青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production,GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以...青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production,GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以及预测其未来的变化趋势。本研究使用6种常见的遥感GPP产品(GLASS、MODIS MOD17A2、FLUXCOM、VODCA2、改进的EC-LUE数据及VPM数据),结合涡度协方差通量观测数据(海北灌丛、海北湿地和当雄)进行验证后,对青藏高原2001—2015年生态系统GPP空间分布格局及时间变化趋势进行分析。结果表明:不同生态遥感产品得到的青藏高原年平均GPP、区域年均GPP时空分布格局与变化趋势存在较大差异,6套产品得到的2001—2015年变化趋势分别-0.77 g C·m^(-2)·a^(-1)(GLASS)、3.63 g C·m^(-2)·a^(-1)(MOD17A2)、-1.21 g C·m^(-2)·a^(-1)(FLUXCOM)、1.53 g C·m^(-2)·a^(-1)(VODCA2)、4.73 g C·m^(-2)·a^(-1)(VPM)和-0.81 g C·m^(-2)·a^(-1)(改进的EC-LUE);在空间分布上多年平均GPP总体呈现“东南高、西北低”的特点,区域差异较大;在青藏高原生态系统中,GLASS产品区域平均年GPP最高(827.78 Tg C·a^(-1)),MOD17A2产品最低(484.04 Tg C·a^(-1)),2001—2015年青藏高原生态系统GPP变化程度分布区域基本相同,东南部最剧烈,而西部最为稳定;经过站点数据验证,MOD17A2在8天尺度上结果相对更好,而FLUXCOM数据集在月尺度上结果相对更好,结合在区域尺度上的表现,MOD17A2数据集更加适用于青藏高原地区。展开更多
The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern.However,there is still a lack of a high-resolution GPP dataset for Qinghai Province.To address ...The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern.However,there is still a lack of a high-resolution GPP dataset for Qinghai Province.To address this issue,we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE,and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model.We compared our GPP dataset with flux observations to verify its accuracy.The results showed that our GPP dataset had a high correlation with the flux tower observations,with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site,respectively,and each site had an RMSE of 11.960 gC·m−2·16d−1 and 12.986 gC·m−2·16d−1,respectively.There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types,with the average correlation coefficient ranging from 0.431 to 0.943.By comparing with the flux observations and the related analysis,we demonstrated that our GPP dataset features better accuracy,higher spatial resolution,and more temporal coverage than mainstream GPP datasets.This study offers the first long-term highresolution GPP dataset for Qinghai Province,and we believe that this dataset has important implications for ecological management and climate research.展开更多
文摘青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production,GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以及预测其未来的变化趋势。本研究使用6种常见的遥感GPP产品(GLASS、MODIS MOD17A2、FLUXCOM、VODCA2、改进的EC-LUE数据及VPM数据),结合涡度协方差通量观测数据(海北灌丛、海北湿地和当雄)进行验证后,对青藏高原2001—2015年生态系统GPP空间分布格局及时间变化趋势进行分析。结果表明:不同生态遥感产品得到的青藏高原年平均GPP、区域年均GPP时空分布格局与变化趋势存在较大差异,6套产品得到的2001—2015年变化趋势分别-0.77 g C·m^(-2)·a^(-1)(GLASS)、3.63 g C·m^(-2)·a^(-1)(MOD17A2)、-1.21 g C·m^(-2)·a^(-1)(FLUXCOM)、1.53 g C·m^(-2)·a^(-1)(VODCA2)、4.73 g C·m^(-2)·a^(-1)(VPM)和-0.81 g C·m^(-2)·a^(-1)(改进的EC-LUE);在空间分布上多年平均GPP总体呈现“东南高、西北低”的特点,区域差异较大;在青藏高原生态系统中,GLASS产品区域平均年GPP最高(827.78 Tg C·a^(-1)),MOD17A2产品最低(484.04 Tg C·a^(-1)),2001—2015年青藏高原生态系统GPP变化程度分布区域基本相同,东南部最剧烈,而西部最为稳定;经过站点数据验证,MOD17A2在8天尺度上结果相对更好,而FLUXCOM数据集在月尺度上结果相对更好,结合在区域尺度上的表现,MOD17A2数据集更加适用于青藏高原地区。
基金supported by the[National Natural Science Foundation of China]under Grant[U20A20981][Natural Science Foundation of Jiangsu Province]under Grant[BK20190785].
文摘The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern.However,there is still a lack of a high-resolution GPP dataset for Qinghai Province.To address this issue,we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE,and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model.We compared our GPP dataset with flux observations to verify its accuracy.The results showed that our GPP dataset had a high correlation with the flux tower observations,with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site,respectively,and each site had an RMSE of 11.960 gC·m−2·16d−1 and 12.986 gC·m−2·16d−1,respectively.There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types,with the average correlation coefficient ranging from 0.431 to 0.943.By comparing with the flux observations and the related analysis,we demonstrated that our GPP dataset features better accuracy,higher spatial resolution,and more temporal coverage than mainstream GPP datasets.This study offers the first long-term highresolution GPP dataset for Qinghai Province,and we believe that this dataset has important implications for ecological management and climate research.
