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Received：28 March 2019，
Published：07 February 2022
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参数敏感性分析SA（Sensitivity Analysis）是遥感、生态和水文模型不确定性分析UA（Uncertainty Analysis）的重要方法之一。本文梳理了遥感散射/辐射模型，以及遥感驱动的生态、水文模型研究中常用的敏感性分析方法，并总结了各类方法的优缺点和适用条件。从识别关键参数、不确定性分析和参数优化3个方面，分析了这些领域中参数敏感性分析研究的进展和存在问题，并介绍了最常用的敏感性分析平台。参数敏感性分析作为模型参数优化的先验知识之一，促进了模型和参数的优化。在不确定性和敏感性矩阵USM（Uncertainty and Sensitivity Matrix）的框架下，结合全局敏感性分析方法开展多阶段遥感反演、参数敏感性的尺度效应、参数敏感性的时空异质性研究更加需要关注。此外，还需要提高敏感性分析的计算效率和模式，来适应未来更加复杂的模型和迅速增长的数据量。
Parameter Sensitivity Analysis (SA) is an important research method for Uncertainty Analysis(UA)
key parameters identification and parameters optimization in remote sensing
ecological and hydrological models. In this paper
the sensitivity analysis of ecological and hydrological research based on remote sensing is analyzed. The sensitivity analysis methods commonly used in remote sensing ecological hydrology are reviewed
and the advantages and applicable conditions of each SA method are summarized. Parameter sensitivity analysis methods are generally divided into Local Sensitivity Analysis (LSA) and Global Sensitivity Analysis (GSA)
also can be divided into variance based
statistics based and graphic based methods from mathematical mechanism. Sobol 'and EFAST are the most reliable and stable global sensitivity methods among the current sensitivity algorithms
which are most suitable for most remote sensing inversion and model. There are many methods for parameter sensitivity analysis
so it is very important to select the appropriate method. The initial setting of sensitivity analysis will also affect the results of the analysis. The sensitivity of parameters varies at different scales
The parameter of remote sensing fluorescence model is also one of the key scientific issues. Parametric sensitivity analysis methods have also promoted the development and use of microwave scattering/radiation models. Parameter sensitivity In the process of remote sensing inversion
the order of importance of parameters can be judged according to the sensitivity order
thus providing prior knowledge for multi-stage inversion. In conclusion
sensitivity analysis can effectively improve the simulation accuracy of hydrological
ecological and growth models driven by remote sensing data
and effectively analyze the uncertainties caused by parameters at different scales. Parameter sensitivity analysis can be judged according to the order of sensitivity so as to provide a priori knowledge for multi-stage inversion in the process of remote sensing inversion. The difference of parameter sensitivity analysis in different scales
different bands and different observation angles
as well as the parameter uncertainty
must be paid attention to and analyzed. The four platforms for sensitivity analysis and uncertainty analysis also are introduced in order to make it more convenient for remote sensing scientists to use parameter sensitivity analysis method. Parameter sensitivity analysis as the prior knowledge of the model promotes the development of uncertainty analysis and parameter optimization. In future studies
Under the framework of Uncertainty and Sensitivity Matrix (USM)
it is necessary to pay more attention to the research of multi-stage remote sensing inversion by combining global SA
scale effect of parameter sensitivity index and spatio-temporal heterogeneity of parameter Sensitivity. Meanwhile
the model construction and parameter setting are supported by prior knowledge of parameter sensitivity analysis. Parameter sensitivity analysis should be combined with parameter optimization
data assimilation
spatial analysis and multi-stage inversion to optimize remote sensing inversion and reduce uncertainty. The improvement of computational efficiency and stability of parameter sensitivity analysis is the trend of future research
which requires multi-threaded synchronization
grouping strategy and cloud computing platform.
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