收稿：2016-12-08，
录用：2017-9-12，
纸质出版：2018-03
移动端阅览
准确估算陆地总初级生产力GPP(Gross Primary Productivity)数值对碳循环过程模拟有重要影响。本文介绍了多种基于植被指数以及基于光能利用率的遥感GPP算法，综述了不同算法在其研究区域的估算精度；并分析了MODIS/GPP以及BESS/GPP两种遥感GPP产品在不同植被类型的估算精度。通过对比全球碳通量站网络GPP数据表明，MODIS/GPP产品在全球估算结果具显著相关性(
R
2
=0.59)及中等标准误差(RMSE=2.86 gC/m
2
/day)，估算精度较高的植被类型有落叶阔叶林，草地等；估算精度较低类型包括常绿阔叶林，稀树草原等。本文对GPP产品中存在的不确定性进行分析，通过综述前人研究中发现的遥感估算GPP方法中存在的问题，指出可能的提高卫星遥感GPP产品估算精度的方法及发展趋势。
Gross primary productivity (GPP) is an important parameter in describing terrestrial ecosystem productivity. This review surveys the existing remote sensing GPP estimation algorithms including vegetation index based and light use efficiency based models and their accuracies
and summarizes two 1 km spatial resolution GPP product accuracy under eight different vegetation types. MOD17
which is the most commonly used GPP product
provides global-scale spatio-temporal continuous data. A strong correlation exists between global-scale MODIS/GPP and in-situ measurement (
R
2
=0.59) with medium estimation accuracy (RMSE=2.86 gC/m
2
/day). Estimation accuracy is high in deciduous broadleaved and evergreen coniferous forests but low in evergreen broadleaved forests and savanna. Finally
we analyze the uncertainties in GPP estimation and verification with the remote sensing method and suggest possible approaches to improve the accuracy of GPP estimation and its development tendency.
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