基于数据机理的植被叶面积指数遥感反演研究

Vegetation Leaf Area Index(LAI)Retrieval based on Data-based Mechanistic Model Using Remote Sensing Data

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。

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 im-prove 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.