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遥感技术与应用  2020, Vol. 35 Issue (5): 990-1003    DOI: 10.11873/j.issn.1004-0323.2020.5.0990
LAI专栏     
我国叶面积指数卫星遥感产品生产及验证
方红亮1,2()
1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
2.中国科学院大学资源与环境学院,北京 100049
Development and Validation of Satellite Leaf Area Index (LAI) Products in China
Hongliang Fang1,2()
1.LREIS,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
2.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

利用卫星遥感生产叶面积指数(Leaf area index: LAI)产品并进行真实性检验是植被定量遥感的一项重要研究内容。过去10 a,我国研究人员利用MODIS或AVHRR观测数据生产了GLOBALBNU, GLASS, GLOBMAP,MuSyQ和FSGOM等数套全球和全国LAI产品,受到了国内外的广泛关注和应用。在产品生产的同时,我国学者也广泛开展了LAI产品在全球和区域尺度的真实性检验研究工作。本文总结了我国LAI卫星产品生产和验证研究工作的现状和趋势。近年来,我国在本领域相关的理论、技术和方法研究方面取得了全面进展,LAI产品精度和连续性已与国外先进水平相当,但仍然存在数据源单一且依赖国外、算法不确定性不清、生产不连续以及缺乏充分验证等问题,客观上影响了LAI产品应用的广度及深度。未来应充分利用新的卫星数据特别是国产数据,加强遥感机理模型、反演算法以及应用的创新研究,生产具有特色的高质量LAI产品,满足地球系统科学的研究需求。同时,应加强LAI验证基础设施建设,发掘利用更广泛的验证站点,同时增进国际合作,加强产品的推广使用,在与用户的互动交流反馈中进一步提高产品的市场。随着我国对相关研究投入的增加,可以预期未来20 a将是我国LAI遥感产品及验证研究由“跟跑”国际先进水平向“并跑”乃至“领跑”过渡的机遇期。

关键词: 叶面积指数遥感产品验证真实性检验中国    
Abstract:

Development and validation of Leaf Area Index (LAI) product from satellite remote sensing data is a crucial research topic in vegetation remote sensing. Over the past decade, a number of global and national LAI products, such as GLOBALBNU, GLASS, GLOBMAP, MuSyQ, and FSGOM have been developed in China from MODIS and AVHRR observations. These products have been widely used in home and abroad. At the same time, Chinese scholars have carried out extensive product validation studies at global and regional scales. This paper summarizes the current status and future development trends in LAI product development and validation in China. During the past years, significant progresses have been made in theory, technology and method studies in this field. The accuracy and continuity of domestic LAI products are on par with the advanced international level. However, there are still some drawbacks, such as heavily relying on data sources from abroad, unclear algorithm uncertainties, discontinuous product, and lack of sufficient validation, which greatly limit the breadth and depth of the product application. For future research, new satellite data, especially domestic satellite data, should be fully harnessed. The development of remote sensing models and inversion algorithms should be strengthened, and applications broadened in order to generate high quality LAI products to meet the research needs in Earth system sciences. In LAI product validation, current field measurement infrastructure should be improved, more extensive validation sites be developed, international collaboration be facilitated, and product usage broadened. The product market should be improved through more interactions and feedbacks with product users. With the increasing funding opportunities in this field, it is expected that the next two decades will see China's LAI remote sensing production and validation studies transit from a “following” role to a “parallel running” and even a “leading” role internationally.

Key words: Leaf Area Index (LAI)    Remote sensing products    Validation    Evaluation    China
收稿日期: 2020-07-21 出版日期: 2020-11-26
ZTFLH:  TP75  
基金资助: 国家重点研发计划(2016YFA0600201)
作者简介: 方红亮(1971-),男,浙江淳安人,研究员,主要从事关键植被参数反演、产品生产与验证研究。E?mail:fanghl@lreis.ac.cn
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引用本文:

方红亮. 我国叶面积指数卫星遥感产品生产及验证[J]. 遥感技术与应用, 2020, 35(5): 990-1003.

