资源简介

摘要： 借助植被辐射传输模型，利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而，地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足，不能完全支持LAI等地表参数的估算，易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息，并将其用于代价函数的构建，利用PROSAIL辐射传输模型和遗传算法，分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m，验证对应尺度LAI结果，评价引入先验信息对于提高LAI反演精度的作用。研究表明，引入先验信息有助于提高不同分辨率下LAI反演精度，且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比，以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R2由0.55提高至0.65，RMSE由1.29下降至0.38。在250 m尺度，以500 m LAI反演结果作为先验信息反演的叶面积指数，其精度优于以MODIS LAI产品为先验知识的估算结果，验证精度R2增加了0.08，RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身，没有地面实测数据的参与，在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。 关键词： 叶面积指数;  遥感反演;  先验信息;  MODIS     Abstract: The estimation of leaf area index using remote sensing observation data depend on canopy radiative transfer models is a reliable and robust method. However, the information deficiency contained in the remote sensing data derived from the limitations of the surface heterogeneity, remote sensing observation and self-correlation, which cannot fully support the retrieval of surface parameters (e.g. LAI) and easily bring about the retrieval become ill-posed. The problem can be solved or alleviated effectively by introducing prior knowledge. This paper come up with an approach to extract priori information of Leaf Area Index (LAI) from the remote sensing data, and the information is utilized to construct cost function, PROSAIL radiative transfer model and genetic algorithm are coupled to retrieve LAI at 500 m and 250 m scales. Then the 10 m spatial resolution LAI is upscaled to 500 m and 250 m respectively to verify the corresponding LAI result, and evaluate effects of introduction of prior information on improving LAI accuracy. The comparison of performance between LAI result using MODIS LAI as prior information at 500 m scale with one without prior information indicates thatR2 increased from 0.55 to 0.65 and RMSE decreased from 1.29 to 0.38. The LAI result using 500 m optimal LAI result as prior information at 250 m scale is better than the estimation result with MODIS LAI priori knowledge, verification result shows thatR2 increased by0.08, RMSE decreased by 0.18. It is shown that LAI retrieval accuracy can be enhanced by auxiliary of LAI prior information, besides prior information quality also affects the LAI result to some extent. Multi-resolution LAI retrieval method developed in this paper has potential to estimate spatial and temporal LAI on large scale.

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