不同遥感水平水稻氮素信息提取研究
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摘要
随着环境问题日益受到重视,如何在保证作物高产优质的同时防止或尽量减少作物生产带来的环境污染也是各国政府、农学家、环境工作者及生产者所必须解决的问题。因此,采取有效的氮素管理措施,合理施用氮肥,准确、迅速、经济地判断植物的氮素状况、确定作物的氮肥需要量以及提高氮肥的利用效率具有重大的经济和生态意义。
遥感技术为获得不同尺度生化组分含量提供了一个便捷的多元化工具。定量化提取植被生化组分信息的研究随着高光谱技术的发展而飞快的发展起来,同时现代计算机技术提供了强大的计算和数据处理能力,极大程度地丰富了遥感提取生化组分信息的数据处理方法。数据挖掘技术因此应运而生,成为定量化提取植被生化组分信息研究中的热点问题。
本研究围绕遥感信息数据挖掘技术这一前沿课题,以不同遥感水平数据定量提取作物氮素信息为研究重点,在研究ANN和SVM数据挖掘技术理论以及PCA技术的基础上,从统计回归方法到ANN和SVM算法,从方法分析到模型建立,进行了一个较为系统的研究,构建了基于数据挖掘技术的不同遥感水平作物氮素信息提取模型,并系统地对比了传统统计方法与ANN算法以及SVM方法用于遥感提取作物氮素信息的精度,以及不同遥感水平作物氮素信息提取的精度。
研究的主要内容与成果如下:
(1)叶片水平氮含量遥感诊断模型研究
研究中,采用线性建模法(Linear Regression,简称LR)、反向传播神经网络法(Back Propagation Neural Network,简称ANN)、径向基函数网络法(RadialBasis Function,简称RBF)以及支持向量机法(Support Vector Machine,简称SVM)构建了基于原始光谱反射率(Reflectance,简称R)和主成份得分值(Scoresof Principal Components,简称PC)的水稻不同发育期以及不同氮素水平的氮含量遥感诊断模型,并采用非建模数据集的大田水稻数据以及非水稻的油菜数据对模型的普适性进行了验证。结果表明:对比不同发育期氮含量诊断模型发现,通常灌浆期和乳熟期模型的各类精度指标表现较好,其中,基于灌浆期光谱主成份得分值构建的PC-RBF模型的精度较高,模型的RMSE值和REP值分别为0.151和6.816%,由其得到的估算氮和实测氮之间的相关系数r=0.977,两者之间极显著相关;对比不同氮素水平氮含量遥感诊断模型发现,基于N1适氮水平的氮含量诊断模型精度通常要优于N0和N2水平模型的精度,其中N1水平的R-LR模型的精度最优,其RMSE和REP值分别为0.720和25.647%,由其得到的估算氮和实测氮之间的相关系数r=0.747,两者极显著相关;采用大田水稻数据对各类模型的普适性进行验证的结果表明,将基于水稻小区试验数据构建的各类氮含量诊断模型应用于大田水稻数据不但可行并且总体结果令人满意;采用油菜数据对各类模型的普适性进行验证,结果发现各类模型虽然也能实现油菜氮含量的估算,但总体精度不如在大田水稻数据中的应用精度,此外还发现,由于参与模型验证的油菜氮含量的取值范围(1.07至2.84 mg/g)要远远小于建模水稻氮含量的取值范围(0.91至4.82 mg/g),从而导致各模型对油菜氮含量的拟合结果普遍高于油菜实测氮含量。
(2)冠层水平氮含量遥感诊断模型研究
采用与叶片水平氮含量遥感诊断研究相同的方法对冠层水平氮含量诊断模型进行讨论。结果表明:对比不同发育期氮含量诊断模型发现,乳熟期和成熟期模型各类精度指标表现较好,其中成熟期的R-ANN模型表现相对最优,模型RMSE和REP值分别为0.746,48.147%,估算氮值和实测值之间显著相关,相关系数r=0.912;对比不同氮素水平氮含量诊断模型发现,三个氮素水平的各类模型都能较好的实现氮含量诊断,精度令人满意,由各类模型得到的估算氮和实测氮之间的最大相关系数r=0.962,最小相关系数r=-0.799,估算氮与实测氮之间极显著相关;采用大田水稻数据对各类模型的普适性进行验证的结果与叶片水平模型的验证结果类似,由R-LR模型和R-SVM模型得到的大田水稻估算氮值与实测值之间的相关关系极显著,r分别等于0.865和0.854;采用油菜数据对各类模型的普适性进行验证,与叶片水平结果类似,估算总体精度不如对大田水稻氮含量的估算精度,并且同样会过高估算油菜氮含量。
(3)基于TM数据的水稻氮含量遥感诊断模型研究
研究中,以TM数据和相应的氮含量数据为数据源,采用LR线性建模法、RBF建模法以及SVM建模法,在相关性分析的基础上,构建了基于TM2、TM3波段光谱变量以及NDⅥ和RⅥ植被指数的氮含量遥感诊断模型并对模型精度进行检验;此外,还将叶片和冠层水平光谱变量模拟TM波段光谱范围,采用相同的建模方法,对模型的精度采用TM数据进行验证,从而探讨基于叶片和冠层水平光谱变量的氮含量诊断模型扩展应用在TM数据中的精度。结果表明,采用TM数据对水稻氮含量进行估算不但可行并且总体精度令人满意;对比不同建模方法,发现LR线性模型总体表现不如RBF模型和SVM模型,进一步对比发现,除了RⅥ-RBF模型的精度高于RⅥ-SVM模型之外,由其它三个输入变量构建的RBF模型的精度均低于对应的SVM模型,总体上SVM模型的表现最佳;由四个输入变量构建的SVM模型均能很好的实现氮含量估算,其中TM2-SVM模型的表现最佳,由其得到的估算氮和实测氮之间的相关系数r=0.751,两者之间极显著相关。
With more attentions to environmental perturbations, how to balance the factors of high yield and great quality of crop, and the need to provide or minimize environmental perturbations caused by crop production has become a problem needing conscientious consideration and settlement of the governments, the agriculturist and the environmental workers and researchers in the world. So appropriate N fertilizer management, efficient monitoring of plant N status and proper supply of N to crop is significant to economy and ecology.
Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties and has been recognized as a reliable and convenient method for the estimation of various variables related to physiology and biochemistry form remote sensing data at different levels. In recent years, quantitatively remote sensing of the vegetation biochemicals has been greatly improved by the development of hyperspectral technology and the use of multivariate statistical methods, particularly the data mining technique, which has become the hot topics in the studies of quantitatively remote sensing of vegetation biochemicals.
