Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the glo...Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.展开更多
Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in th...Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.展开更多
Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun s...Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.展开更多
基金Under the auspices of Asia Pacific Network for Global Change Research(APN)Global Change Fund Project(No.ARCP2015-03CMY-Li)+2 种基金National Natural Science Foundation of China(No.41271361,41501575)National Key Research and Development Project(No.2018YFD0800201)Key Project of Chinese National Programs for Fundamental Research and Development(No.2010CB950702)
文摘Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030204)the West Light Program of Chinese Academy of Sciences(2015-XBQN-B-17)
文摘Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.
基金Supported by the National Key Research and Development Program of China(2018YFC1506500)Natural Science Program of China(U2142212)National Natural Science Foundation of China(41871028).
文摘Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.
