Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT w...Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.展开更多
为了探讨Landsat 8 OIL数据在LAI大范围反演方面的应用潜力,使用Landsat 8 OIL影像,通过PROSAIL辐射传输模型,采用3种波段组合(Band2-7,Band2-5,Band3-5)建立了3个模拟冠层反射率-叶面积指数(LAI)查找表,用2种代价函数(Geman and Mc Cl...为了探讨Landsat 8 OIL数据在LAI大范围反演方面的应用潜力,使用Landsat 8 OIL影像,通过PROSAIL辐射传输模型,采用3种波段组合(Band2-7,Band2-5,Band3-5)建立了3个模拟冠层反射率-叶面积指数(LAI)查找表,用2种代价函数(Geman and Mc Clure代价函数,均方根误差代价函数)实现了对玉米、土豆、森林LAI的定量反演,并用LAI-2200测量数据作为相对真值对反演精度进行评价。结果表明:(1)使用Landsat 8数据,通过PROSAIL模型反演叶面积指数的精度是可以接受的,RMSE范围为在[0.892 4,1.205 0],R2范围为[0.721 3,0.873 3]。(2)Band5(近红外),Band4(红)Band3(绿)的波段组合反演效果在3种组合中精度最高,平均RMSE=0.993 1,R2=0.787 3。(3)Geman and Mc Clure代价函数比常用的均方根误差代价函数得到了更高的反演精度,平均RMSE=0.940 5,R2=0.817 5。(4)相对最优的反演策略是Band5,Band4,Band3的波段组合结合GM代价函数,RMSE=0.892 4,R2=0.873 3。(5)存在玉米土豆的反演值普遍低于测量值,而森林的反演值普遍高于测量值的问题。展开更多
以大豆叶面积指数(Leaf area index,LAI)反演为研究目标,利用PROSAIL模型和遗传算法优化后的BP神经网络模型,分别对重组自交系(Recombinant Inbred Lines,RIL)和自然野生大豆种群的LAI进行反演。结果表明,在对人工定向培育的RIL大豆种...以大豆叶面积指数(Leaf area index,LAI)反演为研究目标,利用PROSAIL模型和遗传算法优化后的BP神经网络模型,分别对重组自交系(Recombinant Inbred Lines,RIL)和自然野生大豆种群的LAI进行反演。结果表明,在对人工定向培育的RIL大豆种群冠层叶片LAI反演中,PROSAIL模型表现出了更优异的反演能力,而对品种繁多的自然野生大豆种群LAI反演中,遗传算法优化后的BP神经网络模型表现出了更好的适用性,并且上述2种模型在始粒期(R5)时性能最佳,PROSAIL模型和遗传算法优化后的BP神经网络模型R2分别为0.89和0.85,RMSE分别为0.11和0.13,EA均为97%,典型生育期内的反演性能均优于全生育期综合反演性能。因此,针对同一农作物不同种群的表型特征反演,需要根据研究对象的特征来选择合适的模型,以便于精确的估测大豆长势情况,为农作物的规模化育种监测提供数据支持。展开更多
基金funded by the National Natural Science Foundation of China(42071300)the Fujian Province Natural Science(2020J01504)+4 种基金the China Postdoctoral Science Foundation(2018M630728)the Open Fund of Fujian Provincial Key Laboratory of Resources and Environment Monitoring&Sustainable Management and Utilization(ZD202102)the Program for Innovative Research Team in Science and Technology in Fujian Province University(KC190002)the Open Fund of University Key Lab of Geomatics Technology and Optimize Resources Utilization in Fujian Province(fafugeo201901)supported by the Research Project of Jinjiang Fuda Science and Education Park Development Center(2019-JJFDKY-17)。
文摘Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.
文摘以大豆叶面积指数(Leaf area index,LAI)反演为研究目标,利用PROSAIL模型和遗传算法优化后的BP神经网络模型,分别对重组自交系(Recombinant Inbred Lines,RIL)和自然野生大豆种群的LAI进行反演。结果表明,在对人工定向培育的RIL大豆种群冠层叶片LAI反演中,PROSAIL模型表现出了更优异的反演能力,而对品种繁多的自然野生大豆种群LAI反演中,遗传算法优化后的BP神经网络模型表现出了更好的适用性,并且上述2种模型在始粒期(R5)时性能最佳,PROSAIL模型和遗传算法优化后的BP神经网络模型R2分别为0.89和0.85,RMSE分别为0.11和0.13,EA均为97%,典型生育期内的反演性能均优于全生育期综合反演性能。因此,针对同一农作物不同种群的表型特征反演,需要根据研究对象的特征来选择合适的模型,以便于精确的估测大豆长势情况,为农作物的规模化育种监测提供数据支持。
