基于叶面积指数估算植被总初级生产力

Estimated gross primary productivity based on global leaf area index

长时间序列的陆地碳通量数据在全球生态环境变化研究中具有重要意义。采用MODIS GPP(Gross Primary Productivity)算法,基于GIMMS LAI3g,MODIS15和Improved-MODIS15三种叶面积指数(LAI),估算了全球2000至2010年的植被总初级生产力(GPP)。该估算的GPP数值经过全球20个通量站点的验证,并结合MODIS17分析了它们在时空变化上的异同。结果表明:(1)4种GPP精度如下:GPP_(MOD17)>GPP_(impro_MOD15)>GPP_(LAI3g)>GPP_(MOD15)。(2)4种GPP整体上具有一致的季节波动,冬季和夏季整体好于春季和秋季。GPP_(LAI3g)的4个季节精度较相近,而GPP_(MOD17)除了春秋季外其它季节都较好。(3)GPP_(LAI3g)在中等GPP值分布区的估值相对较高,其全球总GPP大体为(117±1.5)Pg C/a,GPP_(MOD17)和GPP_(impro_MOD15)相近且都低于该值。(4)GPP_(LAI3g)和GPP_(impro_MOD15)在大约63.29%的陆面上呈显著(P<0.05)的正相关关系,它们和GPP_(MOD17)在LAI不确定性小的地区呈显著的正相关关系。GPP_(LAI3g)和GPP_(MOD15)正相关分布面积占比为40.61%。

Long-term,series gross primary production（ GPP） data are important in carbon cycle research. The MOD17 algorithm,which is based on the radiation conversion efficiency concept of Monteith,has been used widely for estimating GPP. However,MODIS17 only provides the global GPP since 2000 due to the short time series of the MODIS leaf area index（ MODIS15）. LAI plays an important role in calculating the fraction of photosynthetically active radiation absorbed by plants,and errors in LAI will be propagated to GPP estimates. Three global LAI are available： MODIS15,improvedMODIS15,and Global Inventory Modeling and Mapping Studies（ GIMMAS） LAI3 g. The improved-MODIS15 LAI is more realistic and smoother than the MODIS15 because it uses quality control information and an integrated two-step method. The GIMMAS LAI3 g is a new 30-year time series global LAI（ 1981—2011）. In this study,we compared the global GPP estimates during 2000—2010 by using the MODIS GPP algorithm based on the three global LAI. The global GPP estimates based on GIMMAS LAI3 g,MODIS15,and improved-MODIS15 are referred to as GPP_（LAI3 g）,GPP_（MOD15）,and GPP_（impro_MOD15）,respectively. We also compared remote sensing-based GPP estimates with eddy covariance（ EC） flux tower-measured GPP.The representative EC flux towers were selected by considering major typical plant functional types. We also analyzed spatiotemporal patterns and their correlations with the three GPP estimates as well as the MODIS17. The results showed the following.（ 1） The overall accuracy of the four global GPP estimates may be ranked as GPP_（MOD17） GPP_（impro_MOD15） GPPLAI3gGPP_（MOD15）.（ 2） The four GPP estimates had high seasonal dynamic consistency. The estimated GPP values were closer to the flux tower-measured GPP in summer and winter than in spring and autumn. The accuracy of GPP_（LAI3 g）was consistent for all seasons; GPP_（MOD17） was more accurate than GPP_（LAI3 g）for all seasons except for spring and fall.（ 3） GPP_（LAI3 g）overestimated GPP for areas with moderate GPP values,i.e.,the global total GPP value estimated by GPP_（LAI3 g）was approximately（ 117±1.5） Pg C / a,which was higher than GPP_（MOD17） and GPP_（impro_MOD15）.（ 4） The annual GPP values estimated by GPP_（LAI3 g）were positively correlated with those by GPP_（impro_MOD15）,and approximately 63.29% of the global vegetated area had a significant correlation（ P〈0.05）. The GPP_（LAI3 g）values were positively correlated with GPP_（MOD15） in regions with low LAI uncertainty.Approximately 40.61% of the global vegetated area was significantly correlated with GPP_（LAI3 g）and GPP_（MOD15）. There were also several negatively correlated areas,which may have been related to uncertainties and errors in the LAI and meteorological data. Based on our comparison,we conclude that GIMMS LAI3 g is an effective dataset for GPP simulation at the global scale,and thus,the 30-year long-term GPP series estimated using the GIMMS LAI3 g and MODIS GPP algorithms are reasonably acceptable.