水稻叶片高光谱数据降维与叶绿素含量反演方法研究
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摘要
高光谱遥感技术为水稻叶片叶绿素含量的高通量、无损、准确监测提供了有效途径,然而高光谱数据的降维或特征光谱参数的选择是叶绿素含量有效反演的关键环节。利用2017年辽宁省盘锦市大洼水稻氮高效品种筛选试验基地的水稻叶片叶绿素含量与叶片高光谱数据,探讨了高光谱数据的降维方法与叶绿素含量的反演建模。首先应用最优子集选择算法(best subset selection)对工程常用的水稻叶绿素反演特征光谱指数进行优选,筛选出最优组合,作为叶绿素多元回归模型的输入特征;同时应用没有在光谱领域得到有效应用的基函数展开算法,利用Gram-Schmidt正交变换寻找叶片高光谱数据的基函数空间,再将高光谱数据投影到基函数空间从而实现降维,最后利用降维后的数据进行多元回归建模,反演叶绿素。结果表明:最优子集选择算法优选出的mNDVI(445,705,750)、NDVI(705,750)、PSRI(500,680,750)、RD(505,705)、RI1dB(720,735)、MCARI(550,670,700)、PPR(450,550)共7个特征指数组合,回归模型反演精度最高,决定性系数R2为0.844,均方根误差RMSE为0.926;基于基函数展开算法对400～1000nm波段范围601维高光谱数据降至13维,叶绿素反演回归模型的决定性系数R2达到0.861,均方根误差RMSE为0.906。说明基于基函数展开的高光谱降维与叶绿素含量估测方法效果较好,可为水稻叶绿素含量估测与长势诊断提供技术支持。
Hyperspectral remote sensing technology provides an effective way for high-throughput, non-destructive and accurate monitoring of chlorophyll content in rice leaves. However, the reduction of hyperspectral data or the selection of characteristic spectral parameters is the key link for effective retrieval of chlorophyll content. In this study, the rice leaf chlorophyll content and leaf hyperspectral data from Dawa County, Panjin, Liaoning Province in 2017 were used to explore the dimensionality reduction methods and chlorophyll content inversion modeling of hyperspectral data. First, the best subset selection algorithm was used to optimize the spectral index of rice chlorophyll inversion, which was commonly used in engineering. The optimal combination was selected as the input feature of the chlorophyll multivariate regression model. Meanwhile, the Gram-Schmidt orthogonal transformation was applied. To find the basis function space of the hyperspectral data of the blade, the hyperspectral data were projected to the basis function space to achieve dimensionality reduction. Finally, the reduced-dimensional data were used to perform multiple regression modeling to invert the chlorophyll. The results showed that seven characteristic index combinations of mNDVI(445, 705, 750), NDVI(705, 750), PSRI(500, 680, 750), RD(505, 705), and RI1dB(720, 735), MCARI(550, 670, 700), PPR(450, 550)fitted to the regression model with the highest inversion accuracy, the decisive coefficient R2 was 0.844, the root mean square error RMSE was 0.926. Based on the basis function Expanding the algorithm, the 601-dimensional hyperspectral data in the 400-1000 nm band range was reduced to 13-dimensional, and the deterministic coefficient R2 of the chlorophyll inversion regression model reached 0.861, and the root mean square error RMSE was 0.906. It showed that the hyperspectral dimension reduction and chlorophyll content estimation method based on the basis function expansion was effective. So it can provide technical support for rice chlorophyll content estimation and growth diagnosis.
