Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data
Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data
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DOI：
10.1016/j.rse.2014.01.021
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发表时间：
2014-04
影响因子：
13.5
通讯作者：
Qiang Liu;S. Liang;Zhiqiang Xiao;H. Fang
中科院分区：
文献类型：
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作者：
Qiang Liu;S. Liang;Zhiqiang Xiao;H. Fang
叶面积指数（LAI）是植被冠层在区域和全球地球化学、生态学和气象学应用中最关键的结构参数之一。由于信息内容有限，现有的大多数由单卫星数据得出的全球叶面积指数产品都存在数据缺口和时空不一致性。此外，当前LAI产品的精度可能不满足某些应用的要求。因此，利用多卫星数据反演叶面积指数正成为一种流行的方法。现有的叶面积指数反演方案，使用集合卡尔曼滤波（EnKF）技术进一步扩展，在这项研究中，整合时间，光谱和角度信息，从中分辨率成像光谱仪（MODIS），SPOT/VEGETATION，和多角度成像光谱仪（MISR）数据。用反演的LAI和耦合冠层辐射传输模型和动态过程模型的递归更新LAI气候学，使用EnKF技术可以填补缺失的数据，并产生一致的准确的时间序列LAI产品。在每次迭代过程中，我们定义了一个5 × 1的滑动窗口，并比较所选窗口中的RMSE以确定最小值。在6个站点的验证结果表明，从多个传感器的时间信息，由红色和近红外（NIR）波段提供的光谱信息，和角度信息从MISR双向反射因子（BRF）数据的组合可以提供一个更准确的估计叶面积指数比以前可用。
The leaf area index (LAI) is one of the most critical structural parameters of the vegetation canopy in regional and global biogeochemical, ecological, and meteorological applications. Data gaps and spatial and temporal inconsistencies exist in most of the existing global LAI products derived from single-satellite data because of their limited information content. Furthermore, the accuracy of current LAI products may not meet the requirements of certain applications. Therefore, LAI retrieval from multiple satellite data is becoming popular. An existing LAI inversion scheme using the ensemble Kalman filter (EnKF) technique is further extended in this study to integrate temporal, spectral, and angular information from Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT/VEGETATION, and Multi-angle Imaging Spectroradiometer (MISR) data. The recursive update of LAI climatology with the retrieved LAI and the coupling of a canopy radiative-transfer model and a dynamic process model using the EnKF technique can fill in missing data and produce a consistent accurate time-series LAI product. During each iteration, we defined a 5 ∗ 1 sliding window and compared the RMSEs in the selected window to determine the minimum. Validation results at six sites demonstrate that the combination of temporal information from multiple sensors, spectral information provided by red and near-infrared (NIR) bands, and angular information from MISR bidirectional reflectance factor (BRF) data can provide a more accurate estimate of LAI than previously available.