遥感提取植被生化组分信息方法与模型研究
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摘要
植被是生态系统中最重要的组成成分之一。正确估计植被的生化含量为了解不同尺度的生态系统功能提供了非常有用的帮助。遥感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。本文围绕遥感提取植被生化组分信息这一中心，从经验半经验的统计回归提取方法到反演物理模型来提取的方法，从方法分析到模型建立，进行了一个较为系统的研究。本文研究的主要内容和结论如下：
首先回顾了不同的冠层和叶片物理模型，以生化组分反演为目的，对两个典型叶片模型PROSPECT和LIBERTY进行了详细讨论，从理论上模拟了叶片光谱，分析了PROSPECT和LIBERTY模型的每个参数的敏感性。
其次讨论了利用经验和半经验关系提取植被生化组分信息的方法：1)用多元回归方法提取了干叶片纤维素，木质素，总碳和总氮含量，发现对于干叶片，无论是直接利用反射率还是利用反射率一阶导数都能得到很好的回归效果，并且能够很好的估计检验样本的组分含量，其中反射率一阶导数能够有效地去除部分干扰因素，表现要好于反射率。2)在叶片水平，从理论上分析了目前研究者提出的各种光谱指数的可用性及对叶片种类的敏感性，解释了为什么研究者利用这些指数都能很好地建立和叶绿素含量的回归关系，但又由于这些指数对于叶片类型的敏感性，从某个样本集上建立的的预测模型用于其它样本时会失效。考虑到要对叶片类型尽量不敏感，所建立的模型又要简单易用，本文利用指数GM建立了叶片叶绿素含量提取模型，通过实验数据验证，估计值和实际值非常一致，是一种可能加以应用的普适模型。3)在冠层水平，利用光谱指数TCARI和土壤可调节指数OSAVI的结合，能够明显地去除背景因素和LAI的影响，通过分析前人模型的优缺点，提出了一个新的叶绿素含量提取模型，经过实测玉米冠层数据验证，认为是可以用于提取农作物冠层叶绿素含量的方法。
接下来本文对利用反演物理模型来提取生化组分含量的方法和存在的问题进行了详细的讨论：1)该方法受模型可反演性的约束，只有那些可反演的模型才能用于提取生化组分含量，叶片模型PROSPECT是一个完全可反演的模型，而LIBERTY模型对于生化组分含量的准确反演则依赖于对其它模型参数的准确先验知识。2)通过分析数据中的噪声性质，对反演所用的代价函数的选择进行了讨论，对于具体的应用问题，反演中应根据数据的性质选择不同的代价函数，如果数据中存在系统噪声，利用相关系数构造代价函数可明显地减小反演对于系统噪声的敏感性。3)以应用为目的，对于不同的反演算法(传统迭代优化算法SIMPLEX法，NEWTON法，Levenberg—Marquardt(LM)法以及近年被广泛应用的遗传算法)的反演结果从反演精度和所需要的CPU时间两方面进行了比较，指出LM是所讨论算法中反演准确性和耗费机
遥感提取植被生化组分信息方法与模型研究
时综合性能最好的;4)实际的反演问题常常是非常复杂的，利用多阶段反演，能够
将复杂的问题化简，本文通过分隔参数集和数据集，在不同的波段反演不同的生化组
分以及反演冠层模型得到叶片光谱，再反演叶片光谱得到生化组分的实践说明了多阶
段反演的可行性;5)高光谱数据存在冗余，因此，进行波段选择是必要的，针对叶
绿素和水分含量反演进行了波段选择，并对所选波段的意义进行了分析。反演结果说
明，并不是波段数越多，反演生化组分信息就越准确，当波段数达到一定数目后，反
演准确性不再提高，因此实际反演中只需要选择几个最优波段的组合就能达到目的;
6)探讨了贝叶斯反演在生化组分反演中的作用，分别在叶片水平和冠层水平利用实
测数据进行了生化组分含量的贝叶斯反演，并与其他的反演方法进行了对比，结果证
明贝叶斯反演充分利用先验知识，反演结果受观测数据和先验知识的共同制约，对于
大噪声数据，利用贝叶斯反演能够很好地约束反演结果。
最后本文提出了一个冠层光谱的多项式表达模型，利用高阶多项式来描述光在冠
层中的多次散射过程，不仅能很好地拟合冠层光谱，而且多项式系数具有明确的物理
意义。植被结构信息体现在多项式系数上，对于任何结构的植被，都可以清楚地描述。
多项式系数对于传统冠层模型中的热点现象、观测角度、LAI及叶片倾角的影响都能
够真实地反映。利用多项式表达祸合叶片模型PROSPECT反演了两个不同类型的冠层的
生化组分含量，得到了满意的结果。此外，反演实测的玉米冠层多角度高挂高光谱数据，
获得了比较理想的叶绿素含量反演结果。说明多项式表达是一个有理论意义和潜在应
用价值的新方法。
Vegetation is one of the most important components of the ecosystem. So it is important to obtain the content and spatial distribution of vegetation biochemical information over local to regional and eventually global scales. Remote sensing provides an easy and versatile tool to accurately estimate biochemical content information at different scales. Centered on biochemical information retrieval by remote sensing, this paper did a detailed study from retrieval by empirical and semi-empirical regression methods to retrieval by physical model inversion, and from method analysis to model development. The main contents and conclusions of this paper are summarized below.
