This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to o...This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.展开更多
The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insuf...The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly effi-cient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observa-tions are deficient and a priori knowledge is introduced into inversion.展开更多
冰雪在短波区域具有很强的各向异性反射特征,对全球能量平衡及水循环等有重要作用。目前,国内外学者发展了一系列应用于冰雪的二向性反射分布函数BRDF(Bidirectional Reflectance Distribution Function)模型,全面比较和评估这些模型对...冰雪在短波区域具有很强的各向异性反射特征,对全球能量平衡及水循环等有重要作用。目前,国内外学者发展了一系列应用于冰雪的二向性反射分布函数BRDF(Bidirectional Reflectance Distribution Function)模型,全面比较和评估这些模型对星载多角度遥感产品的业务化模型选择有重要参考价值和指导意义。本文基于全球POLDER冰雪多角度反射率数据,选取3个模型,包括核驱动、半经验的MODIS业务化RTLSR模型、渐进辐射传输物理模型ART以及新发展的RTLSRS模型进行了全面比较分析,研究结果表明:(1)在拟合所有POLDER数据时,RTLSRS模型都具有最高精度,对于单组纯雪数据,RTLSRS模型的最小二乘拟合的均方根误差(RMSE)比ART模型降低了45.45%,仅为RTLSR模型的18.46%。对于非纯雪数据,RTLSRS模型与RTLSR模型的拟合能力总体差别不大,但其RMSE比RTLSR模型降低了67.5%,ART模型的精度最差。(2)虽然RTLSRS可以高精度拟合所有数据,但该模型拟合纯雪(R2=0.969,RMSE=0.012)精度较优于非纯雪(R2=0.926,RMSE=0.013)。(3)对RTLSRS模型进行简化,仅保留其各向同性核和雪核ISM(Isotropic-Snow Model),验证结果表明:简化后的模型能够很好地表征雪的二向散射能力,使用POLDER全部纯雪数据进行拟合时,R2达到了0.949,RMSE为0.034。本文有助于用户在应用冰雪多角度数据时选择更合适的BRDF模型,同时对理解这些模型的误差提供了有价值的参考.展开更多
为探究双向反射分布(BidirectionalReflectanceDistributionFunction,BRDF)模型改进的光化学植被指数(Photochemical Reflectance Index,PRI)反演水稻冠层光能利用率(Light Use Efficiency,LUE)能力,该研究利用多角度水稻冠层辐射数据...为探究双向反射分布(BidirectionalReflectanceDistributionFunction,BRDF)模型改进的光化学植被指数(Photochemical Reflectance Index,PRI)反演水稻冠层光能利用率(Light Use Efficiency,LUE)能力,该研究利用多角度水稻冠层辐射数据和同期通量观测数据,引入BRDF模型对多角度PRI进行观测角度标准化处理。获取晴天09:00—15:00每半小时数据156组,其中130组数据用来建模,另外26组数据对所建模型进行验证。结果表明:BRDF模型在晴空指数(ClearnessIndex,CI)较低时拟合效果较差,随着CI的升高模型模拟效果变好;BRDF模型的拟合参数受光照条件和植被状况的影响,不同CI范围下的各向同性权值ki与LUE相关性均良好(决定系数大于0.3),在0.6≤CI<0.7时相关性最佳,决定系数为0.63;无论是否采用BRDF模型的角度校正,由PRI反演LUE的模型均可采用线性形式或指数形式;但采用BRDF模型的角度校正后,反演模型精度得到显著提升,决定系数从0.46(P<0.01,校正前)提高到0.8(P<0.01,校正后);验证结果显示,采用BRDF模型的角度校正前后,相对反演偏差指数由1.34提升到2.6,同时验证的拟合决定系数也由0.44提高到0.87。该研究相比较传统多角度遥感观测的PRI指数,BRDF模型的角度修正提高了PRI对水稻LUE的反演能力,证明了多角度冠层光谱观测可利用BRDF模型提高其植被指数对植物生理活动探测能力的可行性。展开更多
基金supported by JSPS KAKENHI (Grants 17K06633 and 18K18898)
文摘This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.
基金supported by the National Natural Science Foundation of China(Grant No.40471095)the National Basic Research Program(Grant No.G2000077908)the International Cooperation Project of MOST(Grant No.2004DFA06300).
文摘The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly effi-cient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observa-tions are deficient and a priori knowledge is introduced into inversion.
文摘冰雪在短波区域具有很强的各向异性反射特征,对全球能量平衡及水循环等有重要作用。目前,国内外学者发展了一系列应用于冰雪的二向性反射分布函数BRDF(Bidirectional Reflectance Distribution Function)模型,全面比较和评估这些模型对星载多角度遥感产品的业务化模型选择有重要参考价值和指导意义。本文基于全球POLDER冰雪多角度反射率数据,选取3个模型,包括核驱动、半经验的MODIS业务化RTLSR模型、渐进辐射传输物理模型ART以及新发展的RTLSRS模型进行了全面比较分析,研究结果表明:(1)在拟合所有POLDER数据时,RTLSRS模型都具有最高精度,对于单组纯雪数据,RTLSRS模型的最小二乘拟合的均方根误差(RMSE)比ART模型降低了45.45%,仅为RTLSR模型的18.46%。对于非纯雪数据,RTLSRS模型与RTLSR模型的拟合能力总体差别不大,但其RMSE比RTLSR模型降低了67.5%,ART模型的精度最差。(2)虽然RTLSRS可以高精度拟合所有数据,但该模型拟合纯雪(R2=0.969,RMSE=0.012)精度较优于非纯雪(R2=0.926,RMSE=0.013)。(3)对RTLSRS模型进行简化,仅保留其各向同性核和雪核ISM(Isotropic-Snow Model),验证结果表明:简化后的模型能够很好地表征雪的二向散射能力,使用POLDER全部纯雪数据进行拟合时,R2达到了0.949,RMSE为0.034。本文有助于用户在应用冰雪多角度数据时选择更合适的BRDF模型,同时对理解这些模型的误差提供了有价值的参考.
文摘为探究双向反射分布(BidirectionalReflectanceDistributionFunction,BRDF)模型改进的光化学植被指数(Photochemical Reflectance Index,PRI)反演水稻冠层光能利用率(Light Use Efficiency,LUE)能力,该研究利用多角度水稻冠层辐射数据和同期通量观测数据,引入BRDF模型对多角度PRI进行观测角度标准化处理。获取晴天09:00—15:00每半小时数据156组,其中130组数据用来建模,另外26组数据对所建模型进行验证。结果表明:BRDF模型在晴空指数(ClearnessIndex,CI)较低时拟合效果较差,随着CI的升高模型模拟效果变好;BRDF模型的拟合参数受光照条件和植被状况的影响,不同CI范围下的各向同性权值ki与LUE相关性均良好(决定系数大于0.3),在0.6≤CI<0.7时相关性最佳,决定系数为0.63;无论是否采用BRDF模型的角度校正,由PRI反演LUE的模型均可采用线性形式或指数形式;但采用BRDF模型的角度校正后,反演模型精度得到显著提升,决定系数从0.46(P<0.01,校正前)提高到0.8(P<0.01,校正后);验证结果显示,采用BRDF模型的角度校正前后,相对反演偏差指数由1.34提升到2.6,同时验证的拟合决定系数也由0.44提高到0.87。该研究相比较传统多角度遥感观测的PRI指数,BRDF模型的角度修正提高了PRI对水稻LUE的反演能力,证明了多角度冠层光谱观测可利用BRDF模型提高其植被指数对植物生理活动探测能力的可行性。
