基于日光诱导叶绿素荧光的陆地总初级生产力估算

陆地总初级生产力(GPP)是全球碳循环和全球变化研究的关键参数，基于遥感方式是目前陆地生态系统GPP估算的主流方法。为了准确估算全球和区域尺度的陆地GPP,本文通过分析叶绿素荧光与光合作用关系的理论，在GPP-SIF经验线性估算模型的基础上，引入影响植被光合能力和影响冠层SIF发射的因素，构建了适用于未出现严重长期外界胁迫的基于近红外荧光的GPP估算理论模型。结合GOME-2 SIF产品、FLUXNET2015数据集中实测GPP和MODIS相关产品，在不同类型植被进行了验证分析。结果表明：该模型在所有植被类型上的估算精度较经验线性估算模型都有很大的提高，同时本文模型能较好地体现出不同植被类型GPP的季节性变化特征，在全球尺度上的应用也取得了较好的效果。

Estimation of Global Terrestrial Gross Primary Productivity Based on Solar-induced Chlorophyll Fluorescence

Gross primary productivity is a key parameter for the research of global carbon cycle and global change. The remote sensing-based method is the mainstream approach to estimate GPP of terrestrial ecosystems.Solar induced chlorophyll fluorescence is directly related to plant photosynthesis, and it is a signal emitted by the photosystem after plants absorb sunlight energy. Solar-induced chlorophyll fluorescence remote sensing can obtain vegetation growth status information in time. On the basis of the GPP-SIF empirical linear estimation model, some factors affecting the photosynthetic capacity and canopy SIF emission were introduced to construct a theoretical model of GPP estimation based on near-infrared fluorescence. The model is a goodremote sensing tool to monitor vegetation without severe long-term external stress.Verification analysis was carried out in different types of vegetation with the GOME-2 SIF products, FLUXNET2015 GPP products and MODIS GPP products.The research results showed that the estimation accuracy of the model on all vegetation types was greatly improved compared with the empirical linear estimation model. At the same time, the model can better reflect the seasonal change characteristics of the different vegetation types represented by each site. The application on the scale also achieved good results.