表征冬小麦倒伏强度敏感冠层结构参数筛选及光谱诊断模型
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摘要
针对倒伏胁迫下冬小麦冠层结构变化规律不清、冠层光谱响应机理不明的问题,以灌浆期倒伏冬小麦为研究对象,分析不同倒伏强度下冬小麦冠层结构参数变化规律,通过光谱探测视场内的茎、叶、穗面积比率与倒伏角度的相关性分析,筛选出表征倒伏强度的敏感冠层结构参数,采用传统光谱变换方法与连续小波变换方法对倒伏冬小麦冠层高光谱数据进行处理分析,筛选冠层结构参数的敏感波段和小波系数,采用偏最小二乘法构建冠层结构参数与高光谱特征参量的响应模型,并利用野外实测样本验证模型精度(建模集样本28个,验证集样本13个)。研究结果表明:倒伏后的冬小麦茎叶比与倒伏角度的相关性最高(-0.687,P<0.01),能较好地表征冬小麦倒伏强度,且茎叶比随着倒伏角度的减小而增加;基于连续小波变换的冬小麦倒伏灾情诊断模型优于常规光谱变换方法,检验样本的决定系数为0.632(P<0.01);以冠层茎叶比预测结果进行倒伏灾情等级划分的精度可达84.6%。因此,不同倒伏强度的冠层茎叶比与冬小麦冠层光谱之间的响应规律可以有效区分倒伏灾情等级,有助于为区域尺度的冬小麦倒伏灾情遥感监测提供先验知识。
At present, the changes of canopy structure and response mechanism of canopy spectral are not clear on winter wheat under lodging stress. Therefore, in this paper lodging winter wheat at the filling stage was taken as study object, the canopy structural parameters derived from the ratio of canopy stem, leaf and ear with different lodging strength were extracted. The correlation between the canopy structural parameters and lodging angle was analyzed, and the sensitive canopy structural parameters were selected to express lodging strength. The traditional spectral transform and the continuous wavelet transform were adopted to process the canopy hyperspectral data of lodging winter wheat. The bands and wavelet coefficients sensitive to canopy structural parameters were selected. The response model between canopy structural parameters of lodging winter wheat and hyperspectral characteristics parameters were constructed by partial least squares regression(PLSR) method, and the accuracy of the model was verified by field samples(28 samples for the modeling set, and 13 samples for the verification set). The results showed that the spectral curves of winter wheat with various lodging strengths had similar variation characteristics, and the wavelength bands of the troughs and peaks were roughly the same. Throughout the band interval, the spectral reflectance was expressed as: severe lodging > moderate lodging > mild lodging >not lodging. The first-order differential spectral reflectance of winter wheat increased with the increase of lodging degree, which indicated that the more severe the lodging, the more significant the change of the original reflectance data of the canopy spectrum. The correlation between stem-leaf ratio and lodging angle was the highest(R=-0.687, P<0.01), which could be used to characterize the lodging strength of winter wheat. The stem-leaf ratio increased with the decrease of lodging angle. The diagnostic model of lodging disaster of winter wheat based on continuous wavelet transform was superior to that based on the traditional transform, and the determination coefficient of the test samples was 0.632(P<0.01). The accuracy of lodging disaster classification based on the prediction results of canopy stem-leaf ratio could reach 84.6%. Therefore, the contribution proportion of stems, leaves and ears of the winter wheat canopy changed regularly in the sight of spectrometer after lodging. The stem-leaf ratio of winter wheat canopy could effectively characterize the changes of canopy structure under lodging stress, and had a good relationship with the lodging strength. The difference in the spectral reflectance of stem, leaf and ear and the variation in canopy structure after lodging were directly reflected in the canopy spectral difference of lodging wheat. The response rule between stem-leaf ratio with different lodging strength and canopy spectrum of winter wheat canopy can effectively distinguish the level of lodging disaster degree. It is helpful to provide a priori knowledge for remote sensing monitoring of winter wheat lodging disaster at regional scale.