Hyperspectral remote sensing technology provides an effective way for high-throughput, non-destructive and accurate monitoring of chlorophyll content in rice leaves. However, the reduction of hyperspectral data or the selection of characteristic spectral parameters is the key link for effective retrieval of chlorophyll content. In this study, the rice leaf chlorophyll content and leaf hyperspectral data from Dawa County, Panjin, Liaoning Province in 2017 were used to explore the dimensionality reduction methods and chlorophyll content inversion modeling of hyperspectral data. First, the best subset selection algorithm was used to optimize the spectral index of rice chlorophyll inversion, which was commonly used in engineering. The optimal combination was selected as the input feature of the chlorophyll multivariate regression model. Meanwhile, the Gram-Schmidt orthogonal transformation was applied. To find the basis function space of the hyperspectral data of the blade, the hyperspectral data were projected to the basis function space to achieve dimensionality reduction. Finally, the reduced-dimensional data were used to perform multiple regression modeling to invert the chlorophyll. The results showed that seven characteristic index combinations of mNDVI(445, 705, 750), NDVI(705, 750), PSRI(500, 680, 750), RD(505, 705), and RI1dB(720, 735), MCARI(550, 670, 700), PPR(450, 550)fitted to the regression model with the highest inversion accuracy, the decisive coefficient R2 was 0.844, the root mean square error RMSE was 0.926. Based on the basis function Expanding the algorithm, the 601-dimensional hyperspectral data in the 400-1000 nm band range was reduced to 13-dimensional, and the deterministic coefficient R2 of the chlorophyll inversion regression model reached 0.861, and the root mean square error RMSE was 0.906. It showed that the hyperspectral dimension reduction and chlorophyll content estimation method based on the basis function expansion was effective. So it can provide technical support for rice chlorophyll content estimation and growth diagnosis.
引文
[1] COLLINS W.Remote sensing of crop type and maturity[J].Photogrammetric Engineering and Remote Sensing,1978,44:43-55.
[2]章曼,常庆瑞,张晓华,等.不同施肥条件下水稻冠层光谱特征与叶绿素含量的相关性[J].西北农业学报,2015,24(11):49-56.
[3]徐新刚,赵春江,王纪华,等.新型光谱曲线特征参数与水稻叶绿素含量间的关系研究[J].光谱学与光谱分析,2011.31(1):188-191.
[4]姜楠,陈温福.遮光对不同穗型粳稻叶片光合特性和叶绿体超微结构的影响[J].沈阳农业大学学报,2015,46(1):1-6.
[5]李志宏,刘宏斌,张云贵.叶绿素仪在氮肥推荐中的应用研究进展[J].植物营养与肥料学报,2006,12(1):125-130.
[6]刘桃菊,胡雯君,张笑东,等.水稻冠层高光谱特征变量与叶片叶绿素含量的相关性研究[J].激光生物学报,2015,24(5):428-435.
[7]滕利荣,孟庆繁.生物学基础实验教程[M].3版.北京:科学出版社,2008.
[8]冯伟,郭天财,谢迎新,等.作物光谱分析技术及其在生长监测中的应用[J].中国农学通报,2009,25(23):182-188.
[9]王艳,王孝纯.寒地水稻氮磷钾营养诊断技术的研究[J].中国农学通报,2009,25(1):208-211.
[10]岳学军,全东平,洪添胜,等.柑橘叶片叶绿素含量高光谱无损检测模型[J].农业工程学报,2015,31(1):294-302.
[11]王树文,赵越,王丽凤,等.基于高光谱的寒地水稻叶片氮素含量预测[J].农业工程学报,2016,32(20):187-194.
[12]付元元,杨贵军.基于高光谱维数约简与植被指数估算冬小麦叶面积指数的比较[J].农业工程学报,2012,28(23):107-113.
[13]宋开山,张柏,王宗明,等.小波分析在大豆叶绿素含量高光谱反演中的应用[J].中国农学通报,2006,22(9):101-108.
[14] DARVISHZADEH R,SKIDMORE A,SCHLERF M,et al.LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements[J].ISPRS Journal of Photogrammetry and Remote Sensing,2008,63:409-426.
[15]姚付启,张振华,杨润亚,等.基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究[J].测绘科学,2010,35(1):109-112.
[16]王惠文,夏棒,孟洁.快速Gram-Schmidt回归方法[J].北京航空航天大学学报,2013,39(9):1259-1262.
[17]范德芹,朱文泉,潘耀忠,等.基于狄克松检验法的NDVI时序数据噪声检测及其在数据重建中的应用[J].遥感学报,2013,17(5):1166-1174.
