水稻叶片最优叶肉结构参数估算

叶片作为植物冠层的基本组成元素,其自身的光学特性直接影响着遥感所能获得的植物冠层反射光谱。从原理上讲,叶片的光学特性不仅取决于其内部生化组分含量的多少,还与其物理结构密切相关。因此对叶片内部物理结构进行估算有助于分离其对叶片光谱的影响,从而提高叶片生化信息反演的精度。在基于叶片内部辐射传输过程的PROSPECT模型中,叶片内部结构用一个假想的叶肉结构参数N来描述。PROSPECT模型模拟光谱发现,N对叶片反射率和透过率均影响显著,且影响范围涵盖400—2500nm的全部波段。本文利用水稻叶片实测光谱和生化数据尝试了3种N的估算方法,包括两种经验方法和一种模型反演方法,并对其进行比较。结果表明,由于两种经验方法都基于N和表观叶面积（SLA）之间的非线性经验公式,因此两者具有内在的数学关系。运用模型反演方法估算的N可在实测水稻光谱和模型模拟光谱间得到最小RMSE,且其在数值上小于两种经验方法的估算值。以N为因变量,叶片光谱反射率为自变量,运用逐步线性回归分析建立了N的光谱估算模型,550nm,816nm,1210nm和1722nm四个波段被选入模型,回归效果较好,为N的估算提供了一种新的经验方法。

Estimation of Optimal MesophyH Structure Parameter of Rice Leaves

Leaves are a basic component of plant canopy and their optical properties have great influence on canopy reflectance spectra that can be obtained by remote sensors.In principle,the reflectance spectra are determined by the biochemical constituents and biophysical structure of the leaves.The accurate estimation of leaf structure may help to separate its contribution to leaf spectra and improve the inversion of leaf biochemical information that is widely used in many fields.In this paper,leaf biophysical structure is described as an assumed dimensionless variable-leaf mesophyll structure parameter noted as N.It is one of four input variables of the PROSPECT model,a well-known within-leaf radiative tranfer model.Model simulated spectra show that it has great effect on leaf reflectance and transmittance spectra ranging from visible to shortwave infrared radiation.Three methods,including two empirical methods and one model inversion method,are examined and compared.Results show that the calculated N by model inversion method can provide least RMSE between measured spectra and model simulated spectra if leaf biochemical variables are given.Its value is generally less than Ns calculated by empirical methods based on its relation with specific leaf area(SLA).Furthermore,four bands,550nm,816nm,1210nm and 1722nm,are selected to be sensitive for N estimation using stepwise multiple linear regression(SMLR).