| 参数敏感性分析在遥感及生态水文模型中的研究进展 |
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| 引用本文： | 马瀚青,张琨,马春锋,吴小丹,王琛,郑艺,朱高峰,袁文平,李新.参数敏感性分析在遥感及生态水文模型中的研究进展[J].遥感学报,2022,26(2):286-298. |
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| 作者姓名： | 马瀚青 张琨 马春锋 吴小丹 王琛 郑艺 朱高峰 袁文平 李新 |
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| 作者单位： | 1.中国科学院西北生态环境资源研究院, 兰州 730000;2.中国科学院大学, 北京 100049;3.中国科学院青藏高原研究所, 北京 100101;4.兰州大学 资源环境学院, 兰州 730000;5.中国科学院华南植物园, 广州 510650;6.中山大学 大气科学学院, 广州 510275;7.中国科学院青藏高原地球科学卓越创新中心, 北京 100101 |
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| 基金项目： | 国家自然科学基金（编号：91425303）；中国科学院交叉创新团队（编号：XXH13505-06）；中国科学院青年创新促进会会员人才专项（编号：2021428） |
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| 摘 要： | 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。
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| 关 键 词： | 遥感 参数敏感性分析 参数优化 不确定性分析 生态水文 |
| 收稿时间： | 2019/3/28 0:00:00 |
Research progress on parameter sensitivity analysis in ecological and hydrological models of remote sensing |
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| MA Hanqing,ZHANG Kun,MA Chunfeng,WU Xiaodan,WANG Chen,ZHENG Yi,ZHU Gaofeng,YUAN Wenping,LI Xin.Research progress on parameter sensitivity analysis in ecological and hydrological models of remote sensing[J].Journal of Remote Sensing,2022,26(2):286-298. |
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| Authors: | MA Hanqing ZHANG Kun MA Chunfeng WU Xiaodan WANG Chen ZHENG Yi ZHU Gaofeng YUAN Wenping LI Xin |
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| Institution: | 1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;4.Key Laboratory of Western China''s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou 730000, China;5.South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China;6.School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275, China;7.CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | 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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| Keywords: | remote sensing parameter sensitivity analysis parameter optimization uncertainty analysis eco-hydrological |
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