The radiation use efficiency(RUE)is one of the most important functional traits determining crop productivity.The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in...The radiation use efficiency(RUE)is one of the most important functional traits determining crop productivity.The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence.However,previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth,rarely considering the optimization of the leaf area distribution,especially for C4 crops.The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy.To test this hypothesis,various virtual maize canopies were generated by combining the leaf inclination angle,vertical leaf area distribution,and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model.We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE.The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios,particularly in dense canopies.This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.展开更多
In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy...In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.展开更多
Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledg...Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method.this study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence stage.In addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than fAPAR.the NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season.In a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.展开更多
基金supported by the National Key R&D Program of China(2022YFD2001003)the National Natural Science Foundation of China(32330075 and 32001420)+2 种基金the Science and Technology Innovation Special Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401)the Young Elite Scientist Sponsorship Program by BAST(no.BYESS2023204)the earmarked fund for CARS-02 and CARS-54.
文摘The radiation use efficiency(RUE)is one of the most important functional traits determining crop productivity.The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence.However,previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth,rarely considering the optimization of the leaf area distribution,especially for C4 crops.The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy.To test this hypothesis,various virtual maize canopies were generated by combining the leaf inclination angle,vertical leaf area distribution,and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model.We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE.The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios,particularly in dense canopies.This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.
基金This study was supported by the Chinese High Technology Research and Development Research Fund(2016YFD0300600-2016YFD0300606,2016YFD0300600-2016YFD0300610)NSFC program(31501219)+1 种基金the Fundamental Research Funds for the Central Universities(2018TC020,2018XD003)Industry Research Project(QingPu 2017-12).
文摘In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.
基金National Natural Science Foundation of China(41330531)the R&D Special Fund for Public Welfare Industry(Meteorology)Project(GYHY201106027)the State Key Development Program of Basic Research(2010CB951303).
文摘Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method.this study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence stage.In addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than fAPAR.the NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season.In a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.
