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遥感技术与应用  2020, Vol. 35 Issue (5): 1047-1056    DOI: 10.11873/j.issn.1004-0323.2020.5.1047
LAI专栏     
基于数据机理的植被叶面积指数遥感反演研究
郭利彪1,2,3(),刘桂香1,2,运向军1,2,张勇1,孙世贤1,2
1.中国农业科学院草原研究所,内蒙古 呼和浩特 010010
2.农业与农村部草地与农业生态遥感重点实验室,内蒙古 呼和浩特 010010
3.内蒙古工业大学信息工程学院,内蒙古 呼和浩特 010051
Vegetation Leaf Area Index (LAI) Retrieval based on Data-based Mechanistic Model Using Remote Sensing Data
Libiao Guo1,2,3(),Guixiang Liu1,2,Xiangjun Yun1,2,Yong Zhang1,Shixian Sun1,2
1.Institute of Grassland Research,Chinese Academy of Agriculture Sciences,Hohhot 010010,China
2.Key Laboratory of Remote Sensing of Grassland and Agricultural Ecology,Ministry of Agriculture and Rural Affairs PRC,Hohhot 010010,China
3.College of Information Engineering,Inner Mongolia University of Technology,Hohhot 010010,China
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摘要:

定量获取地表植被高精度时序及空间覆盖的叶面积指数(Leaf Area Index, LAI)是生态监测及农业生产应用的重要研究内容。通过使用Moderate Resolution Imaging Spectroradiometer(MODIS)植被冠层多角度观测MOD09GA数据及叶面积指数MOD15A2数据,发展了一种参数化的叶面积指数遥感反演方法并完成了必要的检验分析。研究使用基于辐射传输理论的RossThick LiSparse Reciprocal(RTLSR)核驱动模型及Scattering by Arbitrarily Inclined Leaves with Hotspot(SAILH)模型进行植被冠层辐射特征的提取,使用Anisotropic Index (ANIX)异质性指数作为指示植被冠层二向反射分布Bidirectional Reflectance Distribution Function(BRDF)的辅助特征信息,发展了基于数据机理(Data-Based Mechanistic, DBM)的植被叶面积指数建模和估算方法。通过必要的林地、农作物、草地植被实验区反演及数值分析可得知:①时间序列多角度遥感观测数据结合数据机理的叶面积指数估算方法,可实现模型参数的时序动态更新,改进叶面积指数估算结果的时序完整性及精度。②异质性指数可以用做指示植被冠层二向反射分布特征信息,可降低因观测数据几何条件差异所导致的反演结果不确定情况,同时能够补充植被时序生长过程表现的植被结构变化等动态特征。经研究实践,可将算法应用于时空尺度的叶面积指数估算,并能够为生态、农业应用提供植被的高精度遥感监测指标。

关键词: 植被叶面积指数时间序列辐射传输二向反射分布数据机理遥感反演    
Abstract:

Leaf Area Index (LAI) is the key indicator for ecological monitoring and application in agricultural production. Retrieve precision improved LAI using quantitative algorithms has been a comprehensive work for the ecological research. The paper developed a time series LAI inverse method by using Data-Based Mechanistic(DBM) modeling method and time series multi-angular remote sensing observations. Based on radiative transfer theory, the work used RossThick-LiSparse-Reciprocal(RTLSR) and Scattering by Arbitrarily Inclined Leaves with Hotspot(SAILH) model to extract the vegetation canopy bidirectional reflectance character. The Anisotropic Index (ANIX) derived from MODIS BRDF product was used to express the directional reflectance signature of vegetation canopy, and the MOD09GA multi-angular remote sensing observation and MOD15A2 LAI products data were used together in time series LAI modeling and estimation. Typical vegetation sites data are used to make validation of the LAI inversion. The basic inversion results shows that: (1) Time series multi-angular observation data combined with DBM LAI inversion method can be used to improve the integrity of LAI estimation in time series. The developed method can reduce the disturbance from observation data noise in DBM modeling and estimation. (2) Anisotropic index data enriched the vegetation canopy directional reflectance signature. It not only works for improving the time series LAI inversion but also provides the surface bidirectional reflectance properties for the other relative land surface parameters retrieved. (3) The preliminary results are superior to the MODIS LAI product in time series integrity and data value stable.

