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摘要

引用本文:

DOI:

10.11834/jrs.20209376

收稿日期:

2019-10-16

修改日期:

2020-03-16

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植被光能利用率遥感估算
朱安然, 孙睿, 王梦佳
北京师范大学
摘要:

光能利用率表征植被通过光合作用将所截获/吸收的能量转化为有机碳的效率,是遥感估算植被生产力的关键参数。由于植被分布和气候环境的综合影响,光能利用率表现出显著的空间异质性和时间动态性,光能利用率的不确定性成为后续生产力模型估算精度不高的重要原因。本文以Fluxnet全球通量站点数据和MODIS LAI/fPAR产品为数据源,比较了5种遥感植被生产力模型中的光能利用率估算方法,并在此基础上考虑光照散射条件对光能利用率的影响,结合晴空指数,利用逐步线性回归方法和参数优化方法建立不同植被类型的光能利用率估算模型。验证结果表明,考虑晴空指数可提高光能利用率估算精度,两种方法估算得到的光能利用率值RMSE均低于0.5 gC MJ-1,逐步线性回归法尽管机理欠缺,但由于选择因子较多,光能利用率估算精度较高(R2=0.461,RMSE=0.403 gC MJ-1);广泛应用的参数化方法由于考虑的因子较少、模型形式较固定,光能利用率估算精度稍低(R2=0.306,RMSE=0.489 gC MJ-1)。本文所建立的光能利用率估算模型可应用于区域或全球植被光能利用率及生产力的估算。

Estimation of Light Use Efficiency by Using Remote Sensing Data
Abstract:

Light use efficiency (LUE) characterizes the efficiency with which vegetation converts captured/absorbed radiation into organic dry matter through photosynthesis. LUE is a key parameter for estimating vegetation productivity at regional scale. Due to the combined effects of vegetation distribution and climate, LUE shows significant spatial heterogeneity and time dynamics. The uncertainty of LUE estimation is an important reason for the low accuracy of subsequent productivity models. Based on global Fluxnet data and MODIS LAI/fPAR products, this paper compared five existing LUE estimating methods first. And in order to reflect the difference of LUE between the sunlit and shaded leaves, we took the Clearness Index (CI) into account and established two LUE models by stepwise linear regression method and parameter optimization method respectively. The validation results show that the inclusion of CI can improve the accuracy of LUE estimation, and the RMSE by the two methods are both less than 0.5 gC MJ-1. Although the stepwise linear regression method lacks the mechanism, the estimation accuracy of LUE (R2=0.461, RMSE= 0.403 gC MJ-1) is higher due to the more selected factors. The parameterization method has a slightly lower accuracy (R2=0.306, RMSE=0.489 gC MJ-1) due to fewer factors and a fixed model form. The results show that the LUE estimation models established in this paper can be used for the estimation of regional or global LUE and vegetation productivity.

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