从光谱指数到融合数据集的全球植被遥感数据产品

遥感对地观测为理解陆地植被动态与全球环境变化规律提供了数据基础,基于卫星观测的全球植被遥感数据产品近年来层出不穷,但缺乏基于数据使用者视角的系统梳理。本文回顾了全球植被遥感数据产品的发展历程,梳理了相关卫星计划及对应数据产品,分析了从植被光谱指数到生物物理特征参量、传统光学遥感反演到叶绿素荧光及星载微波反演产品、从单一传感器生产到多数据源—多数据集融合产品的发展脉络,阐述了全球植被遥感数据产品的来源、特征与关联关系。认为当前全球植被遥感数据产品的生产与应用存在一定脱节,遥感产品精度制约了对地球系统的深入理解。全球植被遥感数据产品正从宏观状态监测向专业化、精细化、标准化转变,建议未来制备全球遥感数据产品应充分利用目前已积累的长时序观测数据,融合多源观测资料,提升现有数据集的时空分辨率、精度及连续性;注重提高特殊区域与特定类型生态系统的反演精度,加强多维植被特征的一体化监测;建立全球植被产品数据共享平台,提供标准的全流程数据处理与分发服务,将数据不确定性信息高效、清晰提供给用户。

Remote sensing based global vegetation products: From vegetation spectral index to fusion datasets

In recent years, earth observation and remote sensing technology has enabled a better assessment of the terrestrial vegetation dynamics and improved our understanding of critical global environmental change issues. Various long-term satellite derived vegetation datasets have been produced at the global scale. However, a systematical review of these new resources from the perspective of various end-users is still lacking. We review the history of remote sensing derived global vegetation datasets by focusing on satellite missions and corresponding data products, and paying special attention to the sources, characteristics and connections of them. These products have undergone remarkable transitions, i.e. (1) from vegetation spectral index to biophysical characteristics, (2) from traditional optical remote sensing to Solar-induced Chlorophyll Fluorescence (SIF) and satellite radar system, and (3) from single-sensor-derived products to datasets fusing multiple remote-sensing sources. In the future, in order to improve the quality of existing products considerably, including its temporal and spatial resolutions, accuracy, consistency and continuity, new datasets can be developed through fusing multiple data sources based on the raw data of long-term satellite observations. Regions and ecosystems characterized by unique natural conditions should be given more attention in the way of developing algorithms. Meanwhile, multiple vegetation traits need to be monitored. In addition, the development of sharing platforms for remote-sensing datasets, enabling targeted vegetation monitoring, is urgently required so as to facilitate standard data processing and distribution services, and deliver data quality information to end-users in an efficient and transparent way.