This paper discussed around the new important problem of data mining technique, and focused on the quantitatively remote sensing of rice nitrogen concentration from different remote sensing levels. Based on the research of ANN and SVM technique and principal components analysis (PCA), the thesis conducted a systemic study on data mining technique of remote sensing information, from the statistical regression method to ANN and SVM technique, and from method analysis to modeling. Various estimation models were developed using remote data at different remote sensing levels and the precision tests of models were performed. Finally, systemic comparisons of the predict capability of LR methods, ANN methods and SVM technique, as well as the performances of models at different remote sensing levels were made.
The main contents and conclusions are following:
(1) Quantitative Retrieval of nitrogen concentration using leaf spectral variables
In the paper, four methods were adopted for modeling, i.e. linear regression (LR models), back propagation neural network (ANN models), radial basis function network (RBF models), and support vector machine technology (SVM models), and two variables were used as input variables for various models, i.e. raw spectral reflectance (R) and the scores of principal components (PC). The rice nitrogen concentration estimation models for different growth stages and different nitrogen fertilizer levels were established and the precision tests of models were done. At the meantime, the validations of models universality were carried out using field rice dataset and rape dataset. The following conclusions were obtained:
Comparisons of nitrogen estimation models at different growth stages showed that the models at grain filling stage and milky ripe stage were prior to models at other growth stages. The PC-RBF model at grain filling stage was the best, the RMSE and REP of the models were 0.151 and 6.816% respectively, and the correlation coefficient between the theoretical and the measured nitrogen concentration was 0.977, which achieved significant level;
Comparisons of nitrogen estimation models at different nitrogen fertilizer levels indicated that the models at N1 level were normally better than models at NO and N2 levels. Among various models, the R-LR model performance was the best, which with the RMSE=0.720, REP=25.647 and the correlation coefficient r=0.747;
The validation of models universality using field rice dataset showed that the application of various models which based on experimental datasets in the field rice dataset was not only feasible and but also satisfying. Meantime, the validation of models universality using rape dataset indicated that, although it was feasible to apply the models in the rape dataset, the whole performances of various models in the rape dataset was not as good as they were in the field rice dataset. Furthermore, due to the narrow value range of rape nitrogen concentration (maximum is 2.84 mg/g, minimum is 1.07 mg/g), which is significantly lower than rice nitrogen concentration (maximum is 4.82 mg/g, minimum is 0.91 mg/g), the various models were normally overestimated the rape nitrogen concentrations.
