, which provides an effective means for the biomass estimation of rice during its whole growth period.
A large number of crop biomass remote sensing monitoring studies have been carried out which based on an individual vegetation index(IVR). However, because of the rice show the different canopy structures and background differences in different growth stages, the IVR can only reflect the rice canopy structures and background information of a certain period and can not be used for monitoring the fresh biomass of rice during its whole growth period. Elect 40 vegetation index to establish a vegetation index combination with less redundant information by using the method of stepwise regression analysis with orthogonal, The optimal vegetation index combination was contained seven vegetation index variable and the different variables in this combination are adapted to biomass estimation at different growth stages of rice.Using the support vector machine to verify the effect of vegetation index combination. It is found that the prediction accuracy has been further improved. The sample R~2 reached 0.80, RMSE reached 0.51(kg/m~2). The results shows that the combined has better biomass estimation, which provides an effective means for the biomass estimation of rice during its whole growth period.
引文
[1]王秀珍,黄敬峰,李云梅等,水稻地上鲜生物量的高光谱遥感估算模型研究[J]作物学报,2003,29(6):815-821.
[2]Chen Z,Li S,Ren J.et al.Monitoring and management of agriculture with remote sensing[M].Liang S(Ed).advances in Land Remote sensing:System,Modeling,Inversion and Application.2008.
[3]任建强,陈仲新,唐华俊等,基于遥感信息与作物生长模型的区域单产模拟[J].农业工程学报,2011,27(8):257-264.
[3]梁顺林,李小文,等.定量遥感:理念与算法[M].北京:科学出版社,2013
[4]Winter halter L、Mistletoe B,Schmidhalter U.Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies[J].Field Crops Research,2012,14(20):14-20.
[5]陈鹏飞,Nicolas T,王纪华等,估测作物冠层生物量的新植被指数的研究[J]光谱学与光谱分析,2010,30(2):521-517.
[6]And era A,peter B,Csaba L.et al.Estimating biomass of winter wheat using narrow band vegetation indices for precision agriculture[J]Journal of Central European Green Innovation.2015.3(1):118-128.
[7]Zandler H,Brenning A,Samimi C,et al.Potential of space-born hyper-spectral data for biomass quantification in and arid environment,advantages and limitation[J]Remote sensing.2015,7(4)4566-4580.
[8]SHIBAYAMA M,Akiyama T.Seasonal Visible Near Infrared Spectral of rice canon pies in relation to LAIand above ground dry biomass[J]Remote sensing of environment,1989,27(2):119-127.
[9]Broge N H,Leblanc e E.Remote Sensing of environment,1996,58(3);289-293.
[10]黄敬峰,王福民,王秀珍,水稻高光谱遥感实验研究[M]浙江大学出版社.2010.
[11]万余庆,谭克龙,周日平,高光谱遥感应用研究[M].北京:科学出版社,2006
[12]Gitelson A A.Journal of Plant Physiology,2004.161:165.
[13]杨有,李晓虹.多重共线性的逐步回归检验分析[J]重庆三峡学报,2006,22(3):39-41.
[14]Jayadeva,Khemchani R,Chandra S.Twin support vector machines for pattern classification[J]IEEETransactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[15]田国良,郭世忠.水稻的光谱反射特征[J];自然资源;1982,10(2):75-84
[16]赵英时.遥感应用分析原理与方法[M]科学出版社2003
[17]陈鹏飞,Nicolas Trembly,王纪华,等,估测作物冠层生物量的新植被指数的研究[J]光谱学与光谱分析2010:30(2):512-517.
[18]GAMON J A,PENUELAS J,FIELD C B.A narrowwaveband spectral index that tracks diurnal changes in photosynthetic efficiency[J].Remote Sensing of Environment,1992,41(1):35-44.
[19]唐延林,等,农作物LAI和生物量的高光谱法测定[J]西北农林科技大学2004-11-25
[20]宋开山,张柏,李芳,等,高光谱反射率与大豆叶面积及地上鲜生物量的相关分析[J]农业工程学报,2005,21(1):36-40.
[21]SMITHR C G,ADAMS J,STEPHENS D J,et al.Forecasting wheat yield in a mediterranean-type environment from the Satellite[J].Australian Journal of Agricultural research,1995,46.
[22]BUSCHMAN C,NAGEL E.In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation[J].International Journal of Remote Sensing,1993,14(4):711-722.
