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Comparing accuracy for Leaf Area Index estimation inverting a simple empirical model and a Radiative Transfer model by using multiangular and hyperspectral data

机译:通过使用多角度和高光谱数据将简单的经验模型和辐射传输模型反演的叶面积指数估计的比较精度

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The Leaf Area Index is a key parameter that is indispensable for many biophysical and climatic models. LAI is required for modeling crop water requirements for precision farming and agricultural resource management. The objective of this study was to investigate different approaches for estimating LAI from EO data. To this aim multiangular CHRIS/PROBA data, from SPARC 2003 and 2004, were used in the inversion of PROSPECT-SAILH models using a numerical optimization technique based on Marquardt-Levenberg algorithm. The optimal spectral sampling to estimate LAI was investigated using a sensitivity analysis. From the same data set, the reflectance in the red and near-infrared bands, from the closer to nadir image, was considered in order to estimate the LAI using an empirical approach based on the CLAIR model. The LAI obtained from the empirical approach was finally employed as prior information in the physical based model. LAI values retrieved with the combined approaches were realistically estimated with a good accuracy (RMSE is 0.51 m2m-2).
机译:叶面积指数是许多生物物理和气候模型必不可少的关键参数。需要使用LAI来建模作物水的需求量,以进行精确农业和农业资源管理。这项研究的目的是研究从EO数据估计LAI的不同方法。为此,采用基于Marquardt-Levenberg算法的数值优化技术,将SPARC 2003和2004年的多角CHRIS / PROBA数据用于PROSPECT-SAILH模型的反演。使用灵敏度分析研究了估计LAI的最佳光谱采样。从相同的数据集中,考虑了较接近天底图像的红色和近红外波段的反射率,以便使用基于CLAIR模型的经验方法估算LAI。从经验方法获得的LAI最终被用作基于物理的模型中的先验信息。结合实际方法估算出的LAI值的准确性很高(RMSE为0.51 m2m-2)。

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