涡度相关法作为国际上公认的碳通量测定的标准方法, 在农田生态系统碳通量研究领域具有广阔的空间。本研究旨在总结涡度相关法的农田生态系统碳通量最新研究成果, 为涡度相关法与农田生态系统碳通量研究提供参考。文章综述了国内外基于涡度相关技术的农田生态系统碳通量研究现状, 重点总结了其在时间变化、驱动因子、生产力模型、数据处理等方面的最新研究成果。经了解发现涡度相关技术对农田生态系统碳通量的时间变化特征研究较多, 且众多研究结果表明在单一种植模式下农田碳通量的日变化和季节变化呈显著单峰“U”型趋势, 在多熟种植模式下季变化呈“W”型, 但对种植模式的碳通量研究缺乏区域代表性; 同时驱动因子研究集中在温度、光照、水分等环境因素对农田碳通量的影响方面, 对环境因子与农艺措施之间的关系研究较少; 且对于通量夜间数据的处理和无效、缺失数据的剔除与插补缺乏统一的标准。因此本文认为区域典型种植模式的长期定位监测、多因子协同分析、数据质量监控等方面具有较大研究空间。

As an internationally recognized standard method for carbon flux determination, the eddy covariance method has a broad space in the field of agro-ecosystem carbon flux studies. To provide references for eddy covariance method and carbon flux research in agro-ecosystem, the study aims to summarize the latest research results of agro-ecosystem carbon flux based on eddy covariance method. This paper reviews the research status of carbon flux over agro-ecosystem based on the eddy covariance method at home and abroad, and also put emphasis on the latest research results on the temporal variation characteristics, driving factors, productivity models and data processing. Through literature research, it is found that the eddy covariance method has many researches on the temporal variation characteristics of carbon flux in agro-ecosystems, and many research results show that the daily and seasonal changes of farmland carbon flux show a single-peak “U” trend in single planting mode, while in the multi-crop planting mode, the seasonal change is “W” type. But the carbon flux research on planting mode lacks regional representation. At the same time, the driving factor research focuses on the influence of environmental factors such as temperature, light and moisture on farmland carbon flux. Less research on the relationship between environmental factors and agronomic measures was done. And there is a lack of uniform standards for the processing of flux night data and elimination or interpolation of the invalidation and missing data. Therefore, this paper points out that the long-term continuous monitoring at the typical planting mode, multi-factor collaborative analysis, data quality control should be the important research directions.