Inversion of a Radiative Transfer Model for Estimating Forest LAI From Multisource and Multiangular Optical Remote Sensing Data

文献类型： 外文期刊

第一作者： Yang, Guijun

作者： Yang, Guijun;Zhao, Chunjiang;Huang, Wenjiang;Wang, Jihua;Yang, Guijun;Liu, Qiang

作者机构：

关键词： Forest leaf area index (LAI); inversion; multisource and multiangle; radiative transfer model; remote sensing

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：5.6； 五年影响因子：6.086 ）

ISSN： 0196-2892

年卷期： 2011 年 49 卷 3 期

页码：

收录情况： SCI

摘要： This paper presents a new forest leaf area index (LAI) inversion method from multisource and multiangle data combined with a radiative transfer model and the strategy of k-means clustering and artificial neural network (ANN). Four scenes of Landsat-5 Thematic Mapper (L5TM) and Beijing-1 small satellite multispectral sensors (BJ1) images, acquired at different times, were selected to construct multisource and multiangle image data in this study. Considering a vertical distribution of forest LAI from both overstory and understory, a hybrid model of the invertible forest reflectance model (INFORM) was used to support the retrieval of forest LAI to eliminate the dependence of understory vegetation. The simulated data from INFORM outputs, added with a random noise, were first clustered by k-means method, and were then trained by ANN to obtain the inversion model for each group (cluster). Next, the inversion model was applied to the different combinations of multiangle data to retrieve the forest LAI. Finally, a validation of inverted results with Moderate Resolution Imaging Spectroradiometer LAI product and field measurements was conducted. The experimental results indicate that the accuracy of the inverted forest LAI can be improved through the addition of observation angle data, if the quality of the image data is ensured. The inversion accuracy of LAI with the multiangle image data is improved by 30% compared to the average accuracy of the inverted LAI with the single angle data after considering the addition of random noise to the ANN training data.

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