Hongliang Fang. Development and Validation of Satellite Leaf Area Index (LAI) Products in China. Remote Sensing Technology and Application, 2020, 35(5): 990-1003.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.5.0990        http://www.rsta.ac.cn/CN/Y2020/V35/I5/990

产品*传感器机构覆盖 范围空间 分辨率时间 分辨率覆盖时段输入波段主要方法支持项目参考文献
GLOBALBNUMODIS北京师范 大学全球1 000 m8 d2000~2016MODIS LAI时空滤波自然科学 基金[29]
GLASSMODIS (GLASS 5.0)北京师范 大学全球500 m8 d2000+红-近红外广义神经 网络863项目[30]
AVHRR (GLASS 4.0)北京师范 大学全球0.05°8 d1981+红-近红外广义神经 网络863项目[31]

GLOBMAP

(V3.0)

MODIS/AVRRR中科院地理资源所全球8 km(1981~1999)/500 m(2000~)半月(1981~1999)/8 d(2000~)1981+红/近红外/短波红外经验植被 指数973项目[32]
MuSyQ (V2.0)MODIS中科院 空天院全球500 m5 d2010~2015红-近红外辐射传输 模型查找 表反演高分专项[18]
FSGOM(V1.0)MODIS南京大学中国500 m8 d2000~2014红/近红外/短波红外经验植被 指数973项目[33]
表1  国产的几套主要LAI产品
级别描述主要特点
一级只在少量地点(<30)和时间段,通过与实测和其他参考数据对比对产品精度进行过评价少量地点(<30)和时间段的验证
二级对产品精度在相当数量的地点和时间段进行了估算;对产品自身及与其他类似产品的时空一致性也已在全球代表性地点和时间段进行了评价;结果发表在审稿期刊上全球代表性站点和时间段的验证
三级通过与实测和其他参考数据的对比,对产品的不确定性特征已经得到很好的量化;产品不确定性已经在具有全球代表性的多个地点和时间段进行过深入的统计分析;对产品自身及与其他类似产品的时空一致性也已在全球代表性地点和时间段进行了评价;结果发表在审稿期刊上全球验证,不确定性很好地进行了量化和统计分析,时空一致性得到评价
四级在产品新版本发布以及时间序列延长后,三级验证结果能够系统地更新全球性系统验证
表2  遥感产品的四级验证框架*
方法描述参考文献
Ⅰ.点与像元的直接对比实测点上的LAI与卫星LAI产品的直接对比[36,37]
Ⅱ.与升尺度后的高分辨率参考数据对比通过高分辨率遥感数据将实测LAI升尺度,然后与卫星产品对比[20,22,38]
Ⅲ.多个产品之间的交叉检验多个时空分辨率相似的产品之间的交叉对比[21,39,40]
Ⅳ.与其他相关变量的一致性对比将LAI与其他光谱、生物和大气参数(如NDVI、FAPAR和反照率等)进行一致性评价[41,42]
Ⅴ.与模型模拟的LAI进行对比将LAI产品与模型模拟的LAI进行对比[43,44,45]
Ⅵ.在模型中LAI作为输入对比输出效果将不同LAI产品作为模型的输入值,通过输出结果的优劣评价输入LAI的产品质量[46,47]
表3  LAI遥感产品的主要验证方法[2]
图1  LAI遥感产品的生命周期
模式算法开发产品生产产品验证产品发布描述
(1)AAAA产品各环节均由一个机构A承担
(2)AABA算法—生产—发布由A承担,验证由B承担
(3)AAAB算法—生产—验证由A承担,发布由B承担
(4)AABB算法—生产由A承担,验证—发布由B承担
(5)AABC算法—生产由A承担,验证和发布由B和C分别承担
(6)ABBC算法由A承担,生产—验证由B承担,发布由C承担
(7)ABCC算法由A承担,生产由B承担,验证—发布由C承担
(8)ABCD产品各环节由不同的机构独立承担
表4  LAI等遥感产品生产与验证的组织和运行模式
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