At present, the changes of canopy structure and response mechanism of canopy spectral are not clear on winter wheat under lodging stress. Therefore, in this paper lodging winter wheat at the filling stage was taken as study object, the canopy structural parameters derived from the ratio of canopy stem, leaf and ear with different lodging strength were extracted. The correlation between the canopy structural parameters and lodging angle was analyzed, and the sensitive canopy structural parameters were selected to express lodging strength. The traditional spectral transform and the continuous wavelet transform were adopted to process the canopy hyperspectral data of lodging winter wheat. The bands and wavelet coefficients sensitive to canopy structural parameters were selected. The response model between canopy structural parameters of lodging winter wheat and hyperspectral characteristics parameters were constructed by partial least squares regression(PLSR) method, and the accuracy of the model was verified by field samples(28 samples for the modeling set, and 13 samples for the verification set). The results showed that the spectral curves of winter wheat with various lodging strengths had similar variation characteristics, and the wavelength bands of the troughs and peaks were roughly the same. Throughout the band interval, the spectral reflectance was expressed as: severe lodging > moderate lodging > mild lodging >not lodging. The first-order differential spectral reflectance of winter wheat increased with the increase of lodging degree, which indicated that the more severe the lodging, the more significant the change of the original reflectance data of the canopy spectrum. The correlation between stem-leaf ratio and lodging angle was the highest(R=-0.687, P<0.01), which could be used to characterize the lodging strength of winter wheat. The stem-leaf ratio increased with the decrease of lodging angle. The diagnostic model of lodging disaster of winter wheat based on continuous wavelet transform was superior to that based on the traditional transform, and the determination coefficient of the test samples was 0.632(P<0.01). The accuracy of lodging disaster classification based on the prediction results of canopy stem-leaf ratio could reach 84.6%. Therefore, the contribution proportion of stems, leaves and ears of the winter wheat canopy changed regularly in the sight of spectrometer after lodging. The stem-leaf ratio of winter wheat canopy could effectively characterize the changes of canopy structure under lodging stress, and had a good relationship with the lodging strength. The difference in the spectral reflectance of stem, leaf and ear and the variation in canopy structure after lodging were directly reflected in the canopy spectral difference of lodging wheat. The response rule between stem-leaf ratio with different lodging strength and canopy spectrum of winter wheat canopy can effectively distinguish the level of lodging disaster degree. It is helpful to provide a priori knowledge for remote sensing monitoring of winter wheat lodging disaster at regional scale.
引文
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[3]Shah F,Huang J,Cui K,et al.Impact of high-temperature stress on rice plant and its traits related to tolerance[J]Journal of Agricultural Science,2011,149(5):545-556.
[4]Wu W,Ma B L.Assessment of canola crop lodging under elevated temperatures for adaptation to climate change[J].Agricultural&Forest Meteorology,2018,248:329-338.
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[6]Foulkes M J,Slafer G A,Davies W J,et al.Raising yield potential of wheat.III.Optimizing partitioning to grain while maintaining lodging resistance[J].Journal of Experimental Botany,2011,62(2):469-486.
[7]Acreche M M,Slafer G A.Lodging yield penalties as affected by breeding in Mediterranean wheats[J].Field Crops Research,2011,122(1):40-48.
[8]Pi?era-Chavez F J,Berry P M,Foulkes M J,et al.Avoiding lodging in irrigated spring wheat.I.Stem and root structural requirements[J].Field Crops Research,2016,196:325-336.
[9]Wei W,Ma B.A new method for assessing plant lodging and the impact of management options on lodging in canola crop production[J].Scientific Reports,2016,6(1):31890.
[10]Peake A S,Huth N I,Carberry P S,et al.Quantifying potential yield and lodging-related yield gaps for irrigated spring wheat in sub-tropical Australia[J].Field Crops Research,2014,158(2):1-14.
