遥感物候学研究进展

Progress in Remote Sensing Phenological Research

植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器,其发生时间可以反映陆地生态系统对气候变化的快速响应。近年来,遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点,已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理方法的基础上,从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆盖、植物物候与农作物估产等方面系统阐述了近5年来国内外遥感物候学研究的重要进展,并针对目前研究中存在的问题,提出近期遥感物候研究的主要方向:(1)发展一种更具普适性的物候生长季节划分方法;(2)通过开展植物群落的物候观测和选择合适的尺度转换方法,统一地面与遥感的空间信息;(3)定量分析植物物候变化对人类活动的响应机制;(4)选择适宜的数学方法和模型,实现各种不同分辨率遥感数据的融合;(5)通过动态模拟,预测植物物候对未来气候变化的响应。

Plant phenological phenomena are the most salient and sensitive bio-indicators of the environmental change at seasonal and interannual scales. Timings of plant phenological phenomena can indicate the rapid response of terrestrial ecosystems to climate change. Since the remote sensed phenology observation is characterized by multi-temporal, broad coverage, spatial continuality, and relatively long time series, recently, it has been an important means for detecting responses and feedbacks of vegetation dynamics to global climate change. On the basis of introducing remote sensing data sets and processing methods for monitoring plant phenology, we systematically reviewed important progresses in remote sensing phenology during the last five years worldwide focusing on identification of the phenological growing season, plant phenology and climate change, plant phenology and net primary production, plant phenology and land cover, and plant phenology and crop yield estimate, and so on. Then, we pointed out some existing problems in the current research, and tried to propose some main research aspects in the near future as follows： （1） developing a kind of more general technique for identifying the phenological growing season using remote sensing data; （2） unifying surface observed and satellite derived spatial information by carrying out plant community phenology observations and selecting appropriate scale transition methods; （3） analyzing quantitatively response mechanisms of plant phenology to human activities; （4） implementing amalgamation of remote sensing data with different spatial resolutions using suitable mathematical methods and models; and （5） estimating possible responses of plant phenology to future climate change by dynamic simulations.