(2) Quantitative Retrieval of nitrogen concentration using canopy spectral variables
In this section, the same methods to above section were adopted for the discussions of nitrogen estimation models at canopy level. Conclusions are followings:
Comparisons of nitrogen estimation models at different growth stages showed that the models at milky ripe stage and ripe stage were prior to models at other growth stages. The R-ANN model at ripe was the best, the RMSE and REP of the models were 0.746 and 48.147% respectively, and the correlation coefficient between the theoretical and the measured nitrogen concentration was 0.912, which achieved significant level;
Comparisons of nitrogen estimation models at different nitrogen fertilizer levels indicated that the various models at three nitrogen fertilizer levels all could achieve satisfying results, which is especially true for models at N1 level. The maximum correlation coefficient between the theoretical and the measured nitrogen was 0.962, the minimum was 0.799.
The similar results to leaf level model validation were obtained when using field rice dataset for validation of model universality at canopy level. The theoretical nitrogen concentrations retrieved from R-LR model and R-SVM model were significantly correlated with the measured nitrogen concentration, which with r=0.865 and r=0.854, respectively.
Besides, the results using rape dataset for the validation of model universality at canopy level were also similar to those at leaf level, i.e. the whole performances of various models in the rape dataset were not as good as they were in the field rice dataset, and the nitrogen concentrations were generally overestimated.
(3) Quantitative Retrieval of nitrogen concentration using TM data
In this section, the TM data and corresponding nitrogen concentration were used as data source, and the LR modeling method, the RBF method and SVM technology were adopted for modeling. On the basis of correlation analysis between nitrogen concentration and spectral variables, the models based on TM2, TM3, NDVI and RVI were established and the precision tests of models were done. Besides, in order to explore whether the models at leaf and canopy levels could be applied in TM data, the models based on simulated TM variables using leaf and canopy spectral reflectance were constructed and verified using TM data. The results indicated that retrieval of nitrogen information using TM data was not only feasible and satisfying. Comparison of various models' precisions showed that the LR method performed worst among the three kinds of modeling methods as a whole, and the further comparison indicated except for RVI-RBF model performed better than RVI-SVM model, the SVM models based on other three variables were generally performed better than RBF models. Among all models, the TM2-SVM model could achieve the best results, the theoretical nitrogen concentration derived from it were significantly correlated with the measured nitrogen concentration, with the correlation coefficient r=0.751.
遥感技术为获得不同尺度生化组分含量提供了一个便捷的多元化工具。定量化提取植被生化组分信息的研究随着高光谱技术的发展而飞快的发展起来,同时现代计算机技术提供了强大的计算和数据处理能力,极大程度地丰富了遥感提取生化组分信息的数据处理方法。数据挖掘技术因此应运而生,成为定量化提取植被生化组分信息研究中的热点问题。
本研究围绕遥感信息数据挖掘技术这一前沿课题,以不同遥感水平数据定量提取作物氮素信息为研究重点,在研究ANN和SVM数据挖掘技术理论以及PCA技术的基础上,从统计回归方法到ANN和SVM算法,从方法分析到模型建立,进行了一个较为系统的研究,构建了基于数据挖掘技术的不同遥感水平作物氮素信息提取模型,并系统地对比了传统统计方法与ANN算法以及SVM方法用于遥感提取作物氮素信息的精度,以及不同遥感水平作物氮素信息提取的精度。
研究的主要内容与成果如下:
(1)叶片水平氮含量遥感诊断模型研究
