基于绿叶分离的毛竹林碳通量模拟及其年际变化调控机制研究

The quantitative inversion of canopy structure parameters, the simulation of carbon flux and the control mechanism of interannual change are greatly uncertain due to the uncommon variation characteristics of canopy structure of Moso bamboo. Fine treatment of the canopy structure is an effective way to reduce these uncertainties. Based on the methods of field investigation, satellite remote sensing, unmanned aerial vehicle, flux observation tower and model simulation, taken separation green leaf area from total leaf area as a breakthrough, this project constructs a remote sensing inversion method for green leaf area index and chlorophyll content based on multi-source observation data and biophysical multi-parameter joint constraints to realize accurate inversion of canopy structure parameters. The influence of the decomposition of light-use efficiency of sunlit and shaded green leaves on accurate simulation of carbon flux is analyzed. The evaluation method of contribution of biological and environmental factors to the interannual variation of carbon and water fluxes is developed to reveal the regulation mechanism of the interannual variation of carbon flux. The results of this project will provide important technical and theoretical basis for accurate monitoring of carbon flux in regional scale and increase the production of Moso bamboo forest.

毛竹林冠层结构异变特征使得冠层结构参数遥感定量反演、碳通量模型模拟及其年际变化调控机制存在较大的不确定性。精细化处理冠层结构是降低这些不确定性的有效途径。本项目以样地调查、卫星遥感、无人机航拍、通量观测塔和模型模拟等技术和方法为基础，以从总叶面积中分离绿色叶面积为突破口，构建基于多源观测数据及生物物理多参数联合约束的绿色叶面积指数和叶绿素含量遥感协同反演方法，实现冠层结构参数精准反演；探讨阴阳绿叶光能利用率分解对碳通量精准模拟的影响，实现碳通量精准模拟；开发生物因子和环境因子对碳通量年际变化贡献评价方法，揭示碳通量年际变化调控机制。研究成果对区域尺度毛竹林碳通量精准监测和提升毛竹林生产力具有重要的技术和理论支撑意义。

毛竹林冠层结构异变特征使得冠层结构参数遥感定量反演、碳通量模型模拟及其年际变化调控机制存在较大的不确定性。该项目以样地调查、卫星遥感、无人机航拍、通量观测塔和模型模拟等技术和方法，开展了毛竹林叶生物量（叶面积）和叶绿素含量遥感定量反演研究，探讨了高精度叶面积和叶绿素产品对毛竹林碳通量精准模拟的影响，定量评价了生物因子和环境因子对碳通量年际变化贡献。主要研究成果如下：大年与小年毛竹林叶面积和叶绿素之间存在显著差异，为解释毛竹林碳吸收年际变化调控机制提供研究思路；采用分段建模策略构建了毛竹叶生物量估算模型，精度为75.7%，显著提高毛竹叶生物量估测精度；开发了一种基于局部最大值理论的单株毛竹冠幅监测新方法，创新性地融入合并和分裂策略，为精准监测毛竹冠层结构参数提供技术支撑；构建了针对毛竹叶绿素遥感反演的可见光植被指数，建立了基于无人机影像的叶绿素含量反演模型，揭示了飞行高度和光照强度对叶绿素含量反演精度影响机制；构建了基于辐射传输模型的叶片叶绿素含量遥感反演模型，精度达到91.7%，为精准实时大区域反演毛竹叶绿素提供技术支撑。采用优化后的叶面积产品驱动双叶光能利用率模型显著提高毛竹碳通量模拟精度，为区域尺度准确反演毛竹碳通量提供有效方法。环境因子对毛竹碳通量年际变化贡献呈增加趋势，为评估气候变化对毛竹固碳功能影响提供科学依据。