摘要
为探讨华北平原冬小麦农田生态系统的呼吸作用,利用2013—2017年河南省郑州市农业气象试验站的碳通量观测数据分析了冬小麦生长季的呼吸作用,基于MODIS遥感数据讨论了该生态系统的环境因子并模拟了冬小麦的呼吸作用。结果表明:冬小麦呼吸作用(观测值)和增强型植被指数(EVI)、陆地表面水分指数(LSWI)及地表温度(LST)存在较好的相关关系,可以利用基于MODIS数据的呼吸模型模拟郑州地区冬小麦呼吸作用,呼吸作用的模拟值白天要大于夜间,在2013—2017年冬小麦研究阶段(年积日DOY=73—169)模拟的呼吸作用总累积量分别为472.18、477.02、490.43、482.90、487.37 g·m^(–2)。研究表明,基于MODIS数据模拟郑州地区冬小麦农田生态系统呼吸作用是合理可行的,可为研究中国区域碳收支评估提供数据基础和技术理论支持。
In order to explore the respiration of winter wheat farmland ecosystem in the North China Plain,carbon flux observation data from Zhengzhou agricultural meteorological experimental station in Henan Province from 2013 to 2017 were used to analyze the respiration of winter wheat in Zhengzhou area during the growing season.Based on MODIS data,the environmental factors of this ecosystem were discussed and the respiration of winter wheat was simulated.The results showed that there was a good correlation between observed winter wheat respiration and Enhanced Vegetation Index(EVI),Land Surface Moisture Index(LSWI),Land Surface Temperature(LST).The respiration model based on MODIS data could be used for respiration simulation of winter wheat in Zhengzhou area.The simulated value of respiration was greater during the day than at night.In the research stage(day of year was 73 to 169)of winter wheat from 2013 to 2017,the total accumulation of simulated respiration was 472.18,477.02,490.43,482.90 and 487.37 g·m^(–2),respectively.The study shows that it is rational and feasible to simulate the respiration of winter wheat farmland ecosystem in Zhengzhou based on MODIS data,which can provide data basis and technical theoretical support for the study of regional carbon budget assessment in China.
作者
叶昊天
李颖
余珂
YE Haotian;LI Ying;YU Ke(Henan Key Laboratory of Agrometeorological Support and Applied Technique,CMA,Zhengzhou 450003,China;Henan Institute of Meteorological Sciences,Zhengzhou 450003,China;Zhengzhou Meteorological Bureau,Zhengzhou 450003,China)
出处
《生态科学》
CSCD
2023年第4期182-189,共8页
Ecological Science
基金
国家自然科学基金项目(41805090)
中国气象局河南省农业气象保障与应用技术重点实验室应用技术研究基金项目(KM202018)。