Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical,physiological and structural quantities across spatial scales.The rice leaf NDVI(normalized dif...Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical,physiological and structural quantities across spatial scales.The rice leaf NDVI(normalized difference vegetation index)has been developed and applied in monitoring rice growth,yield prediction and disease status to guide agricultural management practices.This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI.The test site was a japonica rice experiment located in the eastern city of Shenyang,Liaoning Province,China.This paper describes(1)the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy(R2=0.782–0.903),and(2)how the key point of the rice growth period based on these models was determined.The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented.The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data.This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future.展开更多
At the hypothesis of big leaf,an ecosystem photosynthesis-transpiration coupling cycle model was established by the scaled SMPT-SB model from single leaf to canopy,and model parameterization methods were discussed.Thr...At the hypothesis of big leaf,an ecosystem photosynthesis-transpiration coupling cycle model was established by the scaled SMPT-SB model from single leaf to canopy,and model parameterization methods were discussed.Through simulating the canopy light distribution,canopy internal conductance to CO_(2) can be scaled from single leaf to canopy by integrating to canopy using the relationship between single internal conductance and photosynthetic photon flux density.Using the data observed by eddy covariance method from the Changbai Mountains site of ChinaFLUX,the application of the model at the canopy scale was examined.Under no water stress,the simulated net ecosystem photosynthesis rate fitted with the observed data very well,the slope and R2 of the line regression equation of the observed and simulated values were 0.7977 and 0.8892,respectively(n=752),and average absolute error was 3.78μmol CO_(2) m-2s-1;the slope,R2 and average absolute error of transpiration rate were 0.7314,0.4355 and 1.60mmol H2O m-2 s-1,respectively(n=752).The relationship between canopy photosynthesis,transpiration and external environmental conditions was discussed by treating the canopy as a whole and neglecting the comprehensive feedback mechanism within canopy,and it was noted that the precipitation course affected the transpiration rate simulation badly.Compared to the models based on eco-physiological processes,the SMPT-SB model was simple and easy to be used.And it can be used as a basic carbon and water coupling model of soil-plant-atmosphere continuum.展开更多
Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evalua...Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.展开更多
Solar-induced chlorophyll fluorescence(SIF)has shown promise in estimating gross primary production(GPP);however,there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of...Solar-induced chlorophyll fluorescence(SIF)has shown promise in estimating gross primary production(GPP);however,there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis.This study introduces a new global 0.05°SIF-based GPP dataset(CMLR GPP,based on Canopy-scale Mechanistic Light Reaction model)using TROPOMI observations.A modified mechanistic light response model was employed at the canopy scale to generate this dataset.The canopy qL(opened fraction of photosynthesis II reaction centers),required by the CMLR model,was parameterized using a random forest model.The CMLR GPP estimates showed a strong correlation with tower-based GPP(R^(2)=0.72)in the validation dataset,and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator(BEPS)GPP,FluxSat GPP,and GOSIF(global,OCO-2-based SIF product)GPP at a global scale.The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index,vapor pressure deficit,and temperature conditions,as well as different plant functional types and most months of the year.In conclusion,CMLR GPP is a novel global GPP dataset based on mechanistic frameworks,whose availability is expected to contribute to future research in ecological and geobiological regions.展开更多
基金grant from the national key research and development plan of China(2016YFD0200600)the EcoLab laboratory for allowing us to use their instruments.
文摘Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical,physiological and structural quantities across spatial scales.The rice leaf NDVI(normalized difference vegetation index)has been developed and applied in monitoring rice growth,yield prediction and disease status to guide agricultural management practices.This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI.The test site was a japonica rice experiment located in the eastern city of Shenyang,Liaoning Province,China.This paper describes(1)the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy(R2=0.782–0.903),and(2)how the key point of the rice growth period based on these models was determined.The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented.The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data.This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future.
基金This work was supported by the National Natural Science Foundation of China for Distinguished Young Scholars(Grant No.30225012)the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KZCX1-SW-01-01A)the Chinese National Key Basic Research Program of the Ministry of Science and Technology of China(Grant No.2002CB412501).
文摘At the hypothesis of big leaf,an ecosystem photosynthesis-transpiration coupling cycle model was established by the scaled SMPT-SB model from single leaf to canopy,and model parameterization methods were discussed.Through simulating the canopy light distribution,canopy internal conductance to CO_(2) can be scaled from single leaf to canopy by integrating to canopy using the relationship between single internal conductance and photosynthetic photon flux density.Using the data observed by eddy covariance method from the Changbai Mountains site of ChinaFLUX,the application of the model at the canopy scale was examined.Under no water stress,the simulated net ecosystem photosynthesis rate fitted with the observed data very well,the slope and R2 of the line regression equation of the observed and simulated values were 0.7977 and 0.8892,respectively(n=752),and average absolute error was 3.78μmol CO_(2) m-2s-1;the slope,R2 and average absolute error of transpiration rate were 0.7314,0.4355 and 1.60mmol H2O m-2 s-1,respectively(n=752).The relationship between canopy photosynthesis,transpiration and external environmental conditions was discussed by treating the canopy as a whole and neglecting the comprehensive feedback mechanism within canopy,and it was noted that the precipitation course affected the transpiration rate simulation badly.Compared to the models based on eco-physiological processes,the SMPT-SB model was simple and easy to be used.And it can be used as a basic carbon and water coupling model of soil-plant-atmosphere continuum.
基金funded by Key Research Program of Frontier Sciences,CAS(ZDBS-LY-DQC012)the National Natural Science Foundation of China(41971321,41830108)+2 种基金XPCC Science and Technology Project(2022CB002-01)Open Fund of Key Laboratory of Oasis Eco-agriculture,XPCC(201801 and 202003)supported by Youth Innovation Promotion Association,CAS(Y2021047)。
文摘Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.
基金supported by the National Natural Science Foundation of China(grant numbers 41825002 and 42071310)supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy-EXC 2070-390732324.
文摘Solar-induced chlorophyll fluorescence(SIF)has shown promise in estimating gross primary production(GPP);however,there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis.This study introduces a new global 0.05°SIF-based GPP dataset(CMLR GPP,based on Canopy-scale Mechanistic Light Reaction model)using TROPOMI observations.A modified mechanistic light response model was employed at the canopy scale to generate this dataset.The canopy qL(opened fraction of photosynthesis II reaction centers),required by the CMLR model,was parameterized using a random forest model.The CMLR GPP estimates showed a strong correlation with tower-based GPP(R^(2)=0.72)in the validation dataset,and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator(BEPS)GPP,FluxSat GPP,and GOSIF(global,OCO-2-based SIF product)GPP at a global scale.The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index,vapor pressure deficit,and temperature conditions,as well as different plant functional types and most months of the year.In conclusion,CMLR GPP is a novel global GPP dataset based on mechanistic frameworks,whose availability is expected to contribute to future research in ecological and geobiological regions.
