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
利用EO S-M OD IS遥感数据,基于线性混合模型,提出了一种新的作物冠层温度反演方法。首先,利用EO S-M OD IS数据提取了陆地表面温度LST和植被指数NDV I。然后,假定地表只有植被和裸地两种组分,通过植被指数温度V I-T s方法来估算裸土的...利用EO S-M OD IS遥感数据,基于线性混合模型,提出了一种新的作物冠层温度反演方法。首先,利用EO S-M OD IS数据提取了陆地表面温度LST和植被指数NDV I。然后,假定地表只有植被和裸地两种组分,通过植被指数温度V I-T s方法来估算裸土的组分温度,作物冠层温度通过线性混合模型来求解。为了验证反演的地表温度和冠层温度的精度,把反演的地表温度与NA SA M OD IS地表温度产品进行差值运算,在差值图像中90%以上的像元灰度值分布在-1和1之间,像元灰度的平均值小于0.5;同时在河北固城农业气象试验站对冬小麦冠层温度进行同步观测,通过与反演的冠层温度进行比较,其误差在±1.5℃左右。结果表明,文中所提出的作物冠层温度反演方法精度较高,其结果能够满足有关作物生长模型以及土壤水分模型对输入参数的精度要求。展开更多
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.
文摘利用EO S-M OD IS遥感数据,基于线性混合模型,提出了一种新的作物冠层温度反演方法。首先,利用EO S-M OD IS数据提取了陆地表面温度LST和植被指数NDV I。然后,假定地表只有植被和裸地两种组分,通过植被指数温度V I-T s方法来估算裸土的组分温度,作物冠层温度通过线性混合模型来求解。为了验证反演的地表温度和冠层温度的精度,把反演的地表温度与NA SA M OD IS地表温度产品进行差值运算,在差值图像中90%以上的像元灰度值分布在-1和1之间,像元灰度的平均值小于0.5;同时在河北固城农业气象试验站对冬小麦冠层温度进行同步观测,通过与反演的冠层温度进行比较,其误差在±1.5℃左右。结果表明,文中所提出的作物冠层温度反演方法精度较高,其结果能够满足有关作物生长模型以及土壤水分模型对输入参数的精度要求。