[11]Kendall S L,Holmes H,White C A,et al.Quantifying lodging-induced yield losses in oilseed rape[J].Field Crops Research,2017,211:106-113.
[12]Liu T,Li R,Zhong X,et al.Estimates of rice lodging using indices derived from UAV visible and thermal infrared images[J].Agricultural&Forest Meteorology,2018,252:144-154.
[13]Berry P M,Spink J.Predicting yield losses caused by lodging in wheat[J].Field Crops Research,2012,137(3):19-26.
[14]Zhang J,Gu X,Wang J,et al.Evaluating maize grain quality by continuous wavelet analysis under normal and lodging circumstances[J].NJAS-Wageningen Journal of Life Sciences,2012,10(1/2):580-585.
[15]郭翠花,高志强,苗果园.不同产量水平下小麦倒伏与茎秆力学特性的关系[J].农业工程学报,2010,26(3):151-155.GuoCuihua,GaoZhiqiang,Miao Guoyuan.Relationship between lodging and mechanical characteristics of winterwheat stem under different yield levels[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(3):151-155.(in Chinese with English abstract)
[16]刘忠阳,陈怀亮,胡程达,等.后期倒伏对冬小麦干物质分配和产量的影响[J].中国农业气象,2017,38(5):321-329.Liu Zhongyang,Chen Huailiang,Hu Chengda,et al.Effects of lodging at the late growth stage on dry matter distribution and yield of winter wheat[J].Chinese Journal of Agrometeorology,2017,38(5):321-329.(in Chinese with English abstract)
[17]梁玉超,张永强,石书兵,等.施氮量对滴灌冬小麦茎部特征及其抗倒伏性的影响[J].麦类作物学报,2017,37(8):1078-1086.Liang Yuchao,Zhang Yongqiang,Shi Shubing,et al.Effect of nitrogen fertilizer rate on stem morphology characteristics and lodging resistance in winter wheat with drip irrigation[J].Journal of Triticeae Crops,2017,37(8):1078-1086.(in Chinese with English abstract)
[18]杨浩,杨贵军,顾晓鹤,等.小麦倒伏的雷达极化特征及其遥感监测[J].农业工程学报,2014,30(7):1-8.Yang Hao,Yang Guijun,GuXiaohe,et al.Radar polarimetric response features and remote sensing monitoringof wheat lodging[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(7):1-8.(in Chinese with English abstract)
[19]Yang H,Chen E,Li Z,et al.Wheat lodging monitoring using polarimetric index from RADARSAT-2 data[J].International Journal of Applied Earth Observation&Geoinformation,2015,34(1):157-166.
[20]韩东,杨浩,杨贵军,等.基于Sentinel-1雷达影像的玉米倒伏监测模型[J].农业工程学报,2018,34(3):166-172.Han Dong,Yang Hao,Yang Guijun,et al.Monitoring model of maize lodging based on Sentinel-1 radar image[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(3):166-172.(in Chinese with English abstract)
[21]王延仓,顾晓鹤,朱金山,等.利用反射光谱及模拟多光谱数据定量反演北方潮土有机质含量[J].光谱学与光谱分析,2014,34(1):201-206.Wang Yancang,Gu Xiaohe,Zhu Jinshan,et al.Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra[J].Spectroscopy and Spectral Analysis,2014,34(1):201-206.(in Chinese with English abstract)
[22]王延仓,杨贵军,朱金山,等.基于小波变换与偏最小二乘耦合模型估测北方潮土有机质含量[J].光谱学与光谱分析,2014,34(7):1922-1926.Wang Yancang,Yang Guijun,Zhu Jinshan,et al.Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least aquares[J].Spectroscopy and Spectral Analysis,2014,34(7):1922-1926.(in Chinese with English abstract)
[23]杨秀芳,张伟,杨宇祥.基于提升小波变换的雷达生命信号去噪技术[J].光学学报,2014,34(3):292-297.Yang Xiufang,Zhang Wei,Yang Yuxiang,et al.Denoisingtechnology of radar life signal based on lifting wavelet transform[J].ActaOpticaSinica,2014,34(3):292-297.(in Chinese with English abstract)
