Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study ...Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R^(2)= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R^(2)= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale.展开更多
Most crops in northern China are irrigated,but the topography affects the water use,soil erosion,runoff and yields.Technologies for collecting high-resolution topographic data are essential for adequately assessing th...Most crops in northern China are irrigated,but the topography affects the water use,soil erosion,runoff and yields.Technologies for collecting high-resolution topographic data are essential for adequately assessing these effects.Ground surveys and techniques of light detection and ranging have good accuracy,but data acquisition can be time-consuming and expensive for large catchments.Recent rapid technological development has provided new,flexible,high-resolution methods for collecting topographic data,such as photogrammetry using unmanned aerial vehicles(UAVs).The accuracy of UAV photogrammetry for generating high-resolution Digital Elevation Model(DEM)and for determining the width of irrigation channels,however,has not been assessed.A fixed-wing UAV was used for collecting high-resolution(0.15 m)topographic data for the Hetao irrigation district,the third largest irrigation district in China.112 ground checkpoints(GCPs)were surveyed by using a real-time kinematic global positioning system to evaluate the accuracy of the DEMs and channel widths.A comparison of manually measured channel widths with the widths derived from the DEMs indicated that the DEM-derived widths had vertical and horizontal root mean square errors of 13.0 and 7.9 cm,respectively.UAV photogrammetric data can thus be used for land surveying,digital mapping,calculating channel capacity,monitoring crops,and predicting yields,with the advantages of economy,speed and ease.展开更多
A new sensitive fluorometric assay method for acetylcholinesterase （ACHE） and its inhibitor was developed us- ing a fluorescent dye, nile red （NR）. Due to the fluorescence resonance energy transfer between the NR ...A new sensitive fluorometric assay method for acetylcholinesterase （ACHE） and its inhibitor was developed us- ing a fluorescent dye, nile red （NR）. Due to the fluorescence resonance energy transfer between the NR and the gold nanoparticle （AuNPs）, the fluorescence was quenched. AChE can break down acetylthiocholine to produce a thiol-bearing compound, thiocholine. In the presence of thiocholine, the nile red is replaced from the AuNPs sur- faces and simultaneously transformed to a derivative of nile red. The fluorescence intensity of the derivative is much stronger than that of the native nile red with the same concentration and its maximum emission wavelength has a blue shift so that the sensor achieves a good signal-to-background ratio. In addition, when organophosphate pesticide （OPs） exists, the activity of AChE can be inhibited, the generation of thiocholine will be prevented and no fluorescence enhancement occurs. The results show that the method is sensitive to AChE and paraoxon with the de- tection limits of 0.2 mU/mL and 0.05 ng/mL, respectively.展开更多
During the era of global warming and highly urbanized development,extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and prope...During the era of global warming and highly urbanized development,extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property.Despite the vast development of atmospheric models,there still exist substantial numerical forecast biases objectively.To accurately predict extreme weather,severe air pollution,and abrupt climate change,numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution.Global integrated atmospheric simulation at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output(I/O)requirement.Through multi-dimension-parallelism structuring,aggressive and finer-grained optimizing,manual vectorizing,and parallelized I/O fragmenting,an integrated Atmospheric Model Across Scales(iAMAS)was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost.The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour(SDPH)with routine I/O,which enabled us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts.The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol feedbacks may significantly improve the global weather forecast.展开更多
基金funded by the National Natural Science Foundation of China (51979233)the Natural Science Basic Research Plan in Shaanxi Province of China (2022JQ-363)。
文摘Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R^(2)= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R^(2)= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale.
基金This work was financially supported by Major Project of National Key R&D Plan from the MOST of China(2017YFC0403203)National Natural Science Foundation of China(41771315,41301283,61402374,41371274,41301507)+2 种基金Natural Science Foundation of Shaanxi Province(2015JM4142)EU Horizon 2020 research and innovation programme(ISQAPER:635750)State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau(A314021402-1702).
文摘Most crops in northern China are irrigated,but the topography affects the water use,soil erosion,runoff and yields.Technologies for collecting high-resolution topographic data are essential for adequately assessing these effects.Ground surveys and techniques of light detection and ranging have good accuracy,but data acquisition can be time-consuming and expensive for large catchments.Recent rapid technological development has provided new,flexible,high-resolution methods for collecting topographic data,such as photogrammetry using unmanned aerial vehicles(UAVs).The accuracy of UAV photogrammetry for generating high-resolution Digital Elevation Model(DEM)and for determining the width of irrigation channels,however,has not been assessed.A fixed-wing UAV was used for collecting high-resolution(0.15 m)topographic data for the Hetao irrigation district,the third largest irrigation district in China.112 ground checkpoints(GCPs)were surveyed by using a real-time kinematic global positioning system to evaluate the accuracy of the DEMs and channel widths.A comparison of manually measured channel widths with the widths derived from the DEMs indicated that the DEM-derived widths had vertical and horizontal root mean square errors of 13.0 and 7.9 cm,respectively.UAV photogrammetric data can thus be used for land surveying,digital mapping,calculating channel capacity,monitoring crops,and predicting yields,with the advantages of economy,speed and ease.
文摘A new sensitive fluorometric assay method for acetylcholinesterase （ACHE） and its inhibitor was developed us- ing a fluorescent dye, nile red （NR）. Due to the fluorescence resonance energy transfer between the NR and the gold nanoparticle （AuNPs）, the fluorescence was quenched. AChE can break down acetylthiocholine to produce a thiol-bearing compound, thiocholine. In the presence of thiocholine, the nile red is replaced from the AuNPs sur- faces and simultaneously transformed to a derivative of nile red. The fluorescence intensity of the derivative is much stronger than that of the native nile red with the same concentration and its maximum emission wavelength has a blue shift so that the sensor achieves a good signal-to-background ratio. In addition, when organophosphate pesticide （OPs） exists, the activity of AChE can be inhibited, the generation of thiocholine will be prevented and no fluorescence enhancement occurs. The results show that the method is sensitive to AChE and paraoxon with the de- tection limits of 0.2 mU/mL and 0.05 ng/mL, respectively.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDB41000000)the Research Funds of the Double First-Class Initiative of University of Science and Technology of China(YD2080002007)the National Natural Science Foundation of China(91837310,42061134009,and 41775146)。
文摘During the era of global warming and highly urbanized development,extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property.Despite the vast development of atmospheric models,there still exist substantial numerical forecast biases objectively.To accurately predict extreme weather,severe air pollution,and abrupt climate change,numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution.Global integrated atmospheric simulation at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output(I/O)requirement.Through multi-dimension-parallelism structuring,aggressive and finer-grained optimizing,manual vectorizing,and parallelized I/O fragmenting,an integrated Atmospheric Model Across Scales(iAMAS)was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost.The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour(SDPH)with routine I/O,which enabled us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts.The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol feedbacks may significantly improve the global weather forecast.
