基于机器学习和辐射传输模型的农作物叶绿素含量高光谱反演模型
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摘要
快速准确地定量获取农作物叶绿素含量对于大范围农作物生长健康状况监测、产量估算具有极其重要的意义。由于辐射传输模型通常比较复杂,同时农作物光谱与叶绿素含量是非线性的关系,并且地表环境系统包含众多不确定性因素,传统的辐射传输模型反演技术已不能满足精确估算农作物叶绿素含量的需要。机器学习算法能够表达植物生物化学参数与光谱反射率之间隐含、潜在的非线性函数关系,这有可能使机器学习更加适合于通过辐射传输模型反演获取农作物生物化学参数。如何将机器学习中的算法引入到辐射传输模型,用于构建农作物叶绿素含量高光谱反演模型,提高估算农作物叶绿素含量的精度、解释模型输出的结果,是一个值得深入研究的关键问题。
本文在叶片尺度上,通过PROSPECT模拟农作物光谱,将叶片叶绿素含量与光谱特征联系起来；在冠层尺度,通过机器学习和PROSAIL反演估算了冠层叶绿素含量。论文的主要内容和创新点如下：
(1)分别采用一阶导数、包络线去除、小波变换降噪等对ASD实测光谱和Hyperion影像上的纯净像元光谱增强变换。分析了PROSPECT模型模拟的叶片光谱、PROSAIL模拟的冠层光谱对不同叶绿素含量的光谱响应。利用先验知识解决了遥感反演的病态问题。建立了叶片尺度和冠层尺度农作物光谱数据集。
(2)运用遗传算法和粒子群优化支持向量机参数C和γ选择,提出GA-SVM、PSO-SVM模型,分别应用GA-SVM、PSO-SVM与PROSPECT反演获取农作物叶片叶绿素含量。结果表明,PSO-SVM和PROSPECT反演估算叶绿素含量精度高于GA-SVM反演PROSPECT估算叶绿素含量精度。因此,PSO-SVM对于确定SVM参数、提高支持向量机与PROSPECT反演农作物叶片叶绿素含量精度有重要价值。
(3)将梯度助推机与辐射传输模型结合,提出了基于梯度助推机和PROSPECT的农作物叶片叶绿素含量遥感反演模型,即GBM-PROSPECT。应用GBM-PROSPECT在demy Cλ光谱数据集上估算农作物叶片叶绿素含量,结果表明,估算模型R2为0.9714,MSE为36.9652,与SVM-PROSPECT、RF-PROSPECT相比,GBM-PROSPECT估算农作物叶片叶绿素含量精度最高。因而,在叶片尺度上,GBM-PROSPECT更适合获取叶绿素含量。
(4)运用随机森林进行农作物冠层叶绿素含量遥感反演,构建了RF-PROSAIL模型。使用RF-PROSAIL在db9 Cλ光谱数据集上估算研究区农作物冠层叶绿素含量。结果表明,估算模型R2为0.9000,MSE为1670.4,并且RF-PROSAIL运算时间少于GBM-PROSAIL运算时间,表明估算农作物冠层叶绿素含量RF-PROSAIL优于GBM-PROSAIL。因此,在冠层尺度,RF-PROSAIL最适合估算农作物叶绿素含量。
(5)分析了PROSPECT模型、SAIL模型的局限性,探讨了不同尺度高光谱模型反演估算农作物叶绿素含量误差来源。未来研究应建立融合空间格局分析、改进的辐射传输模型、农作物生物理化参数数据库、随机森林算法的综合尺度提升框架,应用于多源遥感数据协同估算农作物冠层叶绿素含量。
An accurate quantitative estimation of crop chlorophyll content is of great importance for a wide range of monitoring crop grow health condition and estimating biomass,since radiative transfer model are complex caused by the nonlinear relationship between crop specral and chlorophyll content and the uncertainties in the land surface systems, traditional inversion techniques can not satisfied with the demand of accurate estimation of chlorophyll content.Alternatively, machine learning algorithms are able to cope with the strong nonlinearity of the functional dependence between the biophysical parameter and the observed reflected radiance, they may therefore be more suitable candidates for estimating crop biochemistry parameters from inversion of radiative transfer model. It is crucial to apply machine learning algorithms for inversion of radiative transfer model, so as to construct hyperspectral remote sensing estimation model for crop chlorophyll content.
