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An ensemble multiscale filter approach for retrieving leaf area index from multiple satellite data

机译:一种从多颗卫星数据中检索叶面积指数的集成多尺度滤波器方法

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High-quality leaf area index (LAI) products retrieved from satellite observations are urgently needed for crop growth monitoring and yield estimation, land-surface process simulation and global change studies. Currently many LAI products are produced from different satellites. However, those products are usually limited to certain resolutions and some of them are incomplete in space. So these products cannot meet the requirements of practical applications. In this study, an ensemble multiscale filer (EnMsF) coupled with the radiative transfer model, is used to retrieve LAI from multi-sensor reflectance data with different spatial resolution. The result shows the approach can produce optimal estimation of LAI at any desired spatial scale given sparse observations at different scales.
机译:迫切需要从卫星观测中获取高质量的叶面积指数(LAI)产品,以进行作物生长监测和产量估算,地表过程模拟和全球变化研究。当前,许多LAI产品是用不同的卫星生产的。但是,这些产品通常仅限于某些分辨率,并且其中一些在空间上是不完整的。因此这些产品不能满足实际应用的要求。在这项研究中,结合辐射转换模型的集成多尺度文件(EnMsF),用于从具有不同空间分辨率的多传感器反射率数据中检索LAI。结果表明,在不同规模的稀疏观测条件下,该方法可以在任何所需的空间规模上对LAI进行最佳估计。

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