[24]杨秀芳,王若嘉,王佩佩,等.基于提升小波改进型阈值函数的雷达生命信号去噪技术[J].光子学报,2016,45(7):119-124.Yang Xiufang,Wang Ruojia,Wang Peipei,et al.De-noising technology of radar life signal based on lifting wavelet transform and improved soft threshold function[J].ActaPhotonicaSinica,2016,45(7):119-124.(in Chinese with English abstract)
[25]陈潇,邢立新,高志勇,等.基于小波变换的遥感影像纹理信息提取[J].安徽农业科学,2015(4):363-366.Chen Xiao,Xing Lixin,GaoZhiyong,et al.Information extraction of remote sensing image texture based on wavelet transform[J].Journal of Anhui Agricultural Sciences,2015(4)363-366.(in Chinese with English abstract)
[26]黄昕,张良培,李翠琳.基于小波变换的影像纹理特征提取试验[J].测绘地理信息,2005,30(6):7-9.Huang Xin,Zhang Liangpei,Li Cuilin.Experiments to extract texture features of images based on wavelet[J].Journal of Geomatics,2005,30(6):7-9.(in Chinese with English abstract)
[27]于雷,洪永胜,周勇,等.连续小波变换高光谱数据的土壤有机质含量反演模型构建[J].光谱学与光谱分析,2016,36(5):1428-1433.Yu Lei,Hong Yongsheng,Zhou Yong,et al.Inversion of soil orangic matter content using hyperspectral data based on continuous wavelet transformation[J].Spectroscopy and Spectral Analysis,2016,36(5):1428-1433.(in Chinese with English abstract)
[28]曹利萍,王君杰,雷梦林,等.高光谱对冬小麦倒伏的响应[J].山西农业科学,2017,45(12):1930-1932.Cao Liping,Wang Junjie,Lei Menglin,et al.Response of canopy spectra on the winter wheat lodging[J].Journal of Shanxi Agricultural Sciences,2017,45(12):1930-1932.(in Chinese with English abstract)
[29]Qu Y,Liu Z.Dimensionality reduction and derivative spectral feature optimization for hyperspectral target recognition[J].Optik-International Journal for Light and Electron Optics,2017,130:1349-1357.
[30]Bao J,Chi M,Benediktsson J A.Spectral derivative features for classification of hyperspectral remote sensing images:experimental evaluation[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2013,6(2):594-601.
[31]Wang X,Zhang J,Ren G,et al.Yellow River Estuary typical wetlands classification based on hyperspectral derivative transformation[J].Proceedings of SPIE-The International Society for Optical Engineering,2014,9142(1):91421O-91421O-12.
[32]Plaza A,Benediktsson J A,Boardman J W,et al.Recent advances in techniques for hyperspectral image processing[J].Remote Sensing of Environment,2009,113(1):S110-S122.
[2]Tao F,Zhao Z.Impacts of climate change as a function of global mean temperature:Maize productivity and water use in China[J].Climatic Change,2011,105(3/4):409-432.
[3]Shah F,Huang J,Cui K,et al.Impact of high-temperature stress on rice plant and its traits related to tolerance[J]Journal of Agricultural Science,2011,149(5):545-556.
[4]Wu W,Ma B L.Assessment of canola crop lodging under elevated temperatures for adaptation to climate change[J].Agricultural&Forest Meteorology,2018,248:329-338.
[5]Berry P M,Sterling M,Baker C J,et al.A calibrated model of wheat lodging compared with field measurements[J].Agricultural&Forest Meteorology,2003,119(3):167-180.
[6]Foulkes M J,Slafer G A,Davies W J,et al.Raising yield potential of wheat.III.Optimizing partitioning to grain while maintaining lodging resistance[J].Journal of Experimental Botany,2011,62(2):469-486.