The thesis first linked crop leaf level optical properties and chlorophyll content throuth the inversion of radiative transfer model, PROSPECT. Next, crop chlorophyll content scaled-up to the crop canopy level was estimated using machine learning and PROSAIL. The main conclusions and creative points are as follows:
(1) First derivative, continuum removal, wavelet transform denoising were applied to measured leaf reflectance spectral and crop canopy reflectance spectral from Hyperion images to enhance chlorophyll absorption features. Next, the responses of crop leaf reflectance spectral generated by PROSPECT and canopy reflectance spectral generated by PROSAIL were analyzed with different chlorophyll contents. The ill-posed inverse problem of remote sensing was solved using prior information. Then, leaf-leavel spectral datasets and canopy-level spectral datasets of crops were created.
(2) Genetic algorithm (GA) and particle swarm optimization (PSO) based approaches for determination the penalty parameter C and the kernel function parameter y of support vector machines (SVM), term GA-SVM and PSO-SVM were proposed. GA-SVM and PSO-SVM were applied to invert PROSPECT for retrieval of crop leaf chlorophyll content. The results demonstrate that the estimation accuracy of PSO-SVM approach surpass GA-PSO.Therefore, the PSO-SVM approach is valuable for parameter determination of SVM.
(3) This study is the first couple gradient boosting machines (GBM) with PROSPECT, a hyperspectral remote sensing model, term GBM-PROSPECT, was developed for estimating crop leaf chlorophyll content. The developed model was compared with SVM-PROSPECT and RF-PROSPECT. The results show that GBM-PROSPECT yield an R2 of 0.9714 and a mean square error (MSE) of 36.9652 using the demyCλspectral datasets. Compared with SVM-PROSEPCT and RF-PROSPECT, GBM-PROSPECT got the highest chlorophyll estimation accuracy, therefore, GBM-PROSPECT is more suitable for crop chlorophyll content estimation at leaf level.
(4) This study demonstrated that couple random forests (RF) with PROSAIL, a hyperspectral remote sensing model, term RF-PROSAIL, was developed for estimating crop canopy chlorophyll content. The developed model was compared with GBM-PROSAIL. The results show that GBM-PROSPECT yielded an R2 of 0.9000 and a mean square error (MSE) of 1670.4 using the db9Cλspectral datasets. Compared with GBM-PROSAIL, RF-PROSAIL got the highest chlorophyll estimation accuracy, and the computation time of RF-PROSAIL less than that of GBM-PROSAIL, therefore, RF-PROSAIL is more suitable for crop chlorophyll content estimation at canopy level.
(5) The limation of PROSPECT and SAIL were analyzed, the errors generated in developed hyperspectral remote sensing inversion model for estimating crop chlorophyll content at different scales were discussed. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, improved radiative transfer models, crop biophysical/biochemistry parameters database, RF to retrieve crop chlorophyll content from multi-source remote sensing data collaboratively at the canopy level.
本文在叶片尺度上,通过PROSPECT模拟农作物光谱,将叶片叶绿素含量与光谱特征联系起来；在冠层尺度,通过机器学习和PROSAIL反演估算了冠层叶绿素含量。论文的主要内容和创新点如下：
(1)分别采用一阶导数、包络线去除、小波变换降噪等对ASD实测光谱和Hyperion影像上的纯净像元光谱增强变换。分析了PROSPECT模型模拟的叶片光谱、PROSAIL模拟的冠层光谱对不同叶绿素含量的光谱响应。利用先验知识解决了遥感反演的病态问题。建立了叶片尺度和冠层尺度农作物光谱数据集。
(2)运用遗传算法和粒子群优化支持向量机参数C和γ选择,提出GA-SVM、PSO-SVM模型,分别应用GA-SVM、PSO-SVM与PROSPECT反演获取农作物叶片叶绿素含量。结果表明,PSO-SVM和PROSPECT反演估算叶绿素含量精度高于GA-SVM反演PROSPECT估算叶绿素含量精度。因此,PSO-SVM对于确定SVM参数、提高支持向量机与PROSPECT反演农作物叶片叶绿素含量精度有重要价值。
(3)将梯度助推机与辐射传输模型结合,提出了基于梯度助推机和PROSPECT的农作物叶片叶绿素含量遥感反演模型,即GBM-PROSPECT。应用GBM-PROSPECT在demy Cλ光谱数据集上估算农作物叶片叶绿素含量,结果表明,估算模型R2为0.9714,MSE为36.9652,与SVM-PROSPECT、RF-PROSPECT相比,GBM-PROSPECT估算农作物叶片叶绿素含量精度最高。因而,在叶片尺度上,GBM-PROSPECT更适合获取叶绿素含量。
(4)运用随机森林进行农作物冠层叶绿素含量遥感反演,构建了RF-PROSAIL模型。使用RF-PROSAIL在db9 Cλ光谱数据集上估算研究区农作物冠层叶绿素含量。结果表明,估算模型R2为0.9000,MSE为1670.4,并且RF-PROSAIL运算时间少于GBM-PROSAIL运算时间,表明估算农作物冠层叶绿素含量RF-PROSAIL优于GBM-PROSAIL。因此,在冠层尺度,RF-PROSAIL最适合估算农作物叶绿素含量。
(5)分析了PROSPECT模型、SAIL模型的局限性,探讨了不同尺度高光谱模型反演估算农作物叶绿素含量误差来源。未来研究应建立融合空间格局分析、改进的辐射传输模型、农作物生物理化参数数据库、随机森林算法的综合尺度提升框架,应用于多源遥感数据协同估算农作物冠层叶绿素含量。