First, after different canopy and leaf physical models were reviewed, two traditional leaf models, PROSPECT and LIBERTY, were discussed in detail with respect to the aim of biochemical inversion. Leaf spectra were modeled theoretically and every parameter's sensitivity of these two models was analyzed.
Second, empirical and semi-empirical vegetation biochemical information retrieval methods were discussed. l)Cellulose, lignin, total carbon and total nitrogen concentration of dry leaf were estimated by multiple regression. It was found out that good regression results can be obtained both by reflectance and first derivative of spectra, and biochemical concentration can be well estimated from validation data with the relationship derived by correction data. And the first derivative of reflectance performed better than reflectance. 2)Chlorophyll content retrieval methods at both leaf and canopy level were analyzed from theoretical as well as practical point of view. At leaf level, the applicability of different kinds of spectral indexes and their sensitivity to leaf types were analyzed, which explained why researchers can construct a good regression relationship between these indexes and chlorophyll content, but because these indexes are sensitive to leaf types, the estimation regression relationship made by
some samples can not be applied to other samples. Considering as less sensitive as possible to leaf types and easy for application, this paper
gave a chlorophyll content estimation relationship made with index GM; and testing with experiment data, the estimation and real values were consistent, which showed that it is a possibly applicable model. At canopy level, with spectral index TCARI and soil adjusted index OSAVI, the background and LAI's effects can be effectively removed. By analyzing former model's merits and drawbacks, a new chlorophyll content retrieval model was put forward at canopy level, and through validation with experimental com data, it is thought to be applicable to crop canopy chlorophyll content estimation.
