为明晰植被冠层叶绿素荧光遥感信息与总初级生产力(Gross Primary Productivity,GPP)间关系,以提高GPP估算精度,该文以典型C3(冬小麦)和C4(夏玉米)作物为例,利用不同传感器采集高频率光谱数据,提取了红光区和远红光区作物冠层日光诱导...为明晰植被冠层叶绿素荧光遥感信息与总初级生产力(Gross Primary Productivity,GPP)间关系,以提高GPP估算精度,该文以典型C3(冬小麦)和C4(夏玉米)作物为例,利用不同传感器采集高频率光谱数据,提取了红光区和远红光区作物冠层日光诱导叶绿素荧光(Sun-induced chlorophyll Fluorescence,SIF)遥感信息。结合通量观测数据,分析了SIF与GPP的日变化特征,并探讨了基于SIF估算作物GPP的能力和差异性。结果表明:1)C3、C4作物的GPP日变化特征差异明显,前者呈“双峰”特征,后者呈“单峰”特征;而SIF760和SIF687均呈现明显的“单峰”特征,即早晚低、午间高;2)不同类型传感器对SIF数值大小的影响强于对其日变化特征的影响,同时,低光谱分辨率传感器对SIF687具有明显的高估现象,且对C3作物的高估强于C4作物;3)C3、C4作物SIF760和SIF687均与冠层吸收光合有效辐射(Absorbed Photosynthetic Active Radiation,APAR)呈显著的线性正相关关系( R 2>0.8),可以直接用于APAR产品的反演;4)针对远红光区SIF,单日观测数据分析结果表明,C3、C4作物SIF760与GPP呈显著的线性正相关关系( R 2>0.6),而基于多日观测数据构建的非线性对数关系模型优于线性关系模型;针对红光区SIF,无论是基于单日还是多日观测数据,C3、C4作物均适宜采用一种非线性对数关系模型来估算GPP( R 2 >0.7),且模型更为稳健。展开更多
二氧化碳捕集与封存(CO2 capture and storage,CCS)是全球CO2减排最重要技术战略,但CCS相关项目存在CO2泄漏的风险不容忽视,及时有效的识别与监测项目区CO2泄漏至关重要。文中以7种典型C3、C4植物为研究对象,通过模拟地质封存CO2泄漏产...二氧化碳捕集与封存(CO2 capture and storage,CCS)是全球CO2减排最重要技术战略,但CCS相关项目存在CO2泄漏的风险不容忽视,及时有效的识别与监测项目区CO2泄漏至关重要。文中以7种典型C3、C4植物为研究对象,通过模拟地质封存CO2泄漏产生的超高CO2浓度对植物稳定碳同位素组成δ13C的影响,分析C3、C4植物δ13C值与超高CO2浓度之间的关系,试图利用植物δ13C分析识别CO2泄漏。结果表明:C3、C4植物的δ13C值能够迅速响应CO2浓度的变化,均呈现随着CO2浓度的增加而先迅速下降后缓慢稳定的态势,其中C3、C4植物δ13C值分别在CO2浓度为10000μmol·mol-1、20000μmol·mol-1时降低显著,C3植物的δ13C值从-28.9‰变化到-47.0‰,C4植物的δ13C值从-15.1‰变化到-43.8‰,C4植物在超高CO2浓度下的δ13C变化远大于C3植物,且与CO2浓度有较强相关性(R2≥0.6343);当二氧化碳浓度大于20000μmol·mol-1时C4植物δ13C值与正常大气环境C4植物δ13C值差异显著,利用C4植物δ13C可有效识别CO2的泄漏。展开更多
Harnessing natural variation in photosynthetic capacity is a promising route toward yield increases, but physiological phenotyping is still too laborious for large-scale genetic screens. Here, we evaluate the potentia...Harnessing natural variation in photosynthetic capacity is a promising route toward yield increases, but physiological phenotyping is still too laborious for large-scale genetic screens. Here, we evaluate the potential of leaf reflectance spectroscopy to predict parameters of photosynthetic capacity in Brassica oleracea and Zea mays, a C3 and a C4 crop, respectively. To this end, we systematically evaluated properties of reflectance spectra and found that they are surprisingly similar over a wide range of species. We assessed the performance of a wide range of machine learning methods and selected recursive feature elimination on untransformed spectra followed by partial least squares regression as the preferred algorithm that yielded the highest predictive power. Learning curves of this algorithm suggest optimal species-specific sample sizes. Using the Brassica relative Moricandia, we evaluated the model transferability between spe- cies and found that cross-species performance cannot be predicted from phylogenetic proximity. The final intra-species models predict crop photosynthetic capacity with high accuracy. Based on the estimated model accuracy, we simulated the use of the models in selective breeding experiments, and showed that high-throughput photosynthetic phenotyping using our method has the potential to greatly improve breeding success. Our results indicate that leaf reflectance phenotyping is an efficient method for improving crop photosynthetic capacity.展开更多
