遥感GPP模型在中国地区多站点的应用与比较

发布时间：2018-06-07 12:42

本文选题：总初级生产力 + 遥感　；参考：《植物生态学报》2017年03期

【摘要】：在区域和全球尺度上估算植被总初级生产力(GPP)对理解陆地生态系统的碳循环具有重要意义。由于地表异质性的存在,局限在站点尺度上的观测数据无法直接扩展到更大空间尺度的区域上。通过与地面观测数据相结合,遥感成为实现植被GPP空间扩展的主要工具。但是现有模型对气象数据依赖较多,且在不同气象数据集的驱动下,模拟结果间会有差异,进而产生不确定性。建立以遥感数据为主的GPP模型(简称遥感GPP模型),使其易于在区域和全球尺度上应用,是解决上述问题的一个可行方案。该研究使用TG(temperature and greenness model)和VI(vegetation index model)两个遥感GPP模型,结合中国通量观测研究联盟(China FLUX)的台站数据,对中国典型植被类型的GPP进行了模拟、比较与评估,旨在进一步提高遥感GPP模型在中国区域的适用性。结果表明:(1)TG和VI模型选用的遥感参数均与GPP观测值有较高的相关性,都可以得到可信的光合转换系数m和a。基于与夜间地表温度平均值的相关关系,m和a在空间尺度上得到了扩展,这使得TG和VI都可以应用到区域尺度上。(2)TG和VI模型的模拟值与实测值间的相关性大多较高,决定系数(R~2)多在0.67以上。但不同台站间的误差变动较大,TG模型的均方根误差为0.29 6.40 g·m~( 2)·d~( 1),VI模型的均方根误差为0.31 7.09 g·m~( 2)·d~( 1)。(3)总体而言,TG模型的表现优于VI,尤其在海拔或纬度较高、以温度限制为主的台站,TG模型的模拟效果较好。上述结果初步揭示遥感GPP模型具备了在区域尺度上应用的潜力。
[Abstract]:The estimation of total primary productivity (GPP) of vegetation at regional and global scales is of great significance in understanding the carbon cycle of terrestrial ecosystems. Because of the heterogeneity of the surface, the observation data confined to the site scale can not be directly extended to the larger spatial scale area. By combining with ground observation data, remote sensing has become the main tool for vegetation GPP spatial expansion. However, the existing models depend more on meteorological data, and driven by different meteorological data sets, the simulation results will be different, which will lead to uncertainty. The GPP model based on remote sensing data (GPP model for short), which is easy to be applied in regional and global scale, is a feasible solution to the above problems. In this study, two remote sensing GPP models, TG(temperature and greenness model) and VI(vegetation index model, were used to simulate, compare and evaluate the GPP of typical vegetation types in China. The purpose of this paper is to improve the applicability of remote sensing GPP model in China. The results show that the remote sensing parameters selected by the TG and VI models are highly correlated with the observed values of GPP, and reliable photosynthetic conversion coefficients m and a can be obtained. Based on the correlation between the mean value of surface temperature at night and the average value of surface temperature, m and a have been extended on spatial scale, which makes TG and VI can be applied to the regional scale. The correlation between simulated values and measured values of TG and VI models is mostly high. The determination coefficient is more than 0.67. However, the root mean square error of TG model is 0.29 ~ 6.40 g / m ~ (-1) 路m ~ (-1), the root mean square error of VI model is 0.31 ~ 7.09 g / m ~ (-1) / m ~ (2) / m ~ (-1) / m ~ (-1) ~ (-1) / t ~ (3), and the performance of TG model is better than that of Vi in general, especially at high altitude or latitude. The simulation effect of TG model with temperature limitation is better. The above results reveal that the remote sensing GPP model has the potential to be applied in the regional scale.
【作者单位】：北京林业大学林学院;
【基金】：国家林业公益性行业科研专项(201404201) 中央高校基本科研业务费专项(BLX2015-16)
【分类号】：Q948;TP79

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