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:2019,28(9):133-139
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兴趣区域高分辨率叶绿素荧光遥感数据集重建框架
(1.中国科学院 计算机网络信息中心, 北京 100190;2.中国科学院大学, 北京 100049)
Reconstruction Framework of High Resolution SIF Remote Sensing Dataset in Region of Interest
(1.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;2.University of Chinese Academy of Sciences, Beijing 100049, China)
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投稿时间:2019-03-10    修订日期:2019-04-04
中文摘要: 太阳诱导叶绿素荧光数据是反映全球植被总初级生产力的关键指标,对于监测全球或地区性的植被生产力变化和气候变化的影响具有重大意义.然而,目前为止仍没有高分辨率和全球覆盖的可用原始数据集.虽然存在一些全球性的重建数据集,但一般存在区域特异性不够明显等问题,从而一定程度上限制了该数据在特定的兴趣区域上的可用性.为了探索重建基于兴趣区域的叶绿素荧光数据的方法,本研究以华北平原为例,综合遥感数据处理技术,机器学习方法和生态学原理,对原始轨道碳观测者二号卫星所提供的叶绿素荧光数据集和MODIS地表反照率数据建模.重建数据集基于兴趣区域内原始数据的时空特征而建,具有连续的空间覆盖和更高的空间分辨率,经过验证,该框架可以为特定区域提供有效的有针对性的的叶绿素荧光数据,可为兴趣区域的与叶绿素荧光数据有关的研究提供数据支持.
Abstract:Solar-Induced chlorophyll Fluorescence (SIF) is a functional proxy of Gross Primary Production (GPP), and it is crucial to monitor the global or regional vegetation productivity and climate change. However, there is no available original dataset with global continuous coverage at high spatial resolution. Although there are some reconstructed datasets, but they are not specific enough to a region of interest. This disadvantage thus will limit their application for research related to SIF in such a region of interest. In order to explore the method to reconstruct SIF dataset in a region of interest, we built models on MODIS reflectance data and original OCO-2 SIF, combining machine learning, remote sensing technology and ecological principles. The reconstructed SIF dataset was built based on the spatial-temporal features with contiguous spatial coverage and higher spatial resolution. Based on the validation performance, this framework is capable for providing efficient and specific SIF data for a region of interest, and it can support the research related to SIF in this area.
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基金项目:国家科技部国家科技基础条件平台项目(DKA2017-12-02-18)
引用文本:
于龙龙,罗泽,阎保平.兴趣区域高分辨率叶绿素荧光遥感数据集重建框架.计算机系统应用,2019,28(9):133-139
YU Long-Long,LUO Ze,YAN Bao-Ping.Reconstruction Framework of High Resolution SIF Remote Sensing Dataset in Region of Interest.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):133-139

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