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陆地生态系统总初级生产力(GPP,Gross Primary Productivity)是陆地生态系统碳循环的重要分量,提高其估算精度具有重要的科学意义。由于受多种因子的影响,GPP的时空变异明显,其估算结果存在较大的不确定性。日光诱导叶绿素荧光(SIF,Sun-Induced Chlorophyll Fluorescence)与GPP密切相关,近年来被应用于估算区域和全球GPP,但其在中国生态系统的适用性尚不清楚。以中国8个典型植被生态系统为研究对象,驱动两叶光能利用率模型(TL-LUE,TwoLeaf Light Use Efficiency Model)模拟以站点为中心0.5°×0.5°区域内的月GPP,验证SIF估算GPP的能力。结果表明,SIF具有监测中国典型植被生态系统GPP的能力,月SIF与TL-LUE模拟的月GPP之间显著相关,其中5个生态系统中两者的R2高于0.8,最高达到0.91,GPP与SIF变化的斜率随生态系统类型变化。模拟的GPP与SIF遥感数据的季节变化特征相同,两者之间的一致性在生长季节好于非生长季节;SIF能更好地监测农田GPP的季节变化。
Gross Primary Productivity (GPP) of terrestrial ecosystems is an important component of the carbon cycle of terrestrial ecosystems. It is of important scientific significance to improve its estimation accuracy. Due to the influence of many factors, the spatiotemporal variation of GPP is obvious, and the estimation results have great uncertainty. Sun-Induced Chlorophyll Fluorescence (SIF) is closely related to GPP and has been used to estimate regional and global GPP in recent years. However, its applicability in Chinese ecosystems is not clear. Eight typical vegetation ecosystems in China were selected as the research object to simulate the monthly GPP in the region of 0.5 ° × 0.5 ° using the two-leaf light-use efficiency model (TL-LUE) to verify the SIF estimation GPP’s ability. The results showed that SIF had the ability to monitor the GPP of typical vegetation ecosystems in China. There was a significant correlation between monthly SIF and monthly GPP simulated by TL-LUE. Among them, R2 of two of the five ecosystems was higher than 0.8 and the highest reached 0.91, The slope of SIF varies with ecosystem type. Simulated GPP and SIF remote sensing data have the same seasonal variation characteristics, and the consistency between the two is better than that in the non-growing season in the growth season; SIF can better monitor the seasonal variation of the GPP in the farmland.