以冠层反射解析模型的反演算法监测叶面积指数及叶绿素含量(英文)

Monitoring of LAI and Chlorophyll Content by the Inversion of a CR Analytical Model

以二向反射辐射信息为基础的植物（作物）冠层反射（ＣＲ）模型已经能够以其反演算法推算植物结构参数，例如叶面积指数、叶角分布、叶绿素含量等。随着计算方法效率的提高，计算机功能的发展，促进人们开发利用卫星影象图大面积估算植物结构参数的计算机程序，推动理论冠层反射模型反演算法的迅速发展。该文在１９９５年由Ｋｕｕｓｋ提出的马尔可夫链冠层反射模型的基础上，发展了一个直接利用ＮＯＡＡＡＶＨＲＲ卫星和陆地卫星影象图数据进行逐个象元反演的计算程序。在ＳＵＮ４工作站上，反演５１２５１２个象元的叶面积指数及叶绿素含量，仅需要几个小时的时间。另外利用地理信息系统中的集群分析法，可使反演更加有效。每个集群点只要运转一次。因此计算机需要的计算时间取决于集群点的数目而与影象图的尺寸无关。一幅影象图如有１００－２００个集群点，就有足够的植被冠层的识辨力。该文利用爱沙尼亚塔图观象台附近的一幅陆地卫星影象图６个波段的数据，再现了农田的叶面积指数。由于马尔可夫链模型对于森林效果不好，对森林的叶面积的估算值均偏低。陆地卫星有６个波段可利用、不像ＮＯＡＡＡＶＨＲＲ只能采用两个波段。大气纠正是必须要做的，该实验采用了Ｖｅｒｍｏｔｅ（１９９４）?

Analytical canopy reflectance (CR) models have reached the level of adequacy that makes it possible to estimate vegetation parameters by the inversion of such models. The increasing efficiency of algorithms, and the increasing power of computers incite to develop procedures for the estimation of vegetation phytometrical parameters on large areas using satellite data and the inversion of theoretical CR models. A Markov chain canopy reflectance model (MCRM) by Kuusk demonstrated its ability to work on wide range of canopy optical and structural parameters even in the case of serious violations of model assumptions . The MCRM is very computer-efficient and can be easily inverted on relatively large sets of reflectance data. Canopy reflectance models relate canopy directional reflectance to canopy structural and optical parameters. In order to solve the inverse problem on satellite images, we have to convert digital counts of satellite radiometers to ground level reflectances. For the conversion it is necessary 1) to determine satellite level radiances, i.e. to perform absolute calibration of radiometers, and 2) to estimate the ground level reflectance of targets, i.e. to perform atmospheric correction of satellite data. A straightforward procedure of pixel-by-pixel inversion of satellite images is possible, however, the inversion time increases rapidly with increasing image size and increasing number of spectral channels. The CR model inversion on large images can be performed more efficiently if some clusterization in the space of spectral signatures is applied. Here the MCR model is inverted on a 256×256 Landsat Thematic Mapper (TM) scene of a test site in Estonia. The spectral images of TM2, TM3, TM4, TM5 and TM7 taken on 8th June 1988 are used. 