二氧化氮(NO2)是一种对植物有害的污染物,它通过破坏植物细胞直接影响植物的生长,并通过促进臭氧的形成间接影响植物的生长。尽管田间试验已经证实空气污染会显著影响作物生长,但由于观测数据有限,二氧化氮对不同类型植被生产力的大规...二氧化氮(NO2)是一种对植物有害的污染物,它通过破坏植物细胞直接影响植物的生长,并通过促进臭氧的形成间接影响植物的生长。尽管田间试验已经证实空气污染会显著影响作物生长,但由于观测数据有限,二氧化氮对不同类型植被生产力的大规模影响仍然知之甚少。在本研究中,我们采用创新方法,综合卫星观测数据,研究氮氧化物对中国植被生产力的影响。研究结果表明,NO2浓度与植被生产力之间存在较强的负相关。不同类型的植被对NO2的敏感性差异较大。其中,草原对NO2的敏感性最高,而常绿针叶林和灌木林对NO2的敏感性最低。当NO2浓度降低至第5百分位数时,农田、草原、灌木林、混交林、落叶阔叶林和常绿针叶林的生产力预计分别提高27.73%、14.71%、12.50%、4.28%、3.58%和3.21%。这些结果与野外实验的结果一致,加强了我们方法的有效性。该研究凸显了卫星观测在量化区域范围内空气污染对植被生长影响方面的潜力。Nitrogen dioxide (NO2) is a phytotoxic pollutant that affects plant growth both directly, by damaging vegetation cells, and indirectly, by contributing to ozone formation. While field experiments have demonstrated the significant impact of air pollution on crop growth, the large-scale effects of NO₂ on vegetation productivity across diverse plant species remain poorly understood due to limited observational data. In this study, we investigated the influence of NO₂ on vegetation productivity across China using an innovative approach that integrates satellite-based observations. Our findings revealed a strong negative correlation between NO₂ concentrations and vegetation productivity, indicating that elevated NO₂ levels are associated with reduced plant growth. The sensitivity of vegetation types to NO₂ varied considerably, with savannas being the most sensitive and evergreen needleleaf forests and shrublands the least. Specifically, reductions in NO₂ concentrations to the 5th percentile were estimated to increase productivity by 27.73% in croplands, 14.71% in savannas, 12.50% in shrublands, 4.28% in mixed forests, 3.58% in deciduous broadleaf forests, and 3.21% in evergreen needleleaf forests. These results are consistent with those from field experiments, reinforcing the validity of our approach. This study highlights the potential of satellite observations for quantifying the effects of air pollution on vegetation growth at regional scales.展开更多
Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric ...Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.展开更多
Background Machine learning is widely used to estimate gross primary productivity(GPP)on large scales.Usually,models are trained at site level using eddy flux observations and remote sensing based vegetation indices.H...Background Machine learning is widely used to estimate gross primary productivity(GPP)on large scales.Usually,models are trained at site level using eddy flux observations and remote sensing based vegetation indices.However,how to more effectively utilize the gradually increasing site observations and select different vegetation indices to improve large-scale estimations remains to be further studied,as there is currently no widely recognized optimal solution.In recent years,flux observations in China have expanded rapidly,and a new batch of publicly shared data has provided opportunities for further research.Results We tested the random forest model at the site scale and found that the model which accounts for vegetation types,using data from FLUXNET2015 and China FLUX sites,was the best for estimating GPP in China(R^(2)=0.73).However,models based on different vegetation indices(leaf area index(LAI)and near-infrared reflectance of vegetation(NIRv))showed no major difference in accuracy.Using these indices separately,we simulated monthly GPP for China from 2001 to 2022 at a 0.05°resolution.The datasets were consistent in annual totals and spatial distribution between 2001 and 2018,reporting totals of 7.52 Pg C yr^(–1).However,significant differences were found in spatiotemporal trends,particularly in southern China,where the linear regression coefficients were 0.04 Pg C yr^(–1) and 0.07 Pg C yr^(–1).Compared to other GPP datasets,our results showed slightly higher totals and trends,but they were still within a reasonable range.Conclusions The study confirms the effectiveness of differentiating between different vegetation types and adding site observations for increasing accuracy of GPP estimates.However,the difference of vegetation index does not affect the accuracy of the model,and more influences are mainly reflected in the regional simulation.These findings will help improve GPP estimates and further highlight sources of uncertainty in regional GPP simulations(input vegetation index datasets).展开更多
Background:Vegetation indices(VIs)by remote sensing are widely used as simple proxies of the gross primary production(GPP)of vegetation,but their performances in capturing the inter-annual variation(IAV)in GPP remain ...Background:Vegetation indices(VIs)by remote sensing are widely used as simple proxies of the gross primary production(GPP)of vegetation,but their performances in capturing the inter-annual variation(IAV)in GPP remain uncertain.Methods:We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China.The VIs assessed included the normalized difference vegetation index(NDVI),the enhanced vegetation index(EVI),and the near-infrared reflectance of vegetation(NIRv)obtained from tower-radiometers(broadband)and the Moderate Resolution Imaging Spectroradiometer(MODIS),respectively.Results:We found that 25%–35%amplitude of the broadband EVI tracked the start of growing season derived by GPP(R^(2):0.56–0.60,bias<4 d),while 45%(or 50%)amplitudes of broadband(or MODIS)NDVI represented the end of growing season estimated by GPP(R^(2):0.58–0.67,bias<3 d).However,all the VIs failed to characterize the summer peaks of GPP.The growing-season integrals but not averaged values of the broadband NDVI,MODIS NIRv and EVI were robust surrogates of the IAV in GPP(R^(2):0.40–0.67).Conclusion:These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak,while the integral VIs have the potential to mirror the IAV in GPP.展开更多
