遥感反演植被含氮量研究进展

植被含氮量表征植被氮素状态。它作为植被生长状况的重要指标,在生态系统健康状况检测、生态系统生产估测、精准农业、生态系统干扰评估等方面均有重要意义。遥感监测植被含氮量主要基于高光谱和多光谱数据,采用的算法包括经验方法(波谱指数与回归分析)及物理方法(辐射传输模型法)。但受数据源和研究方法的局限,目前植被氮含量遥感监测局限于区域范围较小且内部植被类型与环境条件(气候、地形等)基本一致的情形,而对复杂生态系统的监测能力不足。未来的研究需针对氮沉降和人类活动的生态系统响应这一重大研究需求,发展和改进现有植被含氮量遥感反演方法。可考虑开展对不同环境条件下、不同类型植被光谱曲线进行标准化的研究,以形成普适的植被含氮量反演方法。并考虑综合运用多种数据(如微波遥感、无人机遥感),形成多尺度同步监测,以提高遥感对区域乃至全球范围内植被氮含量常规监测的能力。

Overview and outlook of remote sensing inversion of vegetation nitrogen content

Vegetation nitrogen content is an important indicator of vegetation growth, which plays an important role in fields of ecosystem monitoring including ecosystem health, net primary ecosystem, the disturbance of ecosystem, as well as in the precision agriculture management.Remote sensing inversion of vegetation nitrogen content currently relies on the hyperspectral/multispectral data. The inversion methods can be categorized into that based onvegetation indices, regression analysis (e.g. partial least squares regression) and radiation transfer models separately. Current satellite-based inversion of vegetation nitrogen content is limited to a small area, uniformed in species of vegetation and the environmental condition (e.g. climatecondition, topography et al). As a result, the inversion works poor for complex ecosystems. In order to meet the requirements of increasingly meaningful research projects such as global nitrogen deposition and the response of ecosystems to human activities, current methods of vegetation nitrogen content inversion need further development. It may be a potential solution to carry out research on the standardization of vegetation spectrum of different types of plants, as well as under different environmental conditions to generate more general or even universal inversion methods of vegetation nitrogen content. On the other hand, comprehensive utilization of multiple data from various sources (e.g. microwave remote sensing and unmanned aerial vehicle remote sensing data) will be an alternative solution to multi-scale simultaneous monitoring, which helps to improve remote sensing''s routine monitoring capability of regional and worldwide vegetation nitrogen content.