研究中,采用线性建模法(Linear Regression,简称LR)、反向传播神经网络法(Back Propagation Neural Network,简称ANN)、径向基函数网络法(RadialBasis Function,简称RBF)以及支持向量机法(Support Vector Machine,简称SVM)构建了基于原始光谱反射率(Reflectance,简称R)和主成份得分值(Scoresof Principal Components,简称PC)的水稻不同发育期以及不同氮素水平的氮含量遥感诊断模型,并采用非建模数据集的大田水稻数据以及非水稻的油菜数据对模型的普适性进行了验证。结果表明:对比不同发育期氮含量诊断模型发现,通常灌浆期和乳熟期模型的各类精度指标表现较好,其中,基于灌浆期光谱主成份得分值构建的PC-RBF模型的精度较高,模型的RMSE值和REP值分别为0.151和6.816%,由其得到的估算氮和实测氮之间的相关系数r=0.977,两者之间极显著相关;对比不同氮素水平氮含量遥感诊断模型发现,基于N1适氮水平的氮含量诊断模型精度通常要优于N0和N2水平模型的精度,其中N1水平的R-LR模型的精度最优,其RMSE和REP值分别为0.720和25.647%,由其得到的估算氮和实测氮之间的相关系数r=0.747,两者极显著相关;采用大田水稻数据对各类模型的普适性进行验证的结果表明,将基于水稻小区试验数据构建的各类氮含量诊断模型应用于大田水稻数据不但可行并且总体结果令人满意;采用油菜数据对各类模型的普适性进行验证,结果发现各类模型虽然也能实现油菜氮含量的估算,但总体精度不如在大田水稻数据中的应用精度,此外还发现,由于参与模型验证的油菜氮含量的取值范围(1.07至2.84 mg/g)要远远小于建模水稻氮含量的取值范围(0.91至4.82 mg/g),从而导致各模型对油菜氮含量的拟合结果普遍高于油菜实测氮含量。
(2)冠层水平氮含量遥感诊断模型研究
采用与叶片水平氮含量遥感诊断研究相同的方法对冠层水平氮含量诊断模型进行讨论。结果表明:对比不同发育期氮含量诊断模型发现,乳熟期和成熟期模型各类精度指标表现较好,其中成熟期的R-ANN模型表现相对最优,模型RMSE和REP值分别为0.746,48.147%,估算氮值和实测值之间显著相关,相关系数r=0.912;对比不同氮素水平氮含量诊断模型发现,三个氮素水平的各类模型都能较好的实现氮含量诊断,精度令人满意,由各类模型得到的估算氮和实测氮之间的最大相关系数r=0.962,最小相关系数r=-0.799,估算氮与实测氮之间极显著相关;采用大田水稻数据对各类模型的普适性进行验证的结果与叶片水平模型的验证结果类似,由R-LR模型和R-SVM模型得到的大田水稻估算氮值与实测值之间的相关关系极显著,r分别等于0.865和0.854;采用油菜数据对各类模型的普适性进行验证,与叶片水平结果类似,估算总体精度不如对大田水稻氮含量的估算精度,并且同样会过高估算油菜氮含量。
(3)基于TM数据的水稻氮含量遥感诊断模型研究
研究中,以TM数据和相应的氮含量数据为数据源,采用LR线性建模法、RBF建模法以及SVM建模法,在相关性分析的基础上,构建了基于TM2、TM3波段光谱变量以及NDⅥ和RⅥ植被指数的氮含量遥感诊断模型并对模型精度进行检验;此外,还将叶片和冠层水平光谱变量模拟TM波段光谱范围,采用相同的建模方法,对模型的精度采用TM数据进行验证,从而探讨基于叶片和冠层水平光谱变量的氮含量诊断模型扩展应用在TM数据中的精度。结果表明,采用TM数据对水稻氮含量进行估算不但可行并且总体精度令人满意;对比不同建模方法,发现LR线性模型总体表现不如RBF模型和SVM模型,进一步对比发现,除了RⅥ-RBF模型的精度高于RⅥ-SVM模型之外,由其它三个输入变量构建的RBF模型的精度均低于对应的SVM模型,总体上SVM模型的表现最佳;由四个输入变量构建的SVM模型均能很好的实现氮含量估算,其中TM2-SVM模型的表现最佳,由其得到的估算氮和实测氮之间的相关系数r=0.751,两者之间极显著相关。
With more attentions to environmental perturbations, how to balance the factors of high yield and great quality of crop, and the need to provide or minimize environmental perturbations caused by crop production has become a problem needing conscientious consideration and settlement of the governments, the agriculturist and the environmental workers and researchers in the world. So appropriate N fertilizer management, efficient monitoring of plant N status and proper supply of N to crop is significant to economy and ecology.
Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties and has been recognized as a reliable and convenient method for the estimation of various variables related to physiology and biochemistry form remote sensing data at different levels. In recent years, quantitatively remote sensing of the vegetation biochemicals has been greatly improved by the development of hyperspectral technology and the use of multivariate statistical methods, particularly the data mining technique, which has become the hot topics in the studies of quantitatively remote sensing of vegetation biochemicals.
This paper discussed around the new important problem of data mining technique, and focused on the quantitatively remote sensing of rice nitrogen concentration from different remote sensing levels. Based on the research of ANN and SVM technique and principal components analysis (PCA), the thesis conducted a systemic study on data mining technique of remote sensing information, from the statistical regression method to ANN and SVM technique, and from method analysis to modeling. Various estimation models were developed using remote data at different remote sensing levels and the precision tests of models were performed. Finally, systemic comparisons of the predict capability of LR methods, ANN methods and SVM technique, as well as the performances of models at different remote sensing levels were made.
