引用本文:项铭涛,卫 炜,吴文斌※.植被物候参数遥感提取研究进展评述[J].中国农业信息,2018,30(1):55-66
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植被物候参数遥感提取研究进展评述
项铭涛1,卫 炜2,吴文斌※1
1.中国农业科学院农业资源与农业区划研究所/ 农业部农业遥感重点实验室,北京100081;2.农业部规划设计研究院/ 农业部耕地利用遥感重点实验室,北京100125
摘要:
【目的】遥感方法提取植被物候具有宏观、高效、便捷的特点,利用遥感提取植被物 候结果可以从较大尺度上研究整个植被生态系统的物候特征。【方法】文章以植被物候遥 感提取的过程为线索,采用文献综述法,对植被物候参数遥感提取的各个方面进行阐述。 【结果】系统描述了植被物候提取的遥感数据资源,包括遥感专题指数和遥感数据来源;归 纳了植被物候遥感提取的技术方法,包括时序植被指数重构技术和植被物候参数提取方法; 总结了植被物候遥感提取结果验证途径和误差来源,地面物候观测数据和模型模拟数据是直 接验证的途径,他人研究成果和植物生理参量的地面观测数据提供间接验证的途径,误差来 源于遥感数据的时间和空间分辨率以及植被物候提取技术方法。最后,针对当前植被物候遥 感提取存在的主要问题及未来的发展趋势,从研究对象、数据来源、技术方法和结果验证这 4个方面进行了探讨。【结论】尽管植被物候遥感提取的大量研究在理论、技术方法和应用 方面都取得明显进展,但在研究对象、数据来源、技术方法和结果验证这些方面仍然存在着 一些关键科学问题,需要进一步进行深入研究。
关键词:  植被物候;遥感监测;植被指数重构;物候信息提取;验证
DOI:10.12105/j.issn.1672-0423.20180106
分类号:
基金项目:国家重点研发计划项目“小麦生产系统对气候变化的响应机制及其适应性栽培途径”(2017YFD0300200)
Review of vegetation phenology estimationby using remote sensing
Xiang Mingtao1, Wei Wei2, Wu Wenbin1
1.Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081;2.Key Laboratory of Cultivated Land Utilization and Remote Sensing,Chinese Academy of Agricultural Engineering,Ministry of Agriculture,Beijing 100125
Abstract:
[Purpose]Detection of vegetation phenology using remote sensing data has the characteristics of macroscopic,efficient and convenient. With the help of remote sensingmeasurements,we can capture the phenology characters of the whole vegetation ecosystem. [Method]In this article,based on the progress of vegetation phenology detection by remote sensing,we applied literature review method to expound vegetation phenology detection by remote sensing from various aspects.[Result]First,the frequently used remote sensing data resources were systematically,including remote sensing vegetation index and remote sensing data source. Second,the methods of time series data reconstruction method and vegetation phenology detection method were summarized. Third,the result validation methods and the error sources of vegetation phenology detection were illustrated. To be specific,ground phenological observation data and model simulation data are direct validation ways,while other research results and ground observation data of plant physiological parameters provide indirect ways. And the error sources are derived from temporal resolution and spatial resolution of remote sensing data as well as vegetation phenology detection method. In the end,we discussed the primary issues and future trends of vegetation phenology detection by remote sensing from four aspects of study objects, data sources,methods and results validation.[Conclusion]Although plenty of researchers about vegetation phenology detection by remote sensing making distinct progress on theory, methodology as well as application,it still needs further deep research on some key scientific problems such as study objects,data sources,methods and results validation.
Key words:  vegetation phenology;remote sensing;time series data reconstruction;phenologydetection;validation