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Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

机译:利用植被指数约束的高分辨率遥感影像同时反演反演生物物理植被参数

摘要

This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρsoil), chlorophyll content (Ca+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and Ca+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R 2 = 0.89 (RMSE = 0.64) and R 2 = 0.56 (RMSE = 0.61), respectively. © 2013 Springer Science+Business Media New York.
机译:这项研究提出了一种新的方法,用于使用辐射遥感图像反演辐射传递模型以检索冠层生物物理参数。在标准反演方面改进了反演程序,并实现了叶面积指数(LAI),土壤反射率(ρsoil),叶绿素含量(Ca + b)和平均叶角(ALA)的同时反演。在这种方法中,LAI用于在反演过程中约束建模条件,从而提供有关所研究的每个样地的物候状态的信息。由于用于反演程序的植被样区面积较小,并且为了避免冗余信息并提高计算效率,因此使用了现有的样地分割方法。除LAI外,所有检索到的生物物理参数均假定在每个图中均不变。基于PROSPECT和SAILH模型的组合,在两个日期对16个谷物田和51个地块进行了测试,以确保在不同物候期进行作物评估,从而对所提出的方法进行了测试。选择图以提供介于0到6之间的宽范围LAI。对LAI,ALA和Ca + b进行了现场测量,并用作验证所提出的模型反演方法的基础。该方法已应用于来自QuickBird 2卫星的极高空间分辨率的遥感数据。与LAI2000地面测量值相比,该反演程序已成功应用于影像,并获得了R 2 = 0.83和RMSE = 0.63的LAI。大麦和小麦的单独倒数分别为R 2 = 0.89(RMSE = 0.64)和R 2 = 0.56(RMSE = 0.61)。 ©2013纽约Springer Science + Business Media。

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