[18]闫闯.多元回归模型中变量选择问题研究[D].哈尔滨:黑龙江大学,2011.
[19]高林,杨贵军,李长春,等.基于光谱特征与PLSR结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用[J].作物学报,2017,43(4):549-557.
[20] LEMAIRE G,JEUFFROY M H,GASTAL F.Diagnosis tool for plant and crop N status in vegetative stage:Theory and practices for crop N management[J].European Journal of Agronomy,2008,28(4):614-624.
[21] TERUO MATSUNAKA,YUJI WATANABE,TADASHI MIYAWAKI, et al.Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter[J].Soil Science and Plant Nutrition,1997,43(1):127-134.
[2]章曼,常庆瑞,张晓华,等.不同施肥条件下水稻冠层光谱特征与叶绿素含量的相关性[J].西北农业学报,2015,24(11):49-56.
[3]徐新刚,赵春江,王纪华,等.新型光谱曲线特征参数与水稻叶绿素含量间的关系研究[J].光谱学与光谱分析,2011.31(1):188-191.
[4]姜楠,陈温福.遮光对不同穗型粳稻叶片光合特性和叶绿体超微结构的影响[J].沈阳农业大学学报,2015,46(1):1-6.
[5]李志宏,刘宏斌,张云贵.叶绿素仪在氮肥推荐中的应用研究进展[J].植物营养与肥料学报,2006,12(1):125-130.
[6]刘桃菊,胡雯君,张笑东,等.水稻冠层高光谱特征变量与叶片叶绿素含量的相关性研究[J].激光生物学报,2015,24(5):428-435.
[7]滕利荣,孟庆繁.生物学基础实验教程[M].3版.北京:科学出版社,2008.
[8]冯伟,郭天财,谢迎新,等.作物光谱分析技术及其在生长监测中的应用[J].中国农学通报,2009,25(23):182-188.
[9]王艳,王孝纯.寒地水稻氮磷钾营养诊断技术的研究[J].中国农学通报,2009,25(1):208-211.
[10]岳学军,全东平,洪添胜,等.柑橘叶片叶绿素含量高光谱无损检测模型[J].农业工程学报,2015,31(1):294-302.
[11]王树文,赵越,王丽凤,等.基于高光谱的寒地水稻叶片氮素含量预测[J].农业工程学报,2016,32(20):187-194.
[12]付元元,杨贵军.基于高光谱维数约简与植被指数估算冬小麦叶面积指数的比较[J].农业工程学报,2012,28(23):107-113.
[13]宋开山,张柏,王宗明,等.小波分析在大豆叶绿素含量高光谱反演中的应用[J].中国农学通报,2006,22(9):101-108.
[14] DARVISHZADEH R,SKIDMORE A,SCHLERF M,et al.LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements[J].ISPRS Journal of Photogrammetry and Remote Sensing,2008,63:409-426.
[15]姚付启,张振华,杨润亚,等.基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究[J].测绘科学,2010,35(1):109-112.
[16]王惠文,夏棒,孟洁.快速Gram-Schmidt回归方法[J].北京航空航天大学学报,2013,39(9):1259-1262.
[17]范德芹,朱文泉,潘耀忠,等.基于狄克松检验法的NDVI时序数据噪声检测及其在数据重建中的应用[J].遥感学报,2013,17(5):1166-1174.
[18]闫闯.多元回归模型中变量选择问题研究[D].哈尔滨:黑龙江大学,2011.
[19]高林,杨贵军,李长春,等.基于光谱特征与PLSR结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用[J].作物学报,2017,43(4):549-557.
[20] LEMAIRE G,JEUFFROY M H,GASTAL F.Diagnosis tool for plant and crop N status in vegetative stage:Theory and practices for crop N management[J].European Journal of Agronomy,2008,28(4):614-624.
[21] TERUO MATSUNAKA,YUJI WATANABE,TADASHI MIYAWAKI, et al.Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter[J].Soil Science and Plant Nutrition,1997,43(1):127-134.