Third, a detailed discussion of physical model inversion to estimate biochemical content and existing problems was made. l)This method is limited by models' invertibility: only those models that are invertible can be applied to estimate biochemical content. Leaf model PROSPECT is totally invertible while accurate inversion of biochemical content information by LIBERTY model relies on accurate a priori knowledge of the other model parameter. 2)Through analyzing the properties of noises, how to choose the cost function used in inversion process was discussed. It is pointed out that for specific application, different cost function should be chosen according to data's property in inversion: if there exists systematic noise, correlation coefficient can be used in cost function to reduce inversion results' sensitivity to systematic noise. 3)Aiming at application, the performance of different algorithms(traditional optimization algorithm SIMPLEX, NEWTON, Levenberg-Marquardt(LM) and genetic algorithm that is widely used in recent years) were compared in terms of inversion accuracy and CPU time cost. The study pointed out that LM algorithm is the best one. 4)Actual inversion problem is often very complex, multi-stage inversion can simplify complicated inversion problems. In this paper, the feasibility of multi-stage inversion was
首先回顾了不同的冠层和叶片物理模型，以生化组分反演为目的，对两个典型叶片模型PROSPECT和LIBERTY进行了详细讨论，从理论上模拟了叶片光谱，分析了PROSPECT和LIBERTY模型的每个参数的敏感性。
其次讨论了利用经验和半经验关系提取植被生化组分信息的方法：1)用多元回归方法提取了干叶片纤维素，木质素，总碳和总氮含量，发现对于干叶片，无论是直接利用反射率还是利用反射率一阶导数都能得到很好的回归效果，并且能够很好的估计检验样本的组分含量，其中反射率一阶导数能够有效地去除部分干扰因素，表现要好于反射率。2)在叶片水平，从理论上分析了目前研究者提出的各种光谱指数的可用性及对叶片种类的敏感性，解释了为什么研究者利用这些指数都能很好地建立和叶绿素含量的回归关系，但又由于这些指数对于叶片类型的敏感性，从某个样本集上建立的的预测模型用于其它样本时会失效。考虑到要对叶片类型尽量不敏感，所建立的模型又要简单易用，本文利用指数GM建立了叶片叶绿素含量提取模型，通过实验数据验证，估计值和实际值非常一致，是一种可能加以应用的普适模型。3)在冠层水平，利用光谱指数TCARI和土壤可调节指数OSAVI的结合，能够明显地去除背景因素和LAI的影响，通过分析前人模型的优缺点，提出了一个新的叶绿素含量提取模型，经过实测玉米冠层数据验证，认为是可以用于提取农作物冠层叶绿素含量的方法。
接下来本文对利用反演物理模型来提取生化组分含量的方法和存在的问题进行了详细的讨论：1)该方法受模型可反演性的约束，只有那些可反演的模型才能用于提取生化组分含量，叶片模型PROSPECT是一个完全可反演的模型，而LIBERTY模型对于生化组分含量的准确反演则依赖于对其它模型参数的准确先验知识。2)通过分析数据中的噪声性质，对反演所用的代价函数的选择进行了讨论，对于具体的应用问题，反演中应根据数据的性质选择不同的代价函数，如果数据中存在系统噪声，利用相关系数构造代价函数可明显地减小反演对于系统噪声的敏感性。3)以应用为目的，对于不同的反演算法(传统迭代优化算法SIMPLEX法，NEWTON法，Levenberg—Marquardt(LM)法以及近年被广泛应用的遗传算法)的反演结果从反演精度和所需要的CPU时间两方面进行了比较，指出LM是所讨论算法中反演准确性和耗费机
遥感提取植被生化组分信息方法与模型研究
时综合性能最好的;4)实际的反演问题常常是非常复杂的，利用多阶段反演，能够
将复杂的问题化简，本文通过分隔参数集和数据集，在不同的波段反演不同的生化组
分以及反演冠层模型得到叶片光谱，再反演叶片光谱得到生化组分的实践说明了多阶
段反演的可行性;5)高光谱数据存在冗余，因此，进行波段选择是必要的，针对叶
绿素和水分含量反演进行了波段选择，并对所选波段的意义进行了分析。反演结果说
明，并不是波段数越多，反演生化组分信息就越准确，当波段数达到一定数目后，反
演准确性不再提高，因此实际反演中只需要选择几个最优波段的组合就能达到目的;
6)探讨了贝叶斯反演在生化组分反演中的作用，分别在叶片水平和冠层水平利用实
测数据进行了生化组分含量的贝叶斯反演，并与其他的反演方法进行了对比，结果证
明贝叶斯反演充分利用先验知识，反演结果受观测数据和先验知识的共同制约，对于
大噪声数据，利用贝叶斯反演能够很好地约束反演结果。
最后本文提出了一个冠层光谱的多项式表达模型，利用高阶多项式来描述光在冠
层中的多次散射过程，不仅能很好地拟合冠层光谱，而且多项式系数具有明确的物理
意义。植被结构信息体现在多项式系数上，对于任何结构的植被，都可以清楚地描述。
多项式系数对于传统冠层模型中的热点现象、观测角度、LAI及叶片倾角的影响都能
够真实地反映。利用多项式表达祸合叶片模型PROSPECT反演了两个不同类型的冠层的
生化组分含量，得到了满意的结果。此外，反演实测的玉米冠层多角度高挂高光谱数据，
获得了比较理想的叶绿素含量反演结果。说明多项式表达是一个有理论意义和潜在应
用价值的新方法。
Vegetation is one of the most important components of the ecosystem. So it is important to obtain the content and spatial distribution of vegetation biochemical information over local to regional and eventually global scales. Remote sensing provides an easy and versatile tool to accurately estimate biochemical content information at different scales. Centered on biochemical information retrieval by remote sensing, this paper did a detailed study from retrieval by empirical and semi-empirical regression methods to retrieval by physical model inversion, and from method analysis to model development. The main contents and conclusions of this paper are summarized below.
