利用多源观测数据和辐射传输模型解耦干旱胁迫下冠层SIF的生理与非生理信息

作为光合作用的探针，日光诱导叶绿素荧光（SIF）对水分胁迫下植物生理异常的响应迅速。但遥感获取的冠层顶部SIF信号同时受生理和非生理因素的影响。从冠层SIF中解耦生理和非生理信息对胁迫探测有重要意义，但非常困难。本研究拟开展以小麦和玉米为研究对象梯度干旱控制实验，首先基于测量的叶片反射率、透过率和荧光光谱，利用Fluspect模型反演叶片结构、生化参数与荧光量子效率；然后利用无人机多角度高分辨率影像生成的点云，估算冠层叶倾角分布和叶面积指数；最后将叶片和冠层参数输入SCOPE模型，模拟分析生理与非生理参数对冠层SIF变化的贡献，确定水分胁迫下冠层红光和远红光波段SIF日变化的主要驱动因子和变化规律。该研究将多源观测数据与模型反演相结合，减少反演问题中的病态性，兼顾可行性和准确性。研究成果将有助于进一步厘清SIF对干旱的响应机理，为SIF农业干旱遥感监测方法打下基础。

As a probe of photosynthesis, sun-induced chlorophyll fluorescence (SIF）can response rapidly to the physiological abnormalities of plants under water stress. Top-of-canopy (TOC) SIF is affected by both physiological and non-physiological factors. Decoupling physiological and non-physiological information from TOC SIF is important for stress detection, but it is very difficult. In this study, the gradient drought control experiments will carried out on wheat and maize. Firstly, based on the measured reflectance, transmittance and fluorescence spectrum of the leaf, the Fluspect model will be used to invert the leaf structure, biochemical parameters and fluorescence quantum efficiency. Then the point clouds generated by the UAV multi-angle high-resolution image will be used to estimate the canopy leaf inclination distribution and leaf area index. Finally, leaf and canopy parameters will be input into the SCOPE model to simulate and analyze the contribution of physiological and non-physiological variation to the change of TOC SIF. Then, the main driving factors and changing rules of diurnal variation of SIF in red and far-red bands of canopy under water stress will be determined. This study combines multi-source observation data with model inversion to reduce the problem of ill-posed inversion, and also considers the feasibility and accuracy. The research results will help to further clarify the response mechanism of SIF to drought, and lay a foundation for SIF remote sensing monitoring method of agricultural drought.