Key words: Vegetation    Leaf Area Index(LAI)    Time series    Radiative transfer    BRDF    DBM    Remote sensing retrieval
收稿日期: 2019-09-20 出版日期: 2020-11-26
ZTFLH:  TP79  
基金资助: 内蒙古自然科学基金项目博士基金(2017BS0407);国家自然科学基金项目(61962044);内蒙古自治区科技创新引导奖励资金项目(2016001);中央科研院所基本科研业务费项目(1810332014023);资助
作者简介: 郭利彪(1983-),男,内蒙古乌兰察布市人,博士,讲师,主要从事植被定量遥感及人工智能技术应用研究。E?mail: guolibiao@imut.edu.cn
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引用本文:

郭利彪,刘桂香,运向军,张勇,孙世贤. 基于数据机理的植被叶面积指数遥感反演研究[J]. 遥感技术与应用, 2020, 35(5): 1047-1056.

Libiao Guo,Guixiang Liu,Xiangjun Yun,Yong Zhang,Shixian Sun. Vegetation Leaf Area Index (LAI) Retrieval based on Data-based Mechanistic Model Using Remote Sensing Data. Remote Sensing Technology and Application, 2020, 35(5): 1047-1056.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.5.1047        http://www.rsta.ac.cn/CN/Y2020/V35/I5/1047

研究区纬度/经度(°)MODIS Tile植被类型[27]
Shihezi44.33、85.83H24V04农作物
Luancheng37.90、114.75H27V05农作物
Larose45.38、-75.22H12V04林地
Fairbanks64.87、-147.85H11V02林地
Laprida-36.99、-60.55H13V12草地
Le Larzac43.94、3.12H18V04草地
表1  研究使用植被类型及地理位置属性
图1  MOD09GA多角度观测分布特征
数据比特位参数比特值质量控制信息
0~100晴空
01
10混合
11其他
2云/阴影1
0
3~5陆地000陆地
水体001水体
表2  MOD09GA数据质量控制信息(节选)[27]
图2  植被LAI反演方法流程
图3  Shihezi农作物研究区LAI反演结果(单位:m2/m2)
图4  Larose林地研究区LAI反演结果(单位:m2/m2)
图5  Laprida草地研究区LAI反演结果(单位:m2/m2)
数据模型阶数R2YIC
RED[2121212]0.812 52-9.925 01
NIR[2121212]0.820 93-9.890 42
BI[43434343]0.796 51-11.248 41
表3  Shihezi农作物植被反演模型结构及参数
数据模型阶数R2YIC
RED[2222222]0.847 82-10.170 92
NIR[2222222]0.816 91-9.915 79
BI[32232213]0.820 69-10.693 13
表4  Larose林地植被反演模型结构及参数
数据模型阶数R2YIC
RED[2121212]0.772 05-9.872 11
NIR[2121212]0.798 37-9.920 82
BI[32323323]0.782 52-10.501 34
表5  Laprida草地植被反演模型结构及参数
研究区数据R2偏差相对偏差/%STD

相关性

STD

最小值最大值最小值最大值

农作物

(H24V04)

RED0.753 5-0.877 01.001 7-23.6482.210.524 10.256 0
NIR0.752 6-1.335 80.840 8-56.6069.010.522 10.261 0
BI0.635 5-1.957 10.854 9-45.5183.230.783 50.316 2
MOD150.567 9-2.4501.281 6-72.97105.21.121 10.438 6

林地

(H12V04)

RED0.799 6-1.328 71.210 1-62.1032.880.478 90.196 8
NIR0.716 3-1.396 91.421 1-54.7838.620.565 40.217 9
BI0.731 0-1.545 01.092 1-61.7829.680.512 00.199 3
MOD150.694 9-1.414 81.820-70.4649.460.705 20.289 7
表6  研究区不同植被类型反演结果统计
图6  反演结果LAI与地面实测值散点数据分布 (m2/m2)
植被类型REDNIRBIMOD15
农作物0.515 40.513 80.799 61.319 1
林地0.561 60.642 30.608 00.837 4
表7  反演结果RMSE数值分析
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