Wang Tongtong, Xiao Zhiqiang, Liu Zhigang
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China.
Sensors (Basel). 2017 Jan 1;17(1):81. doi: 10.3390/s17010081.
Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.
叶面积指数（LAI）是一个重要的生物物理参数，从遥感数据中反演LAI是在区域和全球尺度上生成LAI产品的唯一可行方法。然而，大多数LAI反演方法利用特定时间的卫星观测来反演LAI。由于云层和气溶胶的影响，这些方法生成的LAI产品在空间上不完整且时间上不连续，因此无法满足实际应用的需求。为了生成高质量的LAI产品，本研究提出了四种机器学习算法，包括反向传播神经网络（BPNN）、径向基函数网络（RBFN）、广义回归神经网络（GRNN）和多输出支持向量回归（MSVR），用于从时间序列的中分辨率成像光谱仪（MODIS）反射率数据中反演LAI，并对这些机器学习算法的性能进行了评估。结果表明，GRNN、RBFN和MSVR对训练样本大小的敏感性较低，而BPNN的敏感性较高。这四种算法在使用红、近红外（NIR）和短波红外（SWIR）波段时的表现略优于使用红和NIR波段时，且结果明显优于使用单波段反射率数据（红或NIR）时获得的结果。无论波段组成如何，GRNN的表现都优于其他三种方法。在这四种算法中，BPNN所需的训练时间最少，而对于任何样本大小，MSVR所需的训练时间最多。