An accurate quantitative estimation of crop chlorophyll content is of great importance for a wide range of monitoring crop grow health condition and estimating biomass,since radiative transfer model are complex caused by the nonlinear relationship between crop specral and chlorophyll content and the uncertainties in the land surface systems, traditional inversion techniques can not satisfied with the demand of accurate estimation of chlorophyll content.Alternatively, machine learning algorithms are able to cope with the strong nonlinearity of the functional dependence between the biophysical parameter and the observed reflected radiance, they may therefore be more suitable candidates for estimating crop biochemistry parameters from inversion of radiative transfer model. It is crucial to apply machine learning algorithms for inversion of radiative transfer model, so as to construct hyperspectral remote sensing estimation model for crop chlorophyll content.
The thesis first linked crop leaf level optical properties and chlorophyll content throuth the inversion of radiative transfer model, PROSPECT. Next, crop chlorophyll content scaled-up to the crop canopy level was estimated using machine learning and PROSAIL. The main conclusions and creative points are as follows:
(1) First derivative, continuum removal, wavelet transform denoising were applied to measured leaf reflectance spectral and crop canopy reflectance spectral from Hyperion images to enhance chlorophyll absorption features. Next, the responses of crop leaf reflectance spectral generated by PROSPECT and canopy reflectance spectral generated by PROSAIL were analyzed with different chlorophyll contents. The ill-posed inverse problem of remote sensing was solved using prior information. Then, leaf-leavel spectral datasets and canopy-level spectral datasets of crops were created.
(2) Genetic algorithm (GA) and particle swarm optimization (PSO) based approaches for determination the penalty parameter C and the kernel function parameter y of support vector machines (SVM), term GA-SVM and PSO-SVM were proposed. GA-SVM and PSO-SVM were applied to invert PROSPECT for retrieval of crop leaf chlorophyll content. The results demonstrate that the estimation accuracy of PSO-SVM approach surpass GA-PSO.Therefore, the PSO-SVM approach is valuable for parameter determination of SVM.
(3) This study is the first couple gradient boosting machines (GBM) with PROSPECT, a hyperspectral remote sensing model, term GBM-PROSPECT, was developed for estimating crop leaf chlorophyll content. The developed model was compared with SVM-PROSPECT and RF-PROSPECT. The results show that GBM-PROSPECT yield an R2 of 0.9714 and a mean square error (MSE) of 36.9652 using the demyCλspectral datasets. Compared with SVM-PROSEPCT and RF-PROSPECT, GBM-PROSPECT got the highest chlorophyll estimation accuracy, therefore, GBM-PROSPECT is more suitable for crop chlorophyll content estimation at leaf level.
(4) This study demonstrated that couple random forests (RF) with PROSAIL, a hyperspectral remote sensing model, term RF-PROSAIL, was developed for estimating crop canopy chlorophyll content. The developed model was compared with GBM-PROSAIL. The results show that GBM-PROSPECT yielded an R2 of 0.9000 and a mean square error (MSE) of 1670.4 using the db9Cλspectral datasets. Compared with GBM-PROSAIL, RF-PROSAIL got the highest chlorophyll estimation accuracy, and the computation time of RF-PROSAIL less than that of GBM-PROSAIL, therefore, RF-PROSAIL is more suitable for crop chlorophyll content estimation at canopy level.
(5) The limation of PROSPECT and SAIL were analyzed, the errors generated in developed hyperspectral remote sensing inversion model for estimating crop chlorophyll content at different scales were discussed. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, improved radiative transfer models, crop biophysical/biochemistry parameters database, RF to retrieve crop chlorophyll content from multi-source remote sensing data collaboratively at the canopy level.
引文
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