草地植物化学元素高光谱遥感监测技术与机理研究

针对目前草地植物化学元素高光谱遥感监测技术不完善，不准确、不智慧问题，本项目选择内蒙古典型草原开展研究，结合高光谱成像、光谱解译、光谱增强技术，挖掘隐藏在纳米级成像高光谱信号中的弱信息，构建多粒度光谱本征向量；以单位面积上的群落尺度植物性状为媒介，拓展遥感观测与群落植物性状融合技术，解决遥感观测与传统植物性状空间尺度不匹配问题，促进实测的传统植物性状数据有效支撑草地植物化学元素高光谱反演和验证；构建具有明确生态功能、可遥感观测反演的草地植物化学元素参数体系，发展草地植物化学元素光谱识别和精准定量反演的新机制、新方法；研发多粒度光谱本征向量自动处理、传统植物性状尺度拓展与地面-无人机遥感协同观测一体化融合的草地植物化学元素高光谱遥感监测系统。推动遥感数据更好地服务于区域生态环境问题的解决，促进遥感、生态学、全球变化等多学科的发展，形成新的生长点。

To solve the problems that hyperspectral remote sensing monitoring of grassland plant chemical elements is imperfect, inaccurate and unintelligent recently, we will select Inner Mongolia typical grassland to carry out this research work, the main research contents include: Integrate hyperspectral imaging, spectral interpretation, and spectral enhancement technology to extract the weak information hidden in the nano-scale hyperspectral signal and build the multi-granularity spectral eigenvectors; Choose community plant traits in unit area as a basic medium, to mactch remote sensing observation and traditional plant trait measurement, so that these large measured plant trait data can be used into hyperspectral inversion and validation of grassland plant chemical elements; Establish a grassland chemical element parameter system with clear ecological function that can be observed by remote sensing, develop a new mechanism, some new models and methods for spectral identification and precise quantitative inversion of grassland plant chemical elements; develop a hyperspectral remote sensing monitoring system of plant grassland chemical elements, which can be able to automatically process multi-granularity spectral eigenvectors, upscale traditional plant traits and realize ground-based and low-altitude collaborative observation. This research will promote the remote sensing data to better serve the solution of regional ecological environment problems and accelerate the development of remote sensing, ecology, and other disciplines.