基于双叶光能利用率模型反演毛竹总初级生产力研究  

Inversion of Gross Primary Productivity of Phyllostachys edulis Forest based on A Two-leaf Light Use Efficiency Model

作　　者：王聚中 徐怀兴 胡璐璐 周忠生 唐燕 徐小军[1,2] WANG Ju-zhong;XU Huai-xing;HU Lu-lu;ZHOU Zhong-sheng;TANG Yan;XU Xiao-jun(College of Environment and Resource,Zhejiang A&F University,Hangzhou Zhejiang 311300,P.R.China;Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration,Zhejiang A&F University,Hangzhou Zhejiang 311300,P.R.China)

机构地区：[1]浙江农林大学环境与资源学院,浙江杭州311300 [2]浙江农林大学浙江省森林生态系统碳循环与固碳减排重点实验室,浙江杭州311300

出　　处：《西部林业科学》2023年第1期139-146,共8页Journal of West China Forestry Science

基　　金：国家自然科学基金项目(31870619)。

摘　　要：毛竹林是重要的碳库之一,准确估算毛竹总初级生产力(GPP)对评估毛竹在碳中和中的贡献有重要意义。本文利用2011—2014年安吉通量塔观测气象数据和遥感数据驱动双叶光能利用率DTEC(diffuse-fraction-based two-leaf terrestrial ecosystem carbon flux model,DTEC)模型模拟安吉毛竹8 d尺度GPP,估算毛竹GPP,有助了解毛竹林对气候变化以及人类活动的影响。本研究不仅考虑光饱和效应,还采用实测叶面积校准的LAI(leaf area index,LAI)产品(LAIi)驱动模型,最后将模拟GPP与实测GPP进行精度分析。结果显示:DTEC模型在估算毛竹的GPP上精度高于MOD17算法。相比MOD17算法,DTEC的拟合RMSE(root mean square error,RMSE)由1.95 g·C/(m^(2)·d)下降到1.41 g·C/(m^(2)·d),拟合RMSEr(relative root mean square error,RMSEr)由41.80%下降到30.25%;相比MOD17算法,DTEC的验证RMSE由2.44 g·C/(m^(2)·d)下降到2.24 g·C/(m^(2)·d),RMSEr由55.59%下降到50.91%。相比MODIS LAI,采用LAIi产品驱动DTEC模型时,拟合和验证阶段的精度均提高,其中拟合RMSE下降了0.10 g·C/(m^(2)·d),RMSEr下降了2.12%;验证RMSE下降了1.01 g·C/(m^(2)·d),RMSEr下降了22.78%。耦合了光饱和效应的DTEC-GPPmax模型,在使用LAIi产品驱动时,反演毛竹GPP精度最高,拟合RMSE为1.09 g·C/(m^(2)·d),RMSEr为23.40%;验证RMSE为1.18 g·C/(m^(2)·d),RMSEr为26.81%。本文考虑光饱和效应和使用LAIi产品驱动模型均能提高毛竹GPP模拟精度。上述成果为区域尺度准确反演毛竹GPP提供一种有效方法,便于评估其碳汇价值和生态经济价值。The Gross primary productivity(GPP)of vegetation is the largest component of carbon flux in terrestrial ecosystems and plays an important role in regulating the global carbon cycle.In this paper,we used meteorological data and remote sensing data from 2011-2014 flux tower observations in Anji County(Zhejiang,China)to drive the DTEC(diffuse-fraction-based two-leaf terrestrial ecosystem carbon flux model,DTEC)model to simulate the 8-day-scale GPP of Phyllostachys edulis forest.Accurate estimates of bamboo GPP help to understand the response of bamboo forest ecosystems to climate change and the impact of human activities.This study not only took into account the light saturation effect of bamboo leaves,but for the underestimation of the MODIS LAI product,this study also used the measured LAI(leaf area index,LAI)of bamboo in the study area to calibrate the LAI product(LAIi)and drive the model,and finally the simulated 8-day average GPP was analysed for accuracy against the measured 8-day average GPP.The results show that the accuracy on estimating the GPP of bamboo is higher than that of the MOD17 algorithm due to the distinction between sunlit and shaded leaves by the DTEC model,which refines the canopy of bamboo.Compared with the MOD17 algorithm,in the 2011-2012 data calibration phase:RMSE(root mean square error,RMSE)of DTEC decreased from 1.95 g·C/(m^(2)·d)to 1.41 g·C/(m^(2)·d),and RMSEr(relative root mean square error,RMSEr)decreased from 41.80% down to 30.25%;compared to the MOD17 algorithm,in the 2013-2014 data validation phase:RMSE of DTEC decreased from 2.44 g·C/(m^(2)·d)to 2.24 g·C/(m^(2)·d)and the RMSEr decreased from 55.59%to 50.91%.Compared to the MODIS LAI data,the accuracy of both the calibration and validation phases was improved when the DTEC model was driven by the LAIi product,with the calibrated RMSE decreasing by 0.10 g·C/(m^(2)·d)and RMSEr by 2.12%,and the validation RMSE decreasing by 1.01 g·C/(m^(2)·d)and RMSEr by 22.78%.The DTEC-GPPmax model coupled with the light saturation effect

关 键 词：毛竹 总初级生产力 双叶光能利用率模型 叶面积指数 光饱和 

分 类 号：S795.7[农业科学—林木遗传育种]