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Global sensitivity analysis of the spectral radiance of a soil-vegetation system

机译:土壤-植被系统光谱辐射的整体敏感性分析

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We analyzed the sensitivity of Top-Of-Atmosphere (TOA) radiance and surface reflectance of a soil-vegetation system to input biophysical and biochemical parameters using the coupled Soil-Leaf-Canopy radiative transfer model SLC and MODTRAN. We applied variance-based global sensitivity analysis for different atmospheric conditions and observation configurations. Among 23 input parameters, crown coverage, leaf area index, leaf inclination distribution function and soil moisture were found to be the most influential parameters driving the output variance of the radiance between 400 and 2500 nm with a few exceptions. Hapke's soil parameters and the canopy layer dissociation factor were recognized to have marginal influence on the output radiance. It is also found that a large portion of uncertainty in the output radiance is driven by the interaction effects among input parameters in the visible (~550 nm), whereas the red-near infrared (~670 nm), seems to have fewer interaction effects. The effect of solar/view direction is found to be significant on TOA radiance sensitivity to the input parameters. The results also confirmed that the sensitivities of surface reflectance are comparable to the TOA radiance sensitivities when the atmosphere is clear and visibility is high. Since coupled surface-atmosphere RT models can be computationally intensive, this work also introduces an improvement to the design and sampling of screening methods for efficient sensitivity analysis of computationally expensive models. The improvement is based on three elements: a) generating sample points by Sobol's sequence generator; b) variational analysis in the parameter space using the winding stairs method; c) use of mean and variance sensitivity measures. The results with 1200 model runs demonstrated high correlation (92%) with variance-based global sensitivity analysis using 49,152 model runs, in determining the most influential and non-influential parameters.
机译:我们使用耦合的土壤-叶-盖层辐射传递模型SLC和MODTRAN分析了土壤-植被系统的大气最高(TOA)辐射和表面反射率对输入生物物理和生化参数的敏感性。我们针对不同的大气条件和观测配置应用了基于方差的全局敏感性分析。在23个输入参数中,树冠覆盖率,叶面积指数,叶倾角分布函数和土壤湿度是影响400至2500 nm辐射输出变化的最具影响力的参数,只有少数例外。哈普克(Hapke)的土壤参数和冠层解离因子被认为对输出辐射有很小的影响。还发现,输出辐射的很大一部分不确定性是由可见光(〜550 nm)中输入参数之间的相互作用所驱动的,而近红外红光(〜670 nm)中似乎具有较少的相互作用。 。发现太阳/视线方向的影响对TOA辐射对输入参数的敏感性非常重要。该结果还证实,当气氛晴朗且可见度高时,表面反射率的灵敏度可与TOA辐射灵敏度相媲美。由于耦合的表面-大气RT模型可能需要大量计算,因此这项工作还为筛选方法的设计和采样带来了改进,从而可以对计算昂贵的模型进行有效的灵敏度分析。改进基于以下三个要素:a)由Sobol的序列生成器生成采样点; b)使用绕线阶梯法在参数空间中进行变化分析; c)使用均值和方差敏感性度量。 1200个模型运行的结果表明,与使用49,152个模型运行的基于方差的全局敏感性分析相比,在确定最有影响力和无影响力的参数方面具有高度相关性(92%)。

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