Inversion of a Radiative Transfer Model for Estimating Forest LAI From Multisource and Multiangular Optical Remote Sensing Data
Inversion of a Radiative Transfer Model for Estimating Forest LAI From Multisource and Multiangular Optical Remote Sensing Data
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DOI：
10.1109/tgrs.2010.2071416
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发表时间：
2011-03
影响因子：
8.2
通讯作者：
Guijun Yang;Chunjiang Zhao;Qiang Liu;Wenjiang Huang;Jihua Wang
中科院分区：
文献类型：
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作者：
Guijun Yang;Chunjiang Zhao;Qiang Liu;Wenjiang Huang;Jihua Wang
本文提出了一种新的森林叶面积指数（LAI）反演方法，该方法利用多波段、多角度的数据，结合辐射传输模型、均值聚类和人工神经网络（ANN）策略。选取Landsat-5专题成像仪（L5 TM）和北京一号小卫星多光谱遥感器（BJ 1）在不同时间获取的4个场景，构建多波段、多角度的影像数据。考虑到森林叶面积指数的垂直分布，从地上和林下，一个混合模型的可逆森林反射率模型（INFORM）被用来支持森林叶面积指数的反演，以消除林下植被的依赖。将INFORM输出的模拟数据加入随机噪声后，首先用均值法进行聚类，然后用人工神经网络进行训练，得到每组（群）的反演模型。然后，将反演模型应用于多角度数据的不同组合，反演森林叶面积指数。最后，中分辨率成像光谱仪LAI产品和现场测量的反演结果进行了验证。实验结果表明，在保证影像数据质量的前提下，通过增加观测角度数据，可以提高森林叶面积指数反演的精度。在考虑向神经网络训练数据中加入随机噪声后，多角度图像数据的LAI反演精度比单角度数据的平均精度提高了30%。
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 -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 -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.