文摘二氧化氮(NO2)是一种对植物有害的污染物,它通过破坏植物细胞直接影响植物的生长,并通过促进臭氧的形成间接影响植物的生长。尽管田间试验已经证实空气污染会显著影响作物生长,但由于观测数据有限,二氧化氮对不同类型植被生产力的大规模影响仍然知之甚少。在本研究中,我们采用创新方法,综合卫星观测数据,研究氮氧化物对中国植被生产力的影响。研究结果表明,NO2浓度与植被生产力之间存在较强的负相关。不同类型的植被对NO2的敏感性差异较大。其中,草原对NO2的敏感性最高,而常绿针叶林和灌木林对NO2的敏感性最低。当NO2浓度降低至第5百分位数时,农田、草原、灌木林、混交林、落叶阔叶林和常绿针叶林的生产力预计分别提高27.73%、14.71%、12.50%、4.28%、3.58%和3.21%。这些结果与野外实验的结果一致,加强了我们方法的有效性。该研究凸显了卫星观测在量化区域范围内空气污染对植被生长影响方面的潜力。Nitrogen dioxide (NO2) is a phytotoxic pollutant that affects plant growth both directly, by damaging vegetation cells, and indirectly, by contributing to ozone formation. While field experiments have demonstrated the significant impact of air pollution on crop growth, the large-scale effects of NO₂ on vegetation productivity across diverse plant species remain poorly understood due to limited observational data. In this study, we investigated the influence of NO₂ on vegetation productivity across China using an innovative approach that integrates satellite-based observations. Our findings revealed a strong negative correlation between NO₂ concentrations and vegetation productivity, indicating that elevated NO₂ levels are associated with reduced plant growth. The sensitivity of vegetation types to NO₂ varied considerably, with savannas being the most sensitive and evergreen needleleaf forests and shrublands the least. Specifically, reductions in NO₂ concentrations to the 5th percentile were estimated to increase productivity by 27.73% in croplands, 14.71% in savannas, 12.50% in shrublands, 4.28% in mixed forests, 3.58% in deciduous broadleaf forests, and 3.21% in evergreen needleleaf forests. These results are consistent with those from field experiments, reinforcing the validity of our approach. This study highlights the potential of satellite observations for quantifying the effects of air pollution on vegetation growth at regional scales.
基金This research was supported by the National Natural Science Foundation of China(42161058).
文摘Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.
基金Qinghai Provincial Natural Science Foundation(2023-QLGKLYCZX-05,2023-QLGKLYCZX-010)the National Natural Science Foundation of China(42130506,42161144003,and 31570464)
文摘Background Machine learning is widely used to estimate gross primary productivity(GPP)on large scales.Usually,models are trained at site level using eddy flux observations and remote sensing based vegetation indices.However,how to more effectively utilize the gradually increasing site observations and select different vegetation indices to improve large-scale estimations remains to be further studied,as there is currently no widely recognized optimal solution.In recent years,flux observations in China have expanded rapidly,and a new batch of publicly shared data has provided opportunities for further research.Results We tested the random forest model at the site scale and found that the model which accounts for vegetation types,using data from FLUXNET2015 and China FLUX sites,was the best for estimating GPP in China(R^(2)=0.73).However,models based on different vegetation indices(leaf area index(LAI)and near-infrared reflectance of vegetation(NIRv))showed no major difference in accuracy.Using these indices separately,we simulated monthly GPP for China from 2001 to 2022 at a 0.05°resolution.The datasets were consistent in annual totals and spatial distribution between 2001 and 2018,reporting totals of 7.52 Pg C yr^(–1).However,significant differences were found in spatiotemporal trends,particularly in southern China,where the linear regression coefficients were 0.04 Pg C yr^(–1) and 0.07 Pg C yr^(–1).Compared to other GPP datasets,our results showed slightly higher totals and trends,but they were still within a reasonable range.Conclusions The study confirms the effectiveness of differentiating between different vegetation types and adding site observations for increasing accuracy of GPP estimates.However,the difference of vegetation index does not affect the accuracy of the model,and more influences are mainly reflected in the regional simulation.These findings will help improve GPP estimates and further highlight sources of uncertainty in regional GPP simulations(input vegetation index datasets).
基金supported by the National Science and Technology Support Program of China(2011BAD37B01)the Fundamental Research Funds for the Central Universities(2572019BA01 and 2572019CP07)the Program for Changjiang Scholars and Innovative Research Team in University(IRT_15R09).
文摘Background:Vegetation indices(VIs)by remote sensing are widely used as simple proxies of the gross primary production(GPP)of vegetation,but their performances in capturing the inter-annual variation(IAV)in GPP remain uncertain.Methods:We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China.The VIs assessed included the normalized difference vegetation index(NDVI),the enhanced vegetation index(EVI),and the near-infrared reflectance of vegetation(NIRv)obtained from tower-radiometers(broadband)and the Moderate Resolution Imaging Spectroradiometer(MODIS),respectively.Results:We found that 25%–35%amplitude of the broadband EVI tracked the start of growing season derived by GPP(R^(2):0.56–0.60,bias<4 d),while 45%(or 50%)amplitudes of broadband(or MODIS)NDVI represented the end of growing season estimated by GPP(R^(2):0.58–0.67,bias<3 d).However,all the VIs failed to characterize the summer peaks of GPP.The growing-season integrals but not averaged values of the broadband NDVI,MODIS NIRv and EVI were robust surrogates of the IAV in GPP(R^(2):0.40–0.67).Conclusion:These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak,while the integral VIs have the potential to mirror the IAV in GPP.