[7]Acreche M M,Slafer G A.Lodging yield penalties as affected by breeding in Mediterranean wheats[J].Field Crops Research,2011,122(1):40-48.
[8]Pi?era-Chavez F J,Berry P M,Foulkes M J,et al.Avoiding lodging in irrigated spring wheat.I.Stem and root structural requirements[J].Field Crops Research,2016,196:325-336.
[9]Wei W,Ma B.A new method for assessing plant lodging and the impact of management options on lodging in canola crop production[J].Scientific Reports,2016,6(1):31890.
[10]Peake A S,Huth N I,Carberry P S,et al.Quantifying potential yield and lodging-related yield gaps for irrigated spring wheat in sub-tropical Australia[J].Field Crops Research,2014,158(2):1-14.
[11]Kendall S L,Holmes H,White C A,et al.Quantifying lodging-induced yield losses in oilseed rape[J].Field Crops Research,2017,211:106-113.
[12]Liu T,Li R,Zhong X,et al.Estimates of rice lodging using indices derived from UAV visible and thermal infrared images[J].Agricultural&Forest Meteorology,2018,252:144-154.
[13]Berry P M,Spink J.Predicting yield losses caused by lodging in wheat[J].Field Crops Research,2012,137(3):19-26.
[14]Zhang J,Gu X,Wang J,et al.Evaluating maize grain quality by continuous wavelet analysis under normal and lodging circumstances[J].NJAS-Wageningen Journal of Life Sciences,2012,10(1/2):580-585.
[15]郭翠花,高志强,苗果园.不同产量水平下小麦倒伏与茎秆力学特性的关系[J].农业工程学报,2010,26(3):151-155.GuoCuihua,GaoZhiqiang,Miao Guoyuan.Relationship between lodging and mechanical characteristics of winterwheat stem under different yield levels[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(3):151-155.(in Chinese with English abstract)
[16]刘忠阳,陈怀亮,胡程达,等.后期倒伏对冬小麦干物质分配和产量的影响[J].中国农业气象,2017,38(5):321-329.Liu Zhongyang,Chen Huailiang,Hu Chengda,et al.Effects of lodging at the late growth stage on dry matter distribution and yield of winter wheat[J].Chinese Journal of Agrometeorology,2017,38(5):321-329.(in Chinese with English abstract)
[17]梁玉超,张永强,石书兵,等.施氮量对滴灌冬小麦茎部特征及其抗倒伏性的影响[J].麦类作物学报,2017,37(8):1078-1086.Liang Yuchao,Zhang Yongqiang,Shi Shubing,et al.Effect of nitrogen fertilizer rate on stem morphology characteristics and lodging resistance in winter wheat with drip irrigation[J].Journal of Triticeae Crops,2017,37(8):1078-1086.(in Chinese with English abstract)
[18]杨浩,杨贵军,顾晓鹤,等.小麦倒伏的雷达极化特征及其遥感监测[J].农业工程学报,2014,30(7):1-8.Yang Hao,Yang Guijun,GuXiaohe,et al.Radar polarimetric response features and remote sensing monitoringof wheat lodging[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(7):1-8.(in Chinese with English abstract)
[19]Yang H,Chen E,Li Z,et al.Wheat lodging monitoring using polarimetric index from RADARSAT-2 data[J].International Journal of Applied Earth Observation&Geoinformation,2015,34(1):157-166.