First, after different canopy and leaf physical models were reviewed, two traditional leaf models, PROSPECT and LIBERTY, were discussed in detail with respect to the aim of biochemical inversion. Leaf spectra were modeled theoretically and every parameter's sensitivity of these two models was analyzed.
Second, empirical and semi-empirical vegetation biochemical information retrieval methods were discussed. l)Cellulose, lignin, total carbon and total nitrogen concentration of dry leaf were estimated by multiple regression. It was found out that good regression results can be obtained both by reflectance and first derivative of spectra, and biochemical concentration can be well estimated from validation data with the relationship derived by correction data. And the first derivative of reflectance performed better than reflectance. 2)Chlorophyll content retrieval methods at both leaf and canopy level were analyzed from theoretical as well as practical point of view. At leaf level, the applicability of different kinds of spectral indexes and their sensitivity to leaf types were analyzed, which explained why researchers can construct a good regression relationship between these indexes and chlorophyll content, but because these indexes are sensitive to leaf types, the estimation regression relationship made by
some samples can not be applied to other samples. Considering as less sensitive as possible to leaf types and easy for application, this paper
gave a chlorophyll content estimation relationship made with index GM; and testing with experiment data, the estimation and real values were consistent, which showed that it is a possibly applicable model. At canopy level, with spectral index TCARI and soil adjusted index OSAVI, the background and LAI's effects can be effectively removed. By analyzing former model's merits and drawbacks, a new chlorophyll content retrieval model was put forward at canopy level, and through validation with experimental com data, it is thought to be applicable to crop canopy chlorophyll content estimation.
Third, a detailed discussion of physical model inversion to estimate biochemical content and existing problems was made. l)This method is limited by models' invertibility: only those models that are invertible can be applied to estimate biochemical content. Leaf model PROSPECT is totally invertible while accurate inversion of biochemical content information by LIBERTY model relies on accurate a priori knowledge of the other model parameter. 2)Through analyzing the properties of noises, how to choose the cost function used in inversion process was discussed. It is pointed out that for specific application, different cost function should be chosen according to data's property in inversion: if there exists systematic noise, correlation coefficient can be used in cost function to reduce inversion results' sensitivity to systematic noise. 3)Aiming at application, the performance of different algorithms(traditional optimization algorithm SIMPLEX, NEWTON, Levenberg-Marquardt(LM) and genetic algorithm that is widely used in recent years) were compared in terms of inversion accuracy and CPU time cost. The study pointed out that LM algorithm is the best one. 4)Actual inversion problem is often very complex, multi-stage inversion can simplify complicated inversion problems. In this paper, the feasibility of multi-stage inversion was
引文
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