The main contents and conclusions are following:
(1) Quantitative Retrieval of nitrogen concentration using leaf spectral variables
In the paper, four methods were adopted for modeling, i.e. linear regression (LR models), back propagation neural network (ANN models), radial basis function network (RBF models), and support vector machine technology (SVM models), and two variables were used as input variables for various models, i.e. raw spectral reflectance (R) and the scores of principal components (PC). The rice nitrogen concentration estimation models for different growth stages and different nitrogen fertilizer levels were established and the precision tests of models were done. At the meantime, the validations of models universality were carried out using field rice dataset and rape dataset. The following conclusions were obtained:
Comparisons of nitrogen estimation models at different growth stages showed that the models at grain filling stage and milky ripe stage were prior to models at other growth stages. The PC-RBF model at grain filling stage was the best, the RMSE and REP of the models were 0.151 and 6.816% respectively, and the correlation coefficient between the theoretical and the measured nitrogen concentration was 0.977, which achieved significant level;
Comparisons of nitrogen estimation models at different nitrogen fertilizer levels indicated that the models at N1 level were normally better than models at NO and N2 levels. Among various models, the R-LR model performance was the best, which with the RMSE=0.720, REP=25.647 and the correlation coefficient r=0.747;
The validation of models universality using field rice dataset showed that the application of various models which based on experimental datasets in the field rice dataset was not only feasible and but also satisfying. Meantime, the validation of models universality using rape dataset indicated that, although it was feasible to apply the models in the rape dataset, the whole performances of various models in the rape dataset was not as good as they were in the field rice dataset. Furthermore, due to the narrow value range of rape nitrogen concentration (maximum is 2.84 mg/g, minimum is 1.07 mg/g), which is significantly lower than rice nitrogen concentration (maximum is 4.82 mg/g, minimum is 0.91 mg/g), the various models were normally overestimated the rape nitrogen concentrations.
(2) Quantitative Retrieval of nitrogen concentration using canopy spectral variables
In this section, the same methods to above section were adopted for the discussions of nitrogen estimation models at canopy level. Conclusions are followings:
Comparisons of nitrogen estimation models at different growth stages showed that the models at milky ripe stage and ripe stage were prior to models at other growth stages. The R-ANN model at ripe was the best, the RMSE and REP of the models were 0.746 and 48.147% respectively, and the correlation coefficient between the theoretical and the measured nitrogen concentration was 0.912, which achieved significant level;
Comparisons of nitrogen estimation models at different nitrogen fertilizer levels indicated that the various models at three nitrogen fertilizer levels all could achieve satisfying results, which is especially true for models at N1 level. The maximum correlation coefficient between the theoretical and the measured nitrogen was 0.962, the minimum was 0.799.
The similar results to leaf level model validation were obtained when using field rice dataset for validation of model universality at canopy level. The theoretical nitrogen concentrations retrieved from R-LR model and R-SVM model were significantly correlated with the measured nitrogen concentration, which with r=0.865 and r=0.854, respectively.
Besides, the results using rape dataset for the validation of model universality at canopy level were also similar to those at leaf level, i.e. the whole performances of various models in the rape dataset were not as good as they were in the field rice dataset, and the nitrogen concentrations were generally overestimated.
(3) Quantitative Retrieval of nitrogen concentration using TM data
In this section, the TM data and corresponding nitrogen concentration were used as data source, and the LR modeling method, the RBF method and SVM technology were adopted for modeling. On the basis of correlation analysis between nitrogen concentration and spectral variables, the models based on TM2, TM3, NDVI and RVI were established and the precision tests of models were done. Besides, in order to explore whether the models at leaf and canopy levels could be applied in TM data, the models based on simulated TM variables using leaf and canopy spectral reflectance were constructed and verified using TM data. The results indicated that retrieval of nitrogen information using TM data was not only feasible and satisfying. Comparison of various models' precisions showed that the LR method performed worst among the three kinds of modeling methods as a whole, and the further comparison indicated except for RVI-RBF model performed better than RVI-SVM model, the SVM models based on other three variables were generally performed better than RBF models. Among all models, the TM2-SVM model could achieve the best results, the theoretical nitrogen concentration derived from it were significantly correlated with the measured nitrogen concentration, with the correlation coefficient r=0.751.
引文
1.鲍艳松,王纪华,刘良云.不同尺度冬小麦氮素遥感监测方法及其应用研究.农业工程学报.2007,23(2):139-145.
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