[20]韩东,杨浩,杨贵军,等.基于Sentinel-1雷达影像的玉米倒伏监测模型[J].农业工程学报,2018,34(3):166-172.Han Dong,Yang Hao,Yang Guijun,et al.Monitoring model of maize lodging based on Sentinel-1 radar image[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(3):166-172.(in Chinese with English abstract)
[21]王延仓,顾晓鹤,朱金山,等.利用反射光谱及模拟多光谱数据定量反演北方潮土有机质含量[J].光谱学与光谱分析,2014,34(1):201-206.Wang Yancang,Gu Xiaohe,Zhu Jinshan,et al.Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra[J].Spectroscopy and Spectral Analysis,2014,34(1):201-206.(in Chinese with English abstract)
[22]王延仓,杨贵军,朱金山,等.基于小波变换与偏最小二乘耦合模型估测北方潮土有机质含量[J].光谱学与光谱分析,2014,34(7):1922-1926.Wang Yancang,Yang Guijun,Zhu Jinshan,et al.Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least aquares[J].Spectroscopy and Spectral Analysis,2014,34(7):1922-1926.(in Chinese with English abstract)
[23]杨秀芳,张伟,杨宇祥.基于提升小波变换的雷达生命信号去噪技术[J].光学学报,2014,34(3):292-297.Yang Xiufang,Zhang Wei,Yang Yuxiang,et al.Denoisingtechnology of radar life signal based on lifting wavelet transform[J].ActaOpticaSinica,2014,34(3):292-297.(in Chinese with English abstract)
[24]杨秀芳,王若嘉,王佩佩,等.基于提升小波改进型阈值函数的雷达生命信号去噪技术[J].光子学报,2016,45(7):119-124.Yang Xiufang,Wang Ruojia,Wang Peipei,et al.De-noising technology of radar life signal based on lifting wavelet transform and improved soft threshold function[J].ActaPhotonicaSinica,2016,45(7):119-124.(in Chinese with English abstract)
[25]陈潇,邢立新,高志勇,等.基于小波变换的遥感影像纹理信息提取[J].安徽农业科学,2015(4):363-366.Chen Xiao,Xing Lixin,GaoZhiyong,et al.Information extraction of remote sensing image texture based on wavelet transform[J].Journal of Anhui Agricultural Sciences,2015(4)363-366.(in Chinese with English abstract)
[26]黄昕,张良培,李翠琳.基于小波变换的影像纹理特征提取试验[J].测绘地理信息,2005,30(6):7-9.Huang Xin,Zhang Liangpei,Li Cuilin.Experiments to extract texture features of images based on wavelet[J].Journal of Geomatics,2005,30(6):7-9.(in Chinese with English abstract)
[27]于雷,洪永胜,周勇,等.连续小波变换高光谱数据的土壤有机质含量反演模型构建[J].光谱学与光谱分析,2016,36(5):1428-1433.Yu Lei,Hong Yongsheng,Zhou Yong,et al.Inversion of soil orangic matter content using hyperspectral data based on continuous wavelet transformation[J].Spectroscopy and Spectral Analysis,2016,36(5):1428-1433.(in Chinese with English abstract)
[28]曹利萍,王君杰,雷梦林,等.高光谱对冬小麦倒伏的响应[J].山西农业科学,2017,45(12):1930-1932.Cao Liping,Wang Junjie,Lei Menglin,et al.Response of canopy spectra on the winter wheat lodging[J].Journal of Shanxi Agricultural Sciences,2017,45(12):1930-1932.(in Chinese with English abstract)
[29]Qu Y,Liu Z.Dimensionality reduction and derivative spectral feature optimization for hyperspectral target recognition[J].Optik-International Journal for Light and Electron Optics,2017,130:1349-1357.
[30]Bao J,Chi M,Benediktsson J A.Spectral derivative features for classification of hyperspectral remote sensing images:experimental evaluation[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2013,6(2):594-601.
[31]Wang X,Zhang J,Ren G,et al.Yellow River Estuary typical wetlands classification based on hyperspectral derivative transformation[J].Proceedings of SPIE-The International Society for Optical Engineering,2014,9142(1):91421O-91421O-12.
[32]Plaza A,Benediktsson J A,Boardman J W,et al.Recent advances in techniques for hyperspectral image processing[J].Remote Sensing of Environment,2009,113(1):S110-S122.