[23]BLACKBU RN G A.Quantifying chlorophyll's and carotenoids at leaf and canopy scales:an evaluation-of some hyper-spectral approaches[J].Remote Sensing of Environment,1998,66(3):273-285.
[24]唐延林,王秀珍,黄敬峰,等.农作物LAI和生物量的高光谱法测定[J]西北农林科技大学学报,2004,32(11)
[25]付元元,王纪华,杨贵军,等.应用波段深度分析和偏最小二乘回归的冬小麦生物量高光谱估算[J]光谱学与光谱分析,2013,33(5):1315-1319.
[26]SIMS D A,GAMON J A.relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2-3):337-354.
[27]Gong P,Pu R L,Healed R C.Analysis of in situ hyperspectral-data for nutrient estimation of giant sequoia[J].Journal of Remote Sensing,2002,181(61):1827-1850.
[28]黄春燕,刘胜利,高登伟,等.大豆叶面积指数的高光谱估算模型研究[J]大豆科学,2008,27(2)228-232
[29]STEDDOM K,HEIDEL G,JONES D,et al.Remote detection of rhizomania in sugar beets[J].Phytopathology,2003,93(6):720-726.
[30]GITELSON A A,KAUFMAN Y J,MERZLYAK M N.Use of a green channel i remote sensing of global vegetation from ES-MODIS[J].Remote Sensing of Environment,1996,58(3):289
[31]张威.施肥试验条件下的冬小麦产量和氮肥利用率地面高光谱遥感估算[D]南京农业大学
[32]DAUGHTRY C S T,WALTHALL C L,KIM M S,et al.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensing of Environment,2000,74(2):229-239.
[33]SIMS D A,GAMON J A.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2-3):337-354.
[34]刘斌,任建强,等.冬小麦鲜生物量估算敏感波段中心及波宽优选[J]农业工程学报,2016,32(16):125-134
[35]MA B L,MORISON M J,DWYER L M.Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of maize[J].Agronomy Journal,1996,88(6):915-920
[36]SE RRANO L,FILELLA I,PENUELAS J.Remote sensing of biomass and yield of winter wheat under different nitrogen supplies[J].Crop Science,2000,40(3):723-731.
[37]王登伟,黄春燕,马勤建,等.棉花高光谱植被指数与LAI和地上鲜生物量的相关分析[J]农业工程学报,2008,3,24(3):426-429
[38]Anatoly A,Gitelson Y K,et al.Novel algorithms for remote of vegetation fraction[J]Remote Sensing of Environment,2002,80(1):76-87
[39]唐延林,王纪华,黄敬峰,等.利用水稻成熟期冠层高光谱数据进行估产研究[J]作物学报,2004,30(8):739-744
[40]Rouse J w,Haas R H,Sch-ell J A,et al.Monitoring the vernal Advancements and Remote Sensing,2008,46(2):423
[2]Chen Z,Li S,Ren J.et al.Monitoring and management of agriculture with remote sensing[M].Liang S(Ed).advances in Land Remote sensing:System,Modeling,Inversion and Application.2008.
[3]任建强,陈仲新,唐华俊等,基于遥感信息与作物生长模型的区域单产模拟[J].农业工程学报,2011,27(8):257-264.
[3]梁顺林,李小文,等.定量遥感:理念与算法[M].北京:科学出版社,2013
[4]Winter halter L、Mistletoe B,Schmidhalter U.Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies[J].Field Crops Research,2012,14(20):14-20.
[5]陈鹏飞,Nicolas T,王纪华等,估测作物冠层生物量的新植被指数的研究[J]光谱学与光谱分析,2010,30(2):521-517.
[6]And era A,peter B,Csaba L.et al.Estimating biomass of winter wheat using narrow band vegetation indices for precision agriculture[J]Journal of Central European Green Innovation.2015.3(1):118-128.
[7]Zandler H,Brenning A,Samimi C,et al.Potential of space-born hyper-spectral data for biomass quantification in and arid environment,advantages and limitation[J]Remote sensing.2015,7(4)4566-4580.
[8]SHIBAYAMA M,Akiyama T.Seasonal Visible Near Infrared Spectral of rice canon pies in relation to LAIand above ground dry biomass[J]Remote sensing of environment,1989,27(2):119-127.
[9]Broge N H,Leblanc e E.Remote Sensing of environment,1996,58(3);289-293.
[10]黄敬峰,王福民,王秀珍,水稻高光谱遥感实验研究[M]浙江大学出版社.2010.