文摘为明晰植被冠层叶绿素荧光遥感信息与总初级生产力(Gross Primary Productivity,GPP)间关系,以提高GPP估算精度,该文以典型C3(冬小麦)和C4(夏玉米)作物为例,利用不同传感器采集高频率光谱数据,提取了红光区和远红光区作物冠层日光诱导叶绿素荧光(Sun-induced chlorophyll Fluorescence,SIF)遥感信息。结合通量观测数据,分析了SIF与GPP的日变化特征,并探讨了基于SIF估算作物GPP的能力和差异性。结果表明:1)C3、C4作物的GPP日变化特征差异明显,前者呈“双峰”特征,后者呈“单峰”特征;而SIF760和SIF687均呈现明显的“单峰”特征,即早晚低、午间高;2)不同类型传感器对SIF数值大小的影响强于对其日变化特征的影响,同时,低光谱分辨率传感器对SIF687具有明显的高估现象,且对C3作物的高估强于C4作物;3)C3、C4作物SIF760和SIF687均与冠层吸收光合有效辐射(Absorbed Photosynthetic Active Radiation,APAR)呈显著的线性正相关关系( R 2>0.8),可以直接用于APAR产品的反演;4)针对远红光区SIF,单日观测数据分析结果表明,C3、C4作物SIF760与GPP呈显著的线性正相关关系( R 2>0.6),而基于多日观测数据构建的非线性对数关系模型优于线性关系模型;针对红光区SIF,无论是基于单日还是多日观测数据,C3、C4作物均适宜采用一种非线性对数关系模型来估算GPP( R 2 >0.7),且模型更为稳健。
文摘二氧化碳捕集与封存(CO2 capture and storage,CCS)是全球CO2减排最重要技术战略,但CCS相关项目存在CO2泄漏的风险不容忽视,及时有效的识别与监测项目区CO2泄漏至关重要。文中以7种典型C3、C4植物为研究对象,通过模拟地质封存CO2泄漏产生的超高CO2浓度对植物稳定碳同位素组成δ13C的影响,分析C3、C4植物δ13C值与超高CO2浓度之间的关系,试图利用植物δ13C分析识别CO2泄漏。结果表明:C3、C4植物的δ13C值能够迅速响应CO2浓度的变化,均呈现随着CO2浓度的增加而先迅速下降后缓慢稳定的态势,其中C3、C4植物δ13C值分别在CO2浓度为10000μmol·mol-1、20000μmol·mol-1时降低显著,C3植物的δ13C值从-28.9‰变化到-47.0‰,C4植物的δ13C值从-15.1‰变化到-43.8‰,C4植物在超高CO2浓度下的δ13C变化远大于C3植物,且与CO2浓度有较强相关性(R2≥0.6343);当二氧化碳浓度大于20000μmol·mol-1时C4植物δ13C值与正常大气环境C4植物δ13C值差异显著,利用C4植物δ13C可有效识别CO2的泄漏。
文摘Harnessing natural variation in photosynthetic capacity is a promising route toward yield increases, but physiological phenotyping is still too laborious for large-scale genetic screens. Here, we evaluate the potential of leaf reflectance spectroscopy to predict parameters of photosynthetic capacity in Brassica oleracea and Zea mays, a C3 and a C4 crop, respectively. To this end, we systematically evaluated properties of reflectance spectra and found that they are surprisingly similar over a wide range of species. We assessed the performance of a wide range of machine learning methods and selected recursive feature elimination on untransformed spectra followed by partial least squares regression as the preferred algorithm that yielded the highest predictive power. Learning curves of this algorithm suggest optimal species-specific sample sizes. Using the Brassica relative Moricandia, we evaluated the model transferability between spe- cies and found that cross-species performance cannot be predicted from phylogenetic proximity. The final intra-species models predict crop photosynthetic capacity with high accuracy. Based on the estimated model accuracy, we simulated the use of the models in selective breeding experiments, and showed that high-throughput photosynthetic phenotyping using our method has the potential to greatly improve breeding success. Our results indicate that leaf reflectance phenotyping is an efficient method for improving crop photosynthetic capacity.
