高温干旱复合胁迫下日光诱导叶绿素荧光与作物光合作用的关联机制

The progress of photosynthesis suffers severe damage from high temperature usually accompanied with drought under the background of climate change. Carbon dioxide fixed through photosynthesis by green vegetation is known as gross primary production (GPP). Hence, the accurate estimation of GPP is of supreme importance for global food security and carbon cycle. As an accompanying product of photosynthesis, sun-induced chlorophyll fluorescence (SIF) is a direct indicator of actual photosynthesis progress. And the linear relationship between GPP and SIF offers a reliable method for GPP measurement by remote sensing. However, this linear relation will be broken under compound stress of heat and drought. Thus, current GPP-SIF model is not applicable under compound stress of heat and drought. Therefore, heat and drought stress simulation experiment is performed choosing winter wheat as the subject. Then, the optimum SIF algorithm is chosen for SIF calculation after comparing with various algorithms. Moreover, the leaf level and canopy level GPP are acquired by diurnal observation data set and SCOPE model simulation. The objects of this project are to reveal mechanistic of the relationship between GPP and SIF at different spatial and temporal scales under compound stress of heat and drought, and to build a new and applicable GPP-SIF model.

在全球气候变化背景下，高温和干旱在作物生长发育过程中常相伴出现，对植物的光合作用进程造成严重破坏。总初级生产力（GPP）反应了绿色植物通过光合作用所固定的有机碳总量，精确估算GPP对世界粮食安全、全球碳循环研究有着重要意义。日光诱导叶绿素荧光（SIF）作为光合作用的伴生产物，能直接反应光合作用的动态变化。GPP和SIF之间存在着的线性关系为遥感估算GPP提供了可靠的方法。然而，在高温干旱复合胁迫下，该线性关系将存在极大的不确定性，故该状态下基于SIF的GPP估算模型仍有待进一步研究。本项目以冬小麦为研究对象，对其进行高温干旱复合胁迫田间试验，对比多种SIF算法，选择最优算法提取叶片和冠层SIF信息，结合日变化观测数据集和SCOPE模型获取叶片和冠层GPP，进而揭示高温干旱复合胁迫下不同季节和不同空间尺度GPP-SIF的关联机制，最终建立适用于胁迫状态下的GPP-SIF估算模型。