[11]万余庆,谭克龙,周日平,高光谱遥感应用研究[M].北京:科学出版社,2006
[12]Gitelson A A.Journal of Plant Physiology,2004.161:165.
[13]杨有,李晓虹.多重共线性的逐步回归检验分析[J]重庆三峡学报,2006,22(3):39-41.
[14]Jayadeva,Khemchani R,Chandra S.Twin support vector machines for pattern classification[J]IEEETransactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[15]田国良,郭世忠.水稻的光谱反射特征[J];自然资源;1982,10(2):75-84
[16]赵英时.遥感应用分析原理与方法[M]科学出版社2003
[17]陈鹏飞,Nicolas Trembly,王纪华,等,估测作物冠层生物量的新植被指数的研究[J]光谱学与光谱分析2010:30(2):512-517.
[18]GAMON J A,PENUELAS J,FIELD C B.A narrowwaveband spectral index that tracks diurnal changes in photosynthetic efficiency[J].Remote Sensing of Environment,1992,41(1):35-44.
[19]唐延林,等,农作物LAI和生物量的高光谱法测定[J]西北农林科技大学2004-11-25
[20]宋开山,张柏,李芳,等,高光谱反射率与大豆叶面积及地上鲜生物量的相关分析[J]农业工程学报,2005,21(1):36-40.
[21]SMITHR C G,ADAMS J,STEPHENS D J,et al.Forecasting wheat yield in a mediterranean-type environment from the Satellite[J].Australian Journal of Agricultural research,1995,46.
[22]BUSCHMAN C,NAGEL E.In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation[J].International Journal of Remote Sensing,1993,14(4):711-722.
[23]BLACKBU RN G A.Quantifying chlorophyll's and carotenoids at leaf and canopy scales:an evaluation-of some hyper-spectral approaches[J].Remote Sensing of Environment,1998,66(3):273-285.
[24]唐延林,王秀珍,黄敬峰,等.农作物LAI和生物量的高光谱法测定[J]西北农林科技大学学报,2004,32(11)
[25]付元元,王纪华,杨贵军,等.应用波段深度分析和偏最小二乘回归的冬小麦生物量高光谱估算[J]光谱学与光谱分析,2013,33(5):1315-1319.
[26]SIMS D A,GAMON J A.relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2-3):337-354.
[27]Gong P,Pu R L,Healed R C.Analysis of in situ hyperspectral-data for nutrient estimation of giant sequoia[J].Journal of Remote Sensing,2002,181(61):1827-1850.
[28]黄春燕,刘胜利,高登伟,等.大豆叶面积指数的高光谱估算模型研究[J]大豆科学,2008,27(2)228-232
[29]STEDDOM K,HEIDEL G,JONES D,et al.Remote detection of rhizomania in sugar beets[J].Phytopathology,2003,93(6):720-726.
[30]GITELSON A A,KAUFMAN Y J,MERZLYAK M N.Use of a green channel i remote sensing of global vegetation from ES-MODIS[J].Remote Sensing of Environment,1996,58(3):289
[31]张威.施肥试验条件下的冬小麦产量和氮肥利用率地面高光谱遥感估算[D]南京农业大学
[32]DAUGHTRY C S T,WALTHALL C L,KIM M S,et al.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensing of Environment,2000,74(2):229-239.
[33]SIMS D A,GAMON J A.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2-3):337-354.
[34]刘斌,任建强,等.冬小麦鲜生物量估算敏感波段中心及波宽优选[J]农业工程学报,2016,32(16):125-134
[35]MA B L,MORISON M J,DWYER L M.Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of maize[J].Agronomy Journal,1996,88(6):915-920
[36]SE RRANO L,FILELLA I,PENUELAS J.Remote sensing of biomass and yield of winter wheat under different nitrogen supplies[J].Crop Science,2000,40(3):723-731.
[37]王登伟,黄春燕,马勤建,等.棉花高光谱植被指数与LAI和地上鲜生物量的相关分析[J]农业工程学报,2008,3,24(3):426-429
[38]Anatoly A,Gitelson Y K,et al.Novel algorithms for remote of vegetation fraction[J]Remote Sensing of Environment,2002,80(1):76-87
[39]唐延林,王纪华,黄敬峰,等.利用水稻成熟期冠层高光谱数据进行估产研究[J]作物学报,2004,30(8):739-744
[40]Rouse J w,Haas R H,Sch-ell J A,et al.Monitoring the vernal Advancements and Remote Sensing,2008,46(2):423