利用GLOPEM-CEVSA模型模拟并分析了中国东北地区2000-2008年植被净初级生产力(NPP)时空分布格局及其影响因素,并以4个森林生态站点(大兴安岭、老爷岭、凉水和长白山森林生态站)为例研究了东北地区森林NPP季节变化特征及其环境驱动.结果...利用GLOPEM-CEVSA模型模拟并分析了中国东北地区2000-2008年植被净初级生产力(NPP)时空分布格局及其影响因素,并以4个森林生态站点(大兴安岭、老爷岭、凉水和长白山森林生态站)为例研究了东北地区森林NPP季节变化特征及其环境驱动.结果表明:2000-2008年,东北地区植被年均NPP为445 g C.m-2.a-1;整个研究区沿长白山山脉到小兴安岭山脉地区以及三江平原部分地区的NPP最高,沿长白山山脉到小兴安岭山脉西侧的辽河平原、松嫩平原东部、三江平原和大兴安岭地区次之,西部稀疏草原和荒漠地区的NPP最低.东北地区森林生态系统年均NPP最高,其次为灌丛、农田和草地,荒漠最低.森林生态系统中,针阔混交林年均NPP最大(722 g C.m-2.a-1),落叶针叶林年均NPP最小(451 g C.m-2.a-1).研究期间,森林NPP无显著年际变化,其中2007、2008年较往年NPP大幅增加,很可能与该地区期间气温上升有关(较往年偏高1℃～2℃).东北地区森林自北向南生长季开始时间逐渐提前,生长季变长.展开更多
Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resour...Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.展开更多
文摘利用GLOPEM-CEVSA模型模拟并分析了中国东北地区2000-2008年植被净初级生产力(NPP)时空分布格局及其影响因素,并以4个森林生态站点(大兴安岭、老爷岭、凉水和长白山森林生态站)为例研究了东北地区森林NPP季节变化特征及其环境驱动.结果表明:2000-2008年,东北地区植被年均NPP为445 g C.m-2.a-1;整个研究区沿长白山山脉到小兴安岭山脉地区以及三江平原部分地区的NPP最高,沿长白山山脉到小兴安岭山脉西侧的辽河平原、松嫩平原东部、三江平原和大兴安岭地区次之,西部稀疏草原和荒漠地区的NPP最低.东北地区森林生态系统年均NPP最高,其次为灌丛、农田和草地,荒漠最低.森林生态系统中,针阔混交林年均NPP最大(722 g C.m-2.a-1),落叶针叶林年均NPP最小(451 g C.m-2.a-1).研究期间,森林NPP无显著年际变化,其中2007、2008年较往年NPP大幅增加,很可能与该地区期间气温上升有关(较往年偏高1℃～2℃).东北地区森林自北向南生长季开始时间逐渐提前,生长季变长.
基金funded by the Strategic Priority Research Program for Space Sciences(Grant No.XDA04061200)of the Chinese Academy of SciencesNational Basic Research Program of China(Grant No.2